CN113589250B - Sparse learning coherent agile radar distance high-resolution processing method - Google Patents
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Abstract
The invention discloses a sparse learning phase-coherent agile radar distance high-resolution processing method, wherein phase correlation refers to the existence of a determined phase relation between pulses, and the phase correlation processing of the pulses can improve the signal-to-noise ratio and obtain high distance resolution. Agility refers to the random selection of carrier frequencies for each pulse in a burst of pulses transmitted by the radar within a frequency band. Because of the discontinuous frequency spectrum of the frequency agile signal, the range side lobe of the frequency agile signal is often higher, which generally causes the problems that a strong target floods a weak target or a plurality of target side lobes are synthesized into a false target, and the like. The invention synthesizes the phase agile frequency signal into a high resolution range profile as a sparse signal reconstruction problem, constructs a Bayesian model and estimates model parameters by using a maximum post-inspection method. And solving the parameter updating expression by minimizing the objective function, and finally realizing the distance high-resolution processing of the coherent agile frequency conversion radar. The method is suitable for the problems of range sidelobe suppression, high-resolution range profile synthesis and the like of the coherent agile radar or discontinuous spectrum signals.
Description
Technical Field
The invention relates to the technical field of radar signal processing, in particular to a sparse learning coherent agile frequency radar distance high-resolution processing method.
Background
And determining the initial phase relation of each pulse in the coherent agile radar transmitting pulse train, and randomly hopping carrier frequencies within the frequency band range. The coherent agile radar has the following advantages: from an electronic countermeasure perspective, the discontinuity in the signal spectrum can avoid concentrating very high energy in a narrower frequency band, thereby reducing the likelihood of being found by an adversary; the coherent agile radar synthesizes a large broadband signal with fewer pulses, so that the resolution of the system can be ensured; the coherent agile radar also has the advantage of a pulse signal. However, due to the discontinuity of the signal spectrum, the range side lobe is often higher, which often causes the problems that the strong target side lobe floods the weak target or a plurality of target side lobes are synthesized into a false target, and the like, thereby causing the situations of false target generation, target loss, and the like, and affecting the working performance of the radar. In addition, the electromagnetic environment of the current radar work is increasingly complex, and a section of clean frequency band is difficult to find, so that the research on the high-resolution range profile synthesis method of the coherent agile radar has important significance.
Aiming at the problem of sidelobe suppression of the coherent agile radar, the aim of reducing the range sidelobes can be achieved by selecting the optimal frequency point combination through a signal waveform design approach (see in detail, research on random frequency hopping signal design and processing technology, peng Jiang, national defense university of science and technology, 2016), but the algorithm can not be used when the available frequency band is limited, and the algorithm generally needs to consume more calculation time; the matched filter can also be weighted in the frequency domain by using a spectrum correction algorithm (see in detail: application of the spectrum correction algorithm in sidelobe suppression of random frequency hopping interference, wen Shuai, etc., ship electronic countermeasure, 2018, 4 th phase), but the method is at the cost of losing signal to noise ratio; or constructing a cross vector by dividing a speed grid, constructing a semi-positive programming (SDP) problem according to a signal model and the cross modulation vector, and solving the SDP problem to obtain a distance parameter estimation value, wherein the construction of the cross modulation vector is based on the speed grid and is greatly influenced by grid intervals (see patent CN201910132135.6, speed-distance parameter joint estimation method and device of the frequency agile radar, huang Tianyao and the like). The invention provides a sparse learning method for processing the range of a coherent agile radar, which aims to solve the problems of strong randomness, large amplitude and the like of the range sidelobe of the coherent agile radar.
Disclosure of Invention
The invention aims to provide a sparse learning coherent agile radar distance high-resolution processing method, which is used for synthesizing a high-resolution range image by using a coherent agile radar as a sparse signal reconstruction problem, constructing a Bayesian model, estimating model parameters by using a maximum post-inspection method, establishing an objective function, and finally obtaining a sparse range image by enabling the objective function to be minimum and solving parameters to update a formula, so that the coherent agile radar distance high-resolution processing is realized.
In order to achieve the above purpose, the present invention is realized by the following technical scheme:
a sparse learning coherent agile radar distance high resolution processing method comprises the following steps:
s1, randomly selecting a certain number of frequencies from a frequency set as frequency stepping amounts of each pulse relative to a central frequency, and constructing a periodic coherent frequency agile transmitting signal;
s2, constructing a sparse signal reconstruction model, and taking a DFT matrix as a dictionary matrix;
s3, initializing a P matrix, a range profile and noise power;
s4, updating the range profile, the noise power and the P matrix each time of iteration;
s5, judging whether the iteration result converges or whether the iteration number reaches an upper limit; if yes, go to S6; otherwise, turning to S4;
s6, stopping iteration and outputting a high-resolution range profile.
Optionally, the step S1 specifically includes:
let the center frequency f c Pulse width T p Pulse repetition interval is T r M (M < N) frequencies are randomly selected from the frequency set {0, Δf, …, (N-1) Δf } as the frequency step amounts f of each pulse relative to the center frequency m The carrier frequency of each pulse is F m ＝f c +f m M=1, …, M; the signal of one waveform period transmitted by the coherent agile radar is:
wherein rect [. Cndot ] is a rectangular window function.
Optionally, the step S2 specifically includes:
considering the synthesized high-resolution range profile as a sparse signal reconstruction problem, the sparse signal reconstruction model is:
y＝Ax+ε
wherein epsilon C M×1 Is noise, x is C N×1 For sparse distance image, y ε C M×1 Dictionary matrix A epsilon C for frequency domain sample data of random frequency hopping echo signals M×N For the DFT matrix:
optionally, the step S3 specifically includes:
initializing a P matrix, a range profile x and noise power eta;
the P matrix is initialized with a matched filter:
wherein a is n Is the nth column of dictionary matrix a;
according to p n ＝|x n | 2-q Initializing each element in the range profile as
According to the expression of eta, the initial value of eta is
Optionally, the step S4 specifically includes:
iteratively updating the distance image x, the noise power eta and the P matrix;
x (t+1) ＝P (t) A H (AP (t) A H +η (t) I) -1 y
P (t+1) ＝diag{p (t+1) },p＝[p 1 ,p 2 ,...,p N ],
optionally, the step S5 specifically includes:
judging whether the iteration satisfies x (t) -x (t-1) || 2 /||x (t) || 2 < delta (delta is a minimum value greater than zero) or overlap
The generation times reach the upper limit, if so, the process goes to S6; otherwise, go to S4 to continue the iteration.
Compared with the prior art, the invention has the following advantages:
the invention regards the synthesis of the high-resolution range profile by using the agile frequency conversion radar as a sparse signal reconstruction problem, constructs a Bayesian model description sparse signal reconstruction problem, estimates parameters by using a maximum post-inspection method, and finally obtains a reconstructed sparse range profile by using a minimum solution parameter updating method of an objective function and iterating the updated parameters. Aiming at the problems that the range side lobe of the coherent agile radar is high and has strong randomness, and a false target is synthesized by the target side lobe and a weak target is submerged by the strong target side lobe, the invention provides a method for inhibiting the range side lobe, which can be used for synthesizing a high-resolution range profile by the coherent agile radar.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method according to an embodiment of the invention;
FIG. 2 is a range profile of a conventional NDFT synthesis;
FIG. 3 is a range profile synthesized in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. It should be noted that the drawings are in a very simplified form and are all to a non-precise scale, merely for the purpose of facilitating and clearly aiding in the description of embodiments of the invention. For a better understanding of the invention with objects, features and advantages, refer to the drawings. It should be understood that the structures, proportions, sizes, etc. shown in the drawings are for illustration purposes only and should not be construed as limiting the invention to the extent that any modifications, changes in the proportions, or adjustments of the sizes of structures, proportions, or otherwise, used in the practice of the invention, are included in the spirit and scope of the invention which is otherwise, without departing from the spirit or essential characteristics thereof.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
Referring to fig. 1, the present embodiment provides a sparse learning method for processing a range of a strapdown variable frequency radar, where the strapdown variable frequency radar is a pulse system radar, and the strapdown refers to that a certain phase relationship exists between each pulse, so that the signal-to-noise ratio can be improved and a high range resolution can be obtained by performing strapdown processing on each pulse. Agility refers to the random selection of carrier frequencies for each pulse in a burst of pulses transmitted by the radar within a frequency band. Because of the discontinuous frequency spectrum of the frequency agile signal, the range side lobe of the frequency agile signal is often higher, which generally causes the problems that a strong target floods a weak target or a plurality of target side lobes are synthesized into a false target, and the like. In order to solve the problem, the invention synthesizes the phase agile signals into a high-resolution range profile as a sparse signal reconstruction problem, constructs a Bayesian model and estimates model parameters by using a maximum post-inspection method. And solving the parameter updating expression by minimizing the objective function, and finally realizing the distance high-resolution processing of the coherent agile frequency conversion radar. The method is suitable for the problems of range sidelobe suppression, high-resolution range profile synthesis and the like of the coherent agile radar or discontinuous spectrum signals.
The sparse learning coherent agile radar distance high resolution processing method in the embodiment specifically comprises the following steps:
s1, randomly selecting a certain number of frequencies from a frequency set as frequency stepping amounts of each pulse relative to a central frequency, and constructing a periodic coherent frequency agile transmitting signal;
s2, constructing a sparse signal reconstruction model, and taking a DFT matrix as a dictionary matrix;
s3, initializing a P matrix, a range profile and noise power;
s4, updating the range profile, the noise power and the P matrix each time of iteration;
s5, judging whether the iteration result converges or whether the iteration number reaches an upper limit; if yes, go to S6; otherwise, turning to S4;
s6, stopping iteration and outputting a high-resolution range profile.
In this embodiment, the step S1 specifically includes:
let the center frequency f c Pulse width T p Pulse repetition interval is T r M (M < N) frequencies are randomly selected from the frequency set {0, Δf, …, (N-1) Δf } as the frequency step amounts f of each pulse relative to the center frequency m The carrier frequency of each pulse is F m ＝f c +f m M=1, …, M; the signal of one waveform period transmitted by the coherent agile radar is:
wherein rect [. Cndot ] is a rectangular window function.
In this embodiment, the step S2 specifically includes:
considering the synthesized high-resolution range profile as a sparse signal reconstruction problem, the sparse signal reconstruction model is:
y＝Ax+ε
wherein epsilon C M×1 Is noise, x is C N×1 For sparse distance image, y ε C M×1 Dictionary matrix A epsilon C for frequency domain sample data of random frequency hopping echo signals M×N For the DFT matrix:
in this embodiment, the step S3 specifically includes:
initializing a P matrix, a range profile x and noise power eta;
the P matrix is initialized with a matched filter:
wherein a is n Is the nth column of dictionary matrix a;
according to p n ＝|x n | 2-q Initializing each element in the range profile as
According to the expression of eta, the initial value of eta is
In this embodiment, the step S4 specifically includes:
iteratively updating the distance image x, the noise power eta and the P matrix;
x (t+1) ＝P (t) A H (AP (t) A H +η (t) I) -1 y
P (t+1) ＝diag{p (t+1) },p＝[p 1 ,p 2 ,...,p N ],
in this embodiment, the step S5 specifically includes:
judging whether the iteration satisfies x (t) -x (t-1) || 2 /||x (t) || 2 < delta (delta is a minimum value greater than zero) or the number of iterations reaches an upper limit, if so, go to S6; otherwise, go to S4 to continue the iteration.
The parameters of the radar signal set in this embodiment are: center frequency f c Frequency step interval Δf=4 MHz, available frequency number n=10 GHz512, randomly decimating the number of frequency points m=64. The distance resolution ρ can be calculated according to the radar signal parameters r =c/2/(n·Δf) is 0.0732m, where c is the speed of light. The parameters of the target are: the number of targets is 3, and the corresponding distance between target points is [16.0000, 16.0659, 17.4648 ]]. Let the upper limit of the number of iterations be 20 times, the norm q be 0.9, and delta be 0.0001.
Fig. 2 shows that the conventional range profile synthesizing method, that is, the range profile obtained by performing NDFT on the frequency domain sample, is used by the coherent agile radar, so that under the simulation parameters, the range profile obtained by performing NDFT processing has higher side lobe, and the first target and the second target cannot be resolved because the interval is smaller than one resolution.
Fig. 3 shows a high-resolution range profile synthesized by the method of the invention, from which the range side lobe can be obviously suppressed, and three targets can be resolved, which proves that the method of the invention can realize the range high-resolution processing of the coherent agile radar.
While the present invention has been described in detail through the foregoing description of the preferred embodiment, it should be understood that the foregoing description is not to be considered as limiting the invention. Many modifications and substitutions of the present invention will become apparent to those of ordinary skill in the art upon reading the foregoing. Accordingly, the scope of the invention should be limited only by the attached claims.
Claims (4)
1. A sparse learning coherent agile radar distance high resolution processing method is characterized by comprising the following steps:
s1, randomly selecting a certain number of frequencies from a frequency set as frequency stepping amounts of each pulse relative to a central frequency, and constructing a periodic coherent frequency agile transmitting signal;
s2, constructing a sparse signal reconstruction model, and taking a DFT matrix as a dictionary matrix;
s3, initializing a P matrix, a range profile and noise power;
s4, updating the range profile, the noise power and the P matrix each time of iteration;
s5, judging whether the iteration result converges or whether the iteration number reaches an upper limit; if yes, go to S6;
otherwise, turning to S4;
s6, stopping iteration and outputting a high-resolution range profile;
the step S2 specifically includes:
considering the synthesized high-resolution range profile as a sparse signal reconstruction problem, the sparse signal reconstruction model is:
y＝Ax+ε
wherein epsilon C M×1 Is noise, x is C N×1 For sparse distance image, y ε C M×1 Dictionary matrix A epsilon C for frequency domain sample data of random frequency hopping echo signals M×N For the DFT matrix:
the step S3 specifically includes:
initializing a P matrix, a range profile x and noise power eta;
the P matrix is initialized with a matched filter:
wherein a is n Is the nth column of dictionary matrix a;
according to p n ＝|x n | 2-q Initializing each element in the range profile as
According to the expression of eta, the initial value of eta is
2. The sparse learning coherent agile radar range high resolution processing method according to claim 1, wherein the step S1 specifically comprises:
let the center frequency f c Pulse width T p Pulse repetition interval is T r M (M < N) frequencies are randomly selected from the frequency set {0, Δf, …, (N-1) Δf } as the frequency step amounts f of each pulse relative to the center frequency m The carrier frequency of each pulse is F m ＝f c +f m ，m＝1，…,M；
The signal of one waveform period transmitted by the coherent agile radar is:
wherein rect [. Cndot ] is a rectangular window function; t represents time.
3. The sparse learning coherent agile radar range high resolution processing method according to claim 1, wherein the step S4 specifically comprises:
iteratively updating the distance image x, the noise power eta and the P matrix;
x (t+1) ＝P (t) A H (AP (t) A H +η (t) I) -1 y
4. the sparse learning coherent agile radar range high resolution processing method according to claim 1, wherein the step S5 specifically comprises:
judging whether the iteration satisfies x (t) -x (t-1) || 2 /||x (t) || 2 < delta (delta is greater thanMinimum value of zero) or the iteration number reaches the upper limit, if yes, the step S6 is carried out; otherwise, go to S4 to continue the iteration.
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| 基于内插外推的随机频率步进信号旁瓣抑制;刘俊豪;付朝伟;;《制导与引信》;第36卷(第02期);45-47 * |
| 基于压缩感知的脉间捷变频SAR成像研究;刘天鹏;刘振;魏玺章;;《电子学报》;第40卷(第06期);1074-1077 * |
| 基于稀疏贝叶斯方法的脉间捷变频ISAR成像技术研究;苏伍各;王宏强;邓彬;秦玉亮;刘天鹏;;《电子与信息学报》;第37卷(第01期);2-4 * |
| 基于随机脉冲重复间隔Radon-Fourier变换的相参积累;陈潜;付朝伟;刘俊豪;吴嗣亮;;《电子与信息学报》;第37卷(第05期);1086-1088 * |
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