[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

CN116430347A - Radar data acquisition and storage method - Google Patents

Radar data acquisition and storage method Download PDF

Info

Publication number
CN116430347A
CN116430347A CN202310694596.9A CN202310694596A CN116430347A CN 116430347 A CN116430347 A CN 116430347A CN 202310694596 A CN202310694596 A CN 202310694596A CN 116430347 A CN116430347 A CN 116430347A
Authority
CN
China
Prior art keywords
signals
training
denoising
target
transmitting
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202310694596.9A
Other languages
Chinese (zh)
Other versions
CN116430347B (en
Inventor
薛爱伦
张欣
周强
彭维刚
周世文
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chengdu Realtime Technology Co ltd
Original Assignee
Chengdu Realtime Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chengdu Realtime Technology Co ltd filed Critical Chengdu Realtime Technology Co ltd
Priority to CN202310694596.9A priority Critical patent/CN116430347B/en
Publication of CN116430347A publication Critical patent/CN116430347A/en
Application granted granted Critical
Publication of CN116430347B publication Critical patent/CN116430347B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/414—Discriminating targets with respect to background clutter
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Computational Mathematics (AREA)
  • Algebra (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Remote Sensing (AREA)
  • Pure & Applied Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention provides a radar data acquisition and storage method, which belongs to the technical field of radar detection targets.

Description

Radar data acquisition and storage method
Technical Field
The invention relates to the technical field of radar detection targets, in particular to a radar data acquisition and storage method.
Background
Radar is used for radio detection and ranging, finding a target by using a radio method, and obtaining information of the target. Radar is an electronic device that detects a target using electromagnetic waves. The radar emits electromagnetic waves to irradiate the target and receives echoes of the target, the received echoes contain target information, and the target information is obtained by analyzing the echo information. But the returned echo also contains noise and interference signals, and the noise and the interference signals influence the extraction of the target information.
Disclosure of Invention
Aiming at the defects in the prior art, the radar data acquisition and storage method provided by the invention solves the problems of noise and interference signals in the reflected signals received by the radar receiving unit.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a radar data acquisition and storage method comprises the following steps:
s1, transmitting signals through a radar transmitting unit;
s2, transmitting signals through target reflection, and receiving the reflected signals through a radar receiving unit;
s3, denoising and interference removal processing is carried out on the reflected signals, and effective receiving signals are obtained;
s4, extracting target characteristics according to the effective received signals and the emission signals;
s5, carrying out encryption distributed storage on the target characteristics.
Further, the expression of the transmission signal in S1 is:
Figure SMS_1
wherein ,
Figure SMS_3
for transmitting signals +.>
Figure SMS_8
For the amplitude of the transmitted signal, +.>
Figure SMS_10
For the base frequency +.>
Figure SMS_2
For the number of array elements, < > for>
Figure SMS_6
For frequency deviation>
Figure SMS_7
For the phase of the transmitted signal>
Figure SMS_9
For time (I)>
Figure SMS_4
For the number of array elements, < > for>
Figure SMS_5
As a cosine function.
Further, the expression of the reflected signal received in S2 is:
Figure SMS_11
wherein ,
Figure SMS_12
for the received reflected signal +.>
Figure SMS_13
For transmitting signals +.>
Figure SMS_14
For the target matrix +.>
Figure SMS_15
For interfering signals +.>
Figure SMS_16
Is a noise signal.
Further, the step S3 includes the following sub-steps:
s31, establishing a denoising and interference elimination model;
s32, transmitting signals to a known target through a radar transmitting unit to obtain theoretical received signals and actual received signals;
s33, constructing a theoretical received signal and an actual received signal into a training set;
s34, training a denoising and interference elimination model by adopting a training set, and calculating an error during training;
s35, judging whether the error is lower than an error threshold, if so, finishing the training of the denoising and interference elimination model, otherwise, adjusting the weight and the bias of the denoising and interference elimination model according to the error, and jumping to S34;
s36, inputting the reflected signals into a trained denoising and interference elimination model to obtain effective received signals.
The beneficial effects of the above further scheme are: the invention constructs the denoising and interference removal process as a model, and achieves the aim of denoising and interference removal at the same time. The invention transmits signals to the known target through the radar transmitting unit, the target characteristics of the known target are known, so that the theoretical receiving signals can be directly obtained, the actual receiving signals and the theoretical receiving signals can be constructed as training samples, the model relation between the actual receiving signals and the theoretical receiving signals is established, and the interference signals are removed
Figure SMS_17
And noise signal->
Figure SMS_18
Without exploring the interference signal +.>
Figure SMS_19
And noise signal->
Figure SMS_20
And particularly, the influence on the actual received signal is realized, and the rapid denoising and interference removal are realized.
Further, the denoising and interference elimination model in S31 is as follows:
Figure SMS_21
Figure SMS_22
wherein ,
Figure SMS_24
for the actual output of the denoising and interference removal model, +.>
Figure SMS_27
For actually receiving the signal, +.>
Figure SMS_29
As a function of the hyperbolic tangent,
Figure SMS_25
as a logarithmic function>
Figure SMS_26
Is natural constant (18)>
Figure SMS_31
For the first weight, ++>
Figure SMS_32
For the first bias->
Figure SMS_23
For the second weight, ++>
Figure SMS_28
For the second bias->
Figure SMS_30
Is a cache parameter.
The beneficial effects of the above further scheme are: the denoising and interference elimination model of the invention comprises two layers of weights and offsets, and when the weights and offsets of the first layer are used, the invention adopts a logarithmic function to strengthen the characteristics of the input actual received signals, thereby facilitating the hyperbolic tangent function
Figure SMS_33
Normalized with the actual received signal>
Figure SMS_34
Multiplying to establish the actual received signal +.>
Figure SMS_35
To the cache parameter->
Figure SMS_36
The invention adopts the second weight and bias to establish the buffer parameter +.>
Figure SMS_37
Relation to actual output->
Figure SMS_38
The relation between the input and the output is expressed through the weight and the bias of the two layers, so that the model has better denoising and interference removing effects.
Further, the calculation formula of the error in S34 is:
Figure SMS_39
wherein ,
Figure SMS_42
is->
Figure SMS_44
Error during training->
Figure SMS_46
The>
Figure SMS_41
Actual output during secondary training, +.>
Figure SMS_43
Is->
Figure SMS_48
Theoretical received signal during secondary training, +.>
Figure SMS_49
For the number of training times, the user is strapped>
Figure SMS_40
As a fraction coefficient +.>
Figure SMS_45
For the number of training times, ∈>
Figure SMS_47
Is the number of training times.
The beneficial effects of the above further scheme are: the invention adopts the actual output when training for a plurality of times
Figure SMS_50
And theoretical received signal->
Figure SMS_51
Difference, and actual output ∈>
Figure SMS_52
And theoretical received signal->
Figure SMS_53
Is integrated with the error condition by actually outputting +.>
Figure SMS_54
And theoretical received signal->
Figure SMS_55
The difference value only represents the difference between the two values, and the difference value cannot represent the similarity degree. According to the invention, the difference of multiple training is considered to obtain the error, so that the model has smaller error on the whole during training, and high-precision denoising and interference elimination are realized.
Further, the formula for adjusting the weight of the denoising and interference elimination model in S35 is as follows:
Figure SMS_56
the formula for adjusting the bias of the denoising and interference elimination model is as follows:
Figure SMS_57
wherein ,
Figure SMS_59
is->
Figure SMS_63
Weight during secondary training, +.>
Figure SMS_66
Is->
Figure SMS_60
Weight during secondary training, +.>
Figure SMS_64
Is->
Figure SMS_68
Error during training->
Figure SMS_70
Is->
Figure SMS_58
Bias during secondary training->
Figure SMS_62
Is->
Figure SMS_67
Bias during secondary training->
Figure SMS_69
For partial derivative operation, < ->
Figure SMS_61
For maximum training times, +.>
Figure SMS_65
As a cosine function.
The beneficial effects of the above further scheme are: the weight and bias descending degree of the invention depends on the error and the training times, and the error is larger and the training times are smaller in the initial training period, so the weight and bias are iterated rapidly, the error is smaller, the training times are more, the weight and bias are slowly descended in the later training period, the model output is approximate to the ideal value, and the self-adaptive adjustment of the output is realized.
Further, the step S4 includes the following sub-steps:
s41, establishing a receiving and transmitting relation according to the effective receiving signals and the transmitting signals to obtain a target matrix;
s42, decomposing the characteristic value of the target matrix to obtain the characteristic value;
s43, constructing the characteristic value as a vector to obtain the target characteristic.
The beneficial effects of the above further scheme are: after the denoising and interference elimination model, no interference signal exists in the receiving and transmitting relation
Figure SMS_71
And noise signal->
Figure SMS_72
Solving a target matrix, and then carrying out eigenvalue decomposition on the target matrix to obtain eigenvalues, wherein the eigenvalues represent target information.
Further, the receiving and transmitting relation in S41 is:
Figure SMS_73
wherein ,
Figure SMS_74
for effective reception of signals, < >>
Figure SMS_75
For transmitting signals +.>
Figure SMS_76
Is a target matrix.
Further, the encryption formula in S5 is:
Figure SMS_77
wherein ,
Figure SMS_78
for the encrypted target feature +.>
Figure SMS_82
For the object feature->
Figure SMS_85
The number of characteristic values in>
Figure SMS_80
As a logarithmic function>
Figure SMS_83
Is natural constant (18)>
Figure SMS_86
For the object feature->
Figure SMS_87
Storage location (s)/(s)>
Figure SMS_79
For encryption coefficients>
Figure SMS_81
For encryption weight, ++>
Figure SMS_84
Is a rounding operation.
The beneficial effects of the above further scheme are: after the target characteristics are obtained, the invention carries out encryption storage, and the target characteristics are encrypted
Figure SMS_88
Storage location->
Figure SMS_89
And target feature->
Figure SMS_90
Number of characteristic values ∈>
Figure SMS_91
Is integrated into the encryption formula, and simultaneously, encryption weight is set>
Figure SMS_92
And encryption coefficient->
Figure SMS_93
As the adjustment quantity, the encryption randomness is convenient to increase, and the data can be stored better.
The technical scheme of the embodiment of the invention has at least the following advantages and beneficial effects: according to the method, the radar transmitting unit transmits signals, the unknown target reflects the transmitted signals, the radar receiving unit receives the reflected signals, the received reflected signals are subjected to denoising and interference elimination processing, effective received signals are extracted, target characteristics are extracted according to the relation between the effective received signals and the transmitted signals, and the target characteristics are stored in an encryption distributed mode, so that the target information is not lost.
Drawings
Fig. 1 is a flow chart of a radar data acquisition and storage method.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
As shown in fig. 1, a radar data acquisition and storage method includes the following steps:
s1, transmitting signals through a radar transmitting unit;
the expression of the emission signal in the S1 is as follows:
Figure SMS_94
wherein ,
Figure SMS_97
for transmitting signals +.>
Figure SMS_99
For the amplitude of the transmitted signal, +.>
Figure SMS_101
For the base frequency +.>
Figure SMS_95
For the number of array elements, < > for>
Figure SMS_100
For frequency deviation>
Figure SMS_102
For the phase of the transmitted signal>
Figure SMS_103
For time (I)>
Figure SMS_96
For the number of array elements, < > for>
Figure SMS_98
As a cosine function.
S2, transmitting signals through target reflection, and receiving the reflected signals through a radar receiving unit;
the expression of the reflected signal received in S2 is:
Figure SMS_104
wherein ,
Figure SMS_105
for the received reflected signal +.>
Figure SMS_106
For transmitting signals +.>
Figure SMS_107
For the target matrix +.>
Figure SMS_108
For interfering signals +.>
Figure SMS_109
Is a noise signal.
S3, denoising and interference removal processing is carried out on the reflected signals, and effective receiving signals are obtained;
the step S3 comprises the following substeps:
s31, establishing a denoising and interference removal model:
Figure SMS_110
Figure SMS_111
wherein ,
Figure SMS_113
for the actual output of the denoising and interference removal model, +.>
Figure SMS_116
For actually receiving the signal, +.>
Figure SMS_118
As a function of the hyperbolic tangent,
Figure SMS_112
as a logarithmic function>
Figure SMS_115
Is natural constant (18)>
Figure SMS_119
For the first weight, ++>
Figure SMS_121
For the first bias->
Figure SMS_114
For the second weight, ++>
Figure SMS_117
For the second bias->
Figure SMS_120
Is a cache parameter.
The denoising and interference elimination model of the invention comprises two layers of weights and offsets, and when the weights and offsets of the first layer are used, the invention adopts a logarithmic function to strengthen the characteristics of the input actual received signals, thereby facilitating the hyperbolic tangent function
Figure SMS_122
Normalized with the actual received signal>
Figure SMS_123
Multiplying to establish the actual received signal +.>
Figure SMS_124
To the cache parameter->
Figure SMS_125
The invention adopts the second weight and bias to establish the buffer parameter +.>
Figure SMS_126
Relation to actual output->
Figure SMS_127
The relation between the input and the output is expressed through the weight and the bias of the two layers, so that the model has better denoising and interference removing effects.
S32, transmitting signals to a known target through a radar transmitting unit to obtain theoretical received signals and actual received signals;
s33, constructing a theoretical received signal and an actual received signal into a training set;
s34, training a denoising and interference elimination model by adopting a training set, and calculating an error during training;
the actual received signal is used as the input of the denoising and interference elimination model during training, and the theoretical received signal is used as the standard for adjusting the weight and the bias.
The calculation formula of the error in S34 is:
Figure SMS_128
wherein ,
Figure SMS_129
is->
Figure SMS_132
Error during training->
Figure SMS_137
The>
Figure SMS_131
Actual output during secondary training, +.>
Figure SMS_134
Is->
Figure SMS_136
Theoretical received signal during secondary training, +.>
Figure SMS_138
For the number of training times, the user is strapped>
Figure SMS_130
As a fraction coefficient +.>
Figure SMS_133
For the number of training times, ∈>
Figure SMS_135
Is the number of training times.
The invention adopts the actual output when training for a plurality of times
Figure SMS_139
And theoretical received signal->
Figure SMS_140
Difference, and actual output ∈>
Figure SMS_141
And theoretical received signal->
Figure SMS_142
Is integrated with the error condition by actually outputting +.>
Figure SMS_143
And theoretical received signal->
Figure SMS_144
The difference value only represents the difference between the two values, and the difference value cannot represent the similarity degree. According to the invention, the difference of multiple training is considered to obtain the error, so that the model has smaller error on the whole during training, and high-precision denoising and interference elimination are realized.
S35, judging whether the error is lower than an error threshold, if so, finishing the training of the denoising and interference elimination model, otherwise, adjusting the weight and the bias of the denoising and interference elimination model according to the error, and jumping to S34;
the formula for adjusting the weight of the denoising and interference elimination model in the step S35 is as follows:
Figure SMS_145
the formula for adjusting the bias of the denoising and interference elimination model is as follows:
Figure SMS_146
wherein ,
Figure SMS_150
is->
Figure SMS_154
Weight during secondary training, +.>
Figure SMS_155
Is->
Figure SMS_149
Weight during secondary training, +.>
Figure SMS_151
Is->
Figure SMS_157
Error during training->
Figure SMS_158
Is->
Figure SMS_147
Bias during secondary training->
Figure SMS_152
Is->
Figure SMS_156
Bias during secondary training->
Figure SMS_159
For partial derivative operation, < ->
Figure SMS_148
For maximum training times, +.>
Figure SMS_153
As a cosine function.
The weight and bias descending degree of the invention depends on the error and the training times, and the error is larger and the training times are smaller in the initial training period, so the weight and bias are iterated rapidly, the error is smaller, the training times are more, the weight and bias are slowly descended in the later training period, the model output is approximate to the ideal value, and the self-adaptive adjustment of the output is realized.
S36, inputting the reflected signals into a trained denoising and interference elimination model to obtain effective received signals.
The invention removes noise and interferenceThe process is constructed as a model, and the purposes of denoising and interference removal are achieved simultaneously. The invention transmits signals to the known target through the radar transmitting unit, the target characteristics of the known target are known, so that the theoretical receiving signals can be directly obtained, the actual receiving signals and the theoretical receiving signals can be constructed as training samples, the model relation between the actual receiving signals and the theoretical receiving signals is established, and the interference signals are removed
Figure SMS_160
And noise signal->
Figure SMS_161
Without exploring the interference signal +.>
Figure SMS_162
And noise signal->
Figure SMS_163
And particularly, the influence on the actual received signal is realized, and the rapid denoising and interference removal are realized.
S4, extracting target characteristics according to the effective received signals and the emission signals;
the step S4 comprises the following substeps:
s41, establishing a receiving and transmitting relation according to the effective receiving signals and the transmitting signals to obtain a target matrix;
the receiving and transmitting relation in S41 is:
Figure SMS_164
wherein ,
Figure SMS_165
for effective reception of signals, < >>
Figure SMS_166
For transmitting signals +.>
Figure SMS_167
Is a target matrix.
S42, decomposing the characteristic value of the target matrix to obtain the characteristic value;
s43, constructing the characteristic value as a vector to obtain the target characteristic.
After the invention passes through the denoising and interference elimination model, no interference signal exists in the receiving and transmitting relation
Figure SMS_168
And noise signal
Figure SMS_169
Solving a target matrix, and then carrying out eigenvalue decomposition on the target matrix to obtain eigenvalues, wherein the eigenvalues represent target information.
S5, carrying out encryption distributed storage on the target characteristics.
The encryption formula in S5 is as follows:
Figure SMS_170
wherein ,
Figure SMS_171
for the encrypted target feature +.>
Figure SMS_176
For the object feature->
Figure SMS_179
The number of characteristic values in>
Figure SMS_173
As a logarithmic function>
Figure SMS_175
Is natural constant (18)>
Figure SMS_178
For the object feature->
Figure SMS_180
Storage location (s)/(s)>
Figure SMS_172
For encryption coefficients>
Figure SMS_174
For encryption weight, ++>
Figure SMS_177
Is a rounding operation.
After the target characteristics are obtained, the invention carries out encryption storage, and the target characteristics are encrypted
Figure SMS_181
Storage location->
Figure SMS_182
And target feature->
Figure SMS_183
Number of characteristic values ∈>
Figure SMS_184
Is integrated into the encryption formula, and simultaneously, encryption weight is set>
Figure SMS_185
And encryption coefficient->
Figure SMS_186
As the adjustment quantity, the encryption randomness is convenient to increase, and the data can be stored better.
The distributed storage model in the invention is as follows:
Figure SMS_187
wherein ,
Figure SMS_188
in the +.>
Figure SMS_192
Storing target features on a table device>
Figure SMS_195
Is (are) located>
Figure SMS_189
In order to activate the function in the shape of an S,
Figure SMS_193
for hyperbolic tangent activation function,/->
Figure SMS_194
Is natural constant (18)>
Figure SMS_196
For rounding operations, ++>
Figure SMS_190
For the remainder operation, ++>
Figure SMS_191
Is the number of the storage device.
In the distributed storage, the storage position is determined by the position calculated by the distributed storage model, and the storage positions of different storage devices are different, so that the random storage method also has the randomness in the storage position, and the data is safer. And rounding operation is adopted in the encryption or storage process, so that the target data can be restored conveniently.
The technical scheme of the embodiment of the invention has at least the following advantages and beneficial effects: according to the method, the radar transmitting unit transmits signals, the unknown target reflects the transmitted signals, the radar receiving unit receives the reflected signals, the received reflected signals are subjected to denoising and interference elimination processing, effective received signals are extracted, target characteristics are extracted according to the relation between the effective received signals and the transmitted signals, and the target characteristics are stored in an encryption distributed mode, so that the target information is not lost.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The radar data acquisition and storage method is characterized by comprising the following steps:
s1, transmitting signals through a radar transmitting unit;
s2, transmitting signals through target reflection, and receiving the reflected signals through a radar receiving unit;
s3, denoising and interference removal processing is carried out on the reflected signals, and effective receiving signals are obtained;
s4, extracting target characteristics according to the effective received signals and the emission signals;
s5, carrying out encryption distributed storage on the target characteristics.
2. The method for collecting and storing radar data according to claim 1, wherein the expression of the transmitted signal in S1 is:
Figure QLYQS_1
wherein ,
Figure QLYQS_3
for transmitting signals +.>
Figure QLYQS_6
For the amplitude of the transmitted signal, +.>
Figure QLYQS_9
For the base frequency +.>
Figure QLYQS_4
For the number of array elements, < > for>
Figure QLYQS_7
For the frequency offset to be a frequency offset,
Figure QLYQS_8
for transmitting signalsThe phase of the number>
Figure QLYQS_10
For time (I)>
Figure QLYQS_2
For the number of array elements, < > for>
Figure QLYQS_5
As a cosine function.
3. The method for collecting and storing radar data according to claim 1, wherein the expression of the reflected signal received in S2 is:
Figure QLYQS_11
wherein ,
Figure QLYQS_12
for the received reflected signal +.>
Figure QLYQS_13
For transmitting signals +.>
Figure QLYQS_14
For the target matrix +.>
Figure QLYQS_15
For interfering signals +.>
Figure QLYQS_16
Is a noise signal.
4. The method for collecting and storing radar data according to claim 1, wherein S3 comprises the following sub-steps:
s31, establishing a denoising and interference elimination model;
s32, transmitting signals to a known target through a radar transmitting unit to obtain theoretical received signals and actual received signals;
s33, constructing a theoretical received signal and an actual received signal into a training set;
s34, training a denoising and interference elimination model by adopting a training set, and calculating an error during training;
s35, judging whether the error is lower than an error threshold, if so, finishing the training of the denoising and interference elimination model, otherwise, adjusting the weight and the bias of the denoising and interference elimination model according to the error, and jumping to S34;
s36, inputting the reflected signals into a trained denoising and interference elimination model to obtain effective received signals.
5. The method for collecting and storing radar data according to claim 4, wherein the denoising and interference removal model in S31 is:
Figure QLYQS_17
Figure QLYQS_18
wherein ,
Figure QLYQS_20
for the actual output of the denoising and interference removal model, +.>
Figure QLYQS_23
For actually receiving the signal, +.>
Figure QLYQS_28
As hyperbolic tangent function, +.>
Figure QLYQS_19
As a logarithmic function>
Figure QLYQS_22
Is natural and normalCount (n)/(l)>
Figure QLYQS_25
For the first weight, ++>
Figure QLYQS_27
For the first bias->
Figure QLYQS_21
For the second weight, ++>
Figure QLYQS_24
For the second bias->
Figure QLYQS_26
Is a cache parameter.
6. The method for collecting and storing radar data according to claim 4, wherein the calculation formula of the error in S34 is:
Figure QLYQS_29
wherein ,
Figure QLYQS_31
is->
Figure QLYQS_33
Error during training->
Figure QLYQS_36
The>
Figure QLYQS_32
Actual output during secondary training, +.>
Figure QLYQS_35
Is->
Figure QLYQS_38
Theoretical received signal during secondary training, +.>
Figure QLYQS_39
For the number of training times, the user is strapped>
Figure QLYQS_30
As a fraction coefficient +.>
Figure QLYQS_34
For the number of training times, ∈>
Figure QLYQS_37
Is the number of training times.
7. The method for collecting and storing radar data according to claim 4, wherein the formula for adjusting the weight of the denoising and interference elimination model in S35 is:
Figure QLYQS_40
the formula for adjusting the bias of the denoising and interference elimination model is as follows:
Figure QLYQS_41
wherein ,
Figure QLYQS_45
is->
Figure QLYQS_49
Weight during secondary training, +.>
Figure QLYQS_52
Is->
Figure QLYQS_44
Weight during secondary training, +.>
Figure QLYQS_48
Is->
Figure QLYQS_50
Error during training->
Figure QLYQS_53
Is->
Figure QLYQS_42
Bias during secondary training->
Figure QLYQS_46
Is->
Figure QLYQS_51
Bias during secondary training->
Figure QLYQS_54
For partial derivative operation, < ->
Figure QLYQS_43
For maximum training times, +.>
Figure QLYQS_47
As a cosine function.
8. The method for collecting and storing radar data according to claim 1, wherein S4 comprises the following sub-steps:
s41, establishing a receiving and transmitting relation according to the effective receiving signals and the transmitting signals to obtain a target matrix;
s42, decomposing the characteristic value of the target matrix to obtain the characteristic value;
s43, constructing the characteristic value as a vector to obtain the target characteristic.
9. The method for collecting and storing radar data according to claim 8, wherein the receiving and transmitting relation in S41 is:
Figure QLYQS_55
wherein ,
Figure QLYQS_56
for effective reception of signals, < >>
Figure QLYQS_57
For transmitting signals +.>
Figure QLYQS_58
Is a target matrix.
10. The method for collecting and storing radar data according to claim 1, wherein the encryption formula in S5 is:
Figure QLYQS_59
wherein ,
Figure QLYQS_61
for the encrypted target feature +.>
Figure QLYQS_63
For the object feature->
Figure QLYQS_67
The number of characteristic values in>
Figure QLYQS_62
As a logarithmic function>
Figure QLYQS_65
Is natural constant (18)>
Figure QLYQS_68
For the object feature->
Figure QLYQS_69
Storage location (s)/(s)>
Figure QLYQS_60
For encryption coefficients>
Figure QLYQS_64
For encryption weight, ++>
Figure QLYQS_66
Is a rounding operation.
CN202310694596.9A 2023-06-13 2023-06-13 Radar data acquisition and storage method Active CN116430347B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310694596.9A CN116430347B (en) 2023-06-13 2023-06-13 Radar data acquisition and storage method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310694596.9A CN116430347B (en) 2023-06-13 2023-06-13 Radar data acquisition and storage method

Publications (2)

Publication Number Publication Date
CN116430347A true CN116430347A (en) 2023-07-14
CN116430347B CN116430347B (en) 2023-08-22

Family

ID=87091145

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310694596.9A Active CN116430347B (en) 2023-06-13 2023-06-13 Radar data acquisition and storage method

Country Status (1)

Country Link
CN (1) CN116430347B (en)

Citations (42)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH06109834A (en) * 1992-09-30 1994-04-22 Mitsubishi Electric Corp Tracking radar apparatus
AU7981194A (en) * 1994-10-17 1996-05-06 Russell D. Brown System and method for earth probing with deep subsurface penetration using low frequency electromagnetic signals
US20100317420A1 (en) * 2003-02-05 2010-12-16 Hoffberg Steven M System and method
CN102608588A (en) * 2012-03-14 2012-07-25 西安电子科技大学 Broadband sub-matrix adaptive beamforming method based on sub-band decomposition
CN103048654A (en) * 2011-10-14 2013-04-17 三星泰科威株式会社 Radar sensor and method for detecting objects using the same
CN103220016A (en) * 2013-04-19 2013-07-24 山东大学 Generation system and method of pulse ultra wideband system orthogonal sparse dictionary
CN103941238A (en) * 2014-05-08 2014-07-23 西安电子科技大学 Networked radar collaborative anti-interference transmitting power distribution method
CN104732493A (en) * 2015-03-18 2015-06-24 西安电子科技大学 SAR image de-noising algorithm based on Primal Sketch classification and SVD domain improvement MMSE estimation
WO2015172622A1 (en) * 2014-05-14 2015-11-19 武汉大学 Method for radio-frequency interference suppression of high-frequency ground wave radar
CN105572664A (en) * 2015-12-31 2016-05-11 上海广电通信技术有限公司 Networking navigation radar target tracking system based on data fusion
WO2016165281A1 (en) * 2015-04-16 2016-10-20 深圳大学 Radar communication method and system
CN106353754A (en) * 2016-08-30 2017-01-25 中国水利水电科学研究院 Integrated detection radar system for ice and water conditions
CN107037410A (en) * 2017-04-17 2017-08-11 电子科技大学 A kind of method disturbed radar, device and frequency control battle array jammer
CN107356921A (en) * 2017-08-11 2017-11-17 桂林电子科技大学 A kind of method that frequency diversity array radar is positioned based on a frequency deviation target
CN108226892A (en) * 2018-03-27 2018-06-29 天津大学 A kind of radar signal restoration methods under complicated noise based on deep learning
CN108287333A (en) * 2018-03-19 2018-07-17 电子科技大学 A kind of main lobe anti-interference method of joint JADE and CLEAN
CN108492258A (en) * 2018-01-17 2018-09-04 天津大学 A kind of radar image denoising method based on generation confrontation network
CN109031287A (en) * 2018-09-21 2018-12-18 西安交通大学 ULTRA-WIDEBAND RADAR human body respiration signal detecting method through walls based on Faster-RCNN network
CN109472747A (en) * 2018-10-18 2019-03-15 北京大学 A kind of deep learning method of microwave remote sensing image speckle noise reduction
CN109919862A (en) * 2019-02-01 2019-06-21 北京佳格天地科技有限公司 Radar image denoising system, method and computer equipment
CN110263646A (en) * 2019-05-21 2019-09-20 华中科技大学 A kind of sea weak target detection method and system based on convolutional neural networks
CN210294523U (en) * 2019-07-16 2020-04-10 成都实时技术股份有限公司 Data recording device for radar detection system
CN111145102A (en) * 2019-11-22 2020-05-12 南京理工大学 Synthetic aperture radar image denoising method based on convolutional neural network
CN111163419A (en) * 2020-02-07 2020-05-15 北京大学 Malicious user detection method based on state mean value in vehicle cooperation dynamic tracking
KR20200054657A (en) * 2018-11-12 2020-05-20 (주) 에코투모로우코리아 Automotive pulse radar device
CN111610518A (en) * 2020-06-09 2020-09-01 电子科技大学 Secondary radar signal denoising method based on depth residual separation convolutional network
WO2020214435A2 (en) * 2019-04-03 2020-10-22 Xonar Technology Inc. Noise reduction in an ultra-wideband (uwb) radar
CN112801218A (en) * 2021-03-22 2021-05-14 中国人民解放军国防科技大学 Multi-view one-dimensional range profile fusion identification method based on noise reduction feature enhancement
CN113157821A (en) * 2021-04-09 2021-07-23 电子科技大学 Inquirable encryption method suitable for relational database
CN113376600A (en) * 2021-05-10 2021-09-10 西安电子科技大学 Pedestrian radar echo denoising method based on RSDNet
CN113673317A (en) * 2021-07-12 2021-11-19 电子科技大学 Atomic norm minimization dimension reduction-based two-dimensional lattice DOA estimation method
CN113962260A (en) * 2021-10-21 2022-01-21 中国人民解放军空军航空大学 Radar signal intelligent sorting method based on denoising depth residual error network
CN114424930A (en) * 2022-01-07 2022-05-03 煤炭科学研究总院有限公司 Ultra-wideband UWB (ultra-wideband) vital signal data processing method and device based on singular value decomposition
CN114444540A (en) * 2021-12-29 2022-05-06 万环 Robust beam former for resisting mismatching of wave arrival angle directions based on MIMO radar
CN114509731A (en) * 2022-01-24 2022-05-17 电子科技大学 Radar main lobe anti-interference method based on double-stage deep network
US20220179040A1 (en) * 2020-12-09 2022-06-09 Richwave Technology Corp. Radar detector and interference suppression method using radar detector
CN114779185A (en) * 2022-04-29 2022-07-22 西安电子科技大学 Radar signal anti-interference low-loss recovery method of coding and decoding convolutional neural network
CN114781457A (en) * 2022-04-29 2022-07-22 西安电子科技大学 Time-frequency domain interference suppression method based on automatic encoder
CN115015869A (en) * 2022-06-27 2022-09-06 清华大学 Learnable low frequency broadband radar target parameter estimation method, apparatus and program product
CN115097398A (en) * 2022-07-01 2022-09-23 西安电子科技大学 Radar anti-interference signal recovery method based on cross-domain signal low-loss recovery network
CN115842566A (en) * 2022-11-28 2023-03-24 哈尔滨工程大学 CNN-Bi-LSTM-based interference machine self-interference digital cancellation method
CN115951322A (en) * 2022-12-30 2023-04-11 广州极飞科技股份有限公司 Radar signal processing method and device for unmanned aerial vehicle, electronic equipment and storage medium

Patent Citations (42)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH06109834A (en) * 1992-09-30 1994-04-22 Mitsubishi Electric Corp Tracking radar apparatus
AU7981194A (en) * 1994-10-17 1996-05-06 Russell D. Brown System and method for earth probing with deep subsurface penetration using low frequency electromagnetic signals
US20100317420A1 (en) * 2003-02-05 2010-12-16 Hoffberg Steven M System and method
CN103048654A (en) * 2011-10-14 2013-04-17 三星泰科威株式会社 Radar sensor and method for detecting objects using the same
CN102608588A (en) * 2012-03-14 2012-07-25 西安电子科技大学 Broadband sub-matrix adaptive beamforming method based on sub-band decomposition
CN103220016A (en) * 2013-04-19 2013-07-24 山东大学 Generation system and method of pulse ultra wideband system orthogonal sparse dictionary
CN103941238A (en) * 2014-05-08 2014-07-23 西安电子科技大学 Networked radar collaborative anti-interference transmitting power distribution method
WO2015172622A1 (en) * 2014-05-14 2015-11-19 武汉大学 Method for radio-frequency interference suppression of high-frequency ground wave radar
CN104732493A (en) * 2015-03-18 2015-06-24 西安电子科技大学 SAR image de-noising algorithm based on Primal Sketch classification and SVD domain improvement MMSE estimation
WO2016165281A1 (en) * 2015-04-16 2016-10-20 深圳大学 Radar communication method and system
CN105572664A (en) * 2015-12-31 2016-05-11 上海广电通信技术有限公司 Networking navigation radar target tracking system based on data fusion
CN106353754A (en) * 2016-08-30 2017-01-25 中国水利水电科学研究院 Integrated detection radar system for ice and water conditions
CN107037410A (en) * 2017-04-17 2017-08-11 电子科技大学 A kind of method disturbed radar, device and frequency control battle array jammer
CN107356921A (en) * 2017-08-11 2017-11-17 桂林电子科技大学 A kind of method that frequency diversity array radar is positioned based on a frequency deviation target
CN108492258A (en) * 2018-01-17 2018-09-04 天津大学 A kind of radar image denoising method based on generation confrontation network
CN108287333A (en) * 2018-03-19 2018-07-17 电子科技大学 A kind of main lobe anti-interference method of joint JADE and CLEAN
CN108226892A (en) * 2018-03-27 2018-06-29 天津大学 A kind of radar signal restoration methods under complicated noise based on deep learning
CN109031287A (en) * 2018-09-21 2018-12-18 西安交通大学 ULTRA-WIDEBAND RADAR human body respiration signal detecting method through walls based on Faster-RCNN network
CN109472747A (en) * 2018-10-18 2019-03-15 北京大学 A kind of deep learning method of microwave remote sensing image speckle noise reduction
KR20200054657A (en) * 2018-11-12 2020-05-20 (주) 에코투모로우코리아 Automotive pulse radar device
CN109919862A (en) * 2019-02-01 2019-06-21 北京佳格天地科技有限公司 Radar image denoising system, method and computer equipment
WO2020214435A2 (en) * 2019-04-03 2020-10-22 Xonar Technology Inc. Noise reduction in an ultra-wideband (uwb) radar
CN110263646A (en) * 2019-05-21 2019-09-20 华中科技大学 A kind of sea weak target detection method and system based on convolutional neural networks
CN210294523U (en) * 2019-07-16 2020-04-10 成都实时技术股份有限公司 Data recording device for radar detection system
CN111145102A (en) * 2019-11-22 2020-05-12 南京理工大学 Synthetic aperture radar image denoising method based on convolutional neural network
CN111163419A (en) * 2020-02-07 2020-05-15 北京大学 Malicious user detection method based on state mean value in vehicle cooperation dynamic tracking
CN111610518A (en) * 2020-06-09 2020-09-01 电子科技大学 Secondary radar signal denoising method based on depth residual separation convolutional network
US20220179040A1 (en) * 2020-12-09 2022-06-09 Richwave Technology Corp. Radar detector and interference suppression method using radar detector
CN112801218A (en) * 2021-03-22 2021-05-14 中国人民解放军国防科技大学 Multi-view one-dimensional range profile fusion identification method based on noise reduction feature enhancement
CN113157821A (en) * 2021-04-09 2021-07-23 电子科技大学 Inquirable encryption method suitable for relational database
CN113376600A (en) * 2021-05-10 2021-09-10 西安电子科技大学 Pedestrian radar echo denoising method based on RSDNet
CN113673317A (en) * 2021-07-12 2021-11-19 电子科技大学 Atomic norm minimization dimension reduction-based two-dimensional lattice DOA estimation method
CN113962260A (en) * 2021-10-21 2022-01-21 中国人民解放军空军航空大学 Radar signal intelligent sorting method based on denoising depth residual error network
CN114444540A (en) * 2021-12-29 2022-05-06 万环 Robust beam former for resisting mismatching of wave arrival angle directions based on MIMO radar
CN114424930A (en) * 2022-01-07 2022-05-03 煤炭科学研究总院有限公司 Ultra-wideband UWB (ultra-wideband) vital signal data processing method and device based on singular value decomposition
CN114509731A (en) * 2022-01-24 2022-05-17 电子科技大学 Radar main lobe anti-interference method based on double-stage deep network
CN114779185A (en) * 2022-04-29 2022-07-22 西安电子科技大学 Radar signal anti-interference low-loss recovery method of coding and decoding convolutional neural network
CN114781457A (en) * 2022-04-29 2022-07-22 西安电子科技大学 Time-frequency domain interference suppression method based on automatic encoder
CN115015869A (en) * 2022-06-27 2022-09-06 清华大学 Learnable low frequency broadband radar target parameter estimation method, apparatus and program product
CN115097398A (en) * 2022-07-01 2022-09-23 西安电子科技大学 Radar anti-interference signal recovery method based on cross-domain signal low-loss recovery network
CN115842566A (en) * 2022-11-28 2023-03-24 哈尔滨工程大学 CNN-Bi-LSTM-based interference machine self-interference digital cancellation method
CN115951322A (en) * 2022-12-30 2023-04-11 广州极飞科技股份有限公司 Radar signal processing method and device for unmanned aerial vehicle, electronic equipment and storage medium

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
代哲;杨艳;: "基于深度学习的超声图像去噪方法研究", 信息技术, no. 09 *
张绍明;陈鹰;林怡;: "小波支持向量机在SAR图像降噪中的应用", 计算机工程与应用, no. 03 *
马飞;曹泽阳;任晓东;: "基于RS的GMDH神经网络在空袭目标识别中的应用", 空军工程大学学报(自然科学版), no. 01 *

Also Published As

Publication number Publication date
CN116430347B (en) 2023-08-22

Similar Documents

Publication Publication Date Title
CN111965632B (en) Radar target detection method based on Riemann manifold dimensionality reduction
EP2041667B1 (en) Method and apparatus for target discrimination within return signals
CN112308008B (en) Radar radiation source individual identification method based on working mode open set of transfer learning
CN109683126A (en) Direction of arrival measurement method, signal handling equipment and storage medium
CN108401489B (en) A kind of quantum chaos wave packet digital signal generation method
CN102736066B (en) For automatically determining the system and method for noise threshold
CN113962151A (en) Intelligent distance decoy identification method based on deep convolution transfer learning
Xu et al. Research on active jamming recognition in complex electromagnetic environment
CN114065803A (en) Training method, identification method and device of interference signal identification model
CN116430347B (en) Radar data acquisition and storage method
CN111277523B (en) Modulation mode determination method and device
KR20180088009A (en) Method and apparatus for distance measurement using radar
CN112213697A (en) Feature fusion method for radar deception jamming recognition based on Bayesian decision theory
CN114223270B (en) Training method and device for antenna signal processing model, antenna and storage medium
CN107102305B (en) Robust cognitive radar transmitting and receiving joint design method in clutter environment
CN106291500B (en) System on chip and its object detection method based on FPGA
CN114578293B (en) Electric scanning radar signal identification method by using intercepted signal amplitude value
CN111948613B (en) Ship-borne ground wave radar target detection method based on self-adaptive background area selection
CN109031188B (en) Monte Carlo-based narrow-band radiation source frequency difference estimation method and device
CN117498982A (en) Unmanned aerial vehicle communication interference suppression method based on spectrum sensing
CN115236620A (en) Intermittent sampling type interference identification and anti-interference target detection method based on DCNN
WO2021117078A1 (en) Radar device and target detection method
CN108333561B (en) Multi-emission-source passive positioning method based on position and waveform parameter joint estimation
KR101877219B1 (en) Apparatus and method for recognizing a target based on 2D data
CN105259538B (en) One kind is based on the convergent signal quality evaluating method of signal characteristic and device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant