CN114374407B - Spatial channel characteristic prediction method, system and storable medium based on m sequence - Google Patents
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Abstract
The invention discloses a spatial channel characteristic prediction method, a spatial channel characteristic prediction system and a storage medium based on m sequences, which belong to the technical field of underwater acoustic channel communication and comprise the following steps: setting array parameters and transmitting signal parameters; intercepting sampling signals according to set array parameters and transmitting signal parameters; carrying out delay summation beam forming on the intercepted sampling signals; capturing the data formed by the delay summation wave beams to obtain a capturing result; generating a delay angle distribution diagram according to the capturing result, and predicting signal delay and incidence angle; and carrying out Doppler analysis on the path signal according to the signal delay and incidence angle prediction result to complete the prediction of the spatial channel characteristics. The method solves the problems of traditional direction of arrival prediction, delay and Doppler, thereby improving the prediction performance of the spatial channel characteristics.
Description
Technical Field
The invention relates to the technical field of underwater acoustic channel communication, in particular to a spatial channel characteristic prediction method and system based on m sequences and a storable medium.
Background
Whatever the communication mode employed, the propagation characteristics of the underwater acoustic channel have a significant impact on the effectiveness and reliability of the underwater acoustic communication system. Before a communication system is designed, the characteristics of the underwater acoustic channel need to be fully known, so the underwater acoustic channel is an important aspect in the study of underwater acoustic communication.
The arrival of a signal from a transmitter to a receiver may be by way of many different propagation paths. During the propagation process of the underwater acoustic signal, the acoustic line is bent due to the non-uniformity of the medium, and multipath effect is generated by reflection and refraction of the underwater acoustic signal through the seabed and the sea surface. In a higher aspect, the energy of the multipath in the actual environment tends to reach the receiver in a group, so that the simultaneous reaching multipath can be defined as a cluster, the occurrence of the multipath cluster is derived from the observation of people in wireless channel measurement, the phenomenon also exists in an underwater channel, the application of the multipath cluster concept can well improve the performance of channel modeling, more channel information is obtained on the basis of the phenomenon, and a more excellent communication system is developed. Multipath exists in the actual channel in clusters, primarily due to the discrete distribution of scatterers in the physical channel, each of which forms one or more paths through which acoustic energy is transmitted from the transmitter to the receiver. At the receiver, the clusters arrive at different times and from different angles, but the multipath signals within each cluster are very similar. Thus, clusters can be observed in the time delay domain, the angle domain.
In order to better study the characteristics of the spatial channels, multipath clusters are observed and create a priori knowledge for the development of the communication system, in the prior art, the incident direction of signals is usually predicted by using a traditional direction of arrival (DOA) prediction algorithm, and then signals in different spaces are separated and utilized by using beam forming, so that delay and Doppler predictions are not involved, or delay and Doppler predictions are performed on a single receiving channel. In general, there are a large number of methods for signal azimuth spectrum prediction, delay and doppler prediction, but the methods are used separately, and although the signal direction can be predicted first and then the delay can be predicted, the resolution problem is caused, that is, signals with similar arrival directions but different arrival times cannot be resolved, or signals with similar arrival times but different arrival directions cannot be resolved.
Specifically, the problem with the conventional direction of arrival (DOA) prediction method is:
the first problem, the DOA prediction problem of coherent sources, is that the signals of each path are coherent signal sources because of complex propagation environment, especially for communication, the signals reach the receiver through reflection and refraction, and for coherent signal sources, general DOA prediction algorithms, such as subspace-type algorithms like traditional MUSIC, ESPRIT, and the like, cannot effectively distinguish the DOA of the signals. In a communication system, channel characteristics may be complex, the number of coherent signal sources may be further increased along with the increase of the number of paths, the channel characteristics are unknown, the number of coherent signal sources reaching a receiver is unknown, if complete decoherence is required, great redundancy needs to be left, which may cause an array to be very huge, and a huge array may cause a lot of inconveniences in practical use.
The second problem is that the number of signal sources is predicted, most algorithms in array signal processing need to know the number of incident signals, and when the number of the signal sources is unknown, the number of the signal sources needs to be predicted first or redundancy is reserved, and when the number of the signal sources is predicted to have errors, DOA prediction results are seriously affected. These problems need to be further solved for subspace DOA prediction algorithms, but classical delay-and-sum beamforming has the advantage that the delay-and-sum beamforming only needs to change the delay of each array input, scans the incident signal in a certain direction, and then uses a matched filter, i.e. the input signal is related to the sample signal to obtain the output signal with the maximum signal-to-noise ratio, which is irrelevant to the number of the signal incident coherent sources, although the resolution of the delay-and-sum beamforming may not be resolved due to poor resolution, as long as the incident signal exists in a certain direction, the gain exists in that direction. The delay summation beam forming method has the advantages of small operation amount, good robustness and the like, but the array gain provided by the delay summation beam forming method is limited, the suppression capability on strong interference is not strong, the spatial resolution is limited by Rayleigh limit, and multiple targets positioned in the beam width cannot exist.
Third, in the underwater acoustic communication category, if long-distance communication is to be performed, the signal-to-noise ratio reaching the receiving end is low due to large propagation loss in the sea, and the interference is serious due to the influence of various severe environments in the sea, and these problems are to be solved by the DOA algorithm of subspace class or by the delay-and-sum beamforming.
Therefore, how to provide a spatial channel characteristic prediction method, system and storable medium based on m-sequences is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of this, the present invention provides a spatial channel characteristic prediction method, system and storable medium based on m-sequences, which solves the problems of conventional DOA prediction, delay and Doppler.
In order to achieve the above object, the present invention provides the following technical solutions:
in one aspect, the invention provides a spatial channel characteristic prediction method based on m sequences, which comprises the following specific steps:
s100: setting array parameters and transmitting signal parameters;
s200: intercepting sampling signals according to set array parameters and transmitting signal parameters;
s300: carrying out delay summation beam forming on the sampling signals;
s400: capturing the data obtained in the step S300 to obtain a capturing result;
s500: generating a delay angle distribution diagram according to the capturing result, and acquiring a path signal, an angle corresponding to the path signal and delay information according to the delay angle distribution diagram;
s600: and carrying out Doppler analysis on the path signals according to the m sequences and the wave beam formation to complete the prediction of the spatial channel characteristics.
Preferably, in S100, setting the array parameter includes:
s110: the distance between the confidence source and the reference array element is set as follows:
r>>2D 2 /λ
wherein r is the distance between the information source and the reference array element, D is the array aperture, and lambda is the signal wavelength of the information source;
s120: setting array element spacing as follows:
wherein, d is the array element interval;
s130: the number of array elements is set to be 5-20.
Preferably, in S100, setting parameters of the transmission signal includes: the transmitting signal is a section of signal which spreads the single frequency signal by m sequence, and the formula is:
C(t)＝c(t)cos(ω c t)-c(t)sin(ω c t)
where c (t) is the spread spectrum signal,c i e (-1, 1) is the ith symbol of m sequence, T c For symbol interval, N m For m sequence period, +.>Is a symbol pulse shaping filter omega c Is the carrier frequency.
Preferably, the step S200 of intercepting the sampling signal includes: collecting multiple array element signals in the transmitting signal, and intercepting a signal with a length of N from each array element signal, wherein N=N m T c Is the length of the spread signal c (t).
Preferably, the step S300 of performing delay-sum beamforming on the truncated sampling signal includes:
s310: FIR filtering the sampled signal intercepted by the mth path (m=1, 2, …, M), and outputting the filtered output u of the mth path signal m (t)。
S320: for u m (t) performing appropriate time delays, such as τ (m, θ) (corresponding to the amount of delay required for the steering angle of the beam to be θ), and then summing the resulting beamformed data at steering angle θ
In which θ belongs to the angular range [ θ ] of the scan min ,θ max ]τ is one period of the m sequence, and all angles in the range are scanned sequentially to obtain the data of the formed wave beams in each direction;
preferably, the capturing the data obtained in S300 in S400 includes:
s410: detecting the data after beam forming one by one, and judging whether the maximum value in capturing exceeds a preset threshold value;
s420: if the maximum value does not exceed the preset threshold value, returning to S200, delaying the starting point of the intercepted signal by N/2 compared with the last time, and intercepting the signal with the length of N from the starting point;
s430: if the maximum value exceeds the preset threshold value, the capturing is successful.
Preferably, the S500 includes: combining the successfully captured delay summation beam formed data with the m sequence to obtain a delay angle distribution diagram, and acquiring a path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram.
Preferably, the S600 includes: and (3) taking out the signal of the path by utilizing the delay summation beam formed data successfully captured and the autocorrelation function of the m sequence according to the data acquired in the step (S500) for Doppler analysis, and completing the prediction of the spatial channel characteristics.
In another aspect, the present invention provides an m-sequence based spatial channel characteristic prediction system, including:
the presetting module is used for setting array parameters and transmitting signal parameters;
the intercepting module is connected with the preset module and used for intercepting sampling signals according to set array parameters and emission signal parameters;
the processing module is connected with the intercepting module and is used for carrying out delay summation beam forming on intercepted sampling signals;
the capturing module is connected with the processing module and is used for capturing the data obtained in the step S300 to obtain a capturing result;
the generation module is connected with the capture module and is used for generating a delay angle distribution diagram according to a capture result and acquiring a path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram;
and the prediction module is connected with the generation module and is used for carrying out Doppler analysis on the path signals according to the m-sequence and the wave beam formation to complete the prediction of the spatial channel characteristics.
In one aspect, the invention also provides a non-transitory computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of an m-sequence based spatial channel characteristic prediction method.
Compared with the prior art, the invention discloses a spatial channel characteristic prediction method, a spatial channel characteristic prediction system and a storage medium based on m sequences, solves the problems of traditional DOA prediction, time delay and Doppler, and improves the spatial channel characteristic prediction performance, and particularly:
(1) By combining delay and summation beam forming and rapid parallel capturing, the signal to noise ratio of an input signal can be improved, the capturing performance is better than that of a single array element, and more accurate delay and Doppler frequency offset estimation can be obtained;
(2) The time delay-angle distribution diagram can be obtained by the invention, the space channel characteristic can be better analyzed, and a foundation is laid for the development of a communication system;
(3) Compared with the common delay and sum beam forming, the method has the advantages that the method inherits the excellent characteristics, is simple in structure, is not influenced by estimating a coherent signal source, solves the problem of resolution, enables the resolution to be higher by rising a spatial spectrum to a delay-angle spectrum, is not influenced by the number of the coherent sources, suppresses other strong interference by the pseudo-random characteristic of m sequences, is not influenced by interference signals, and only estimates the signal incidence direction, delay and Doppler of a transmitting terminal;
(4) The m-sequence excellent correlation can stably estimate the signal incidence direction, the time delay and the Doppler under the condition of low signal-to-noise ratio;
(5) The invention does not need to estimate the number of signal sources and the coherent sources generated by multipath have no influence on the signal sources, and can maintain the aperture of the array unchanged in different channel scenes, thereby having more adaptability;
(6) In special environment, when the communication is not suitable for adding single frequency signal, the m sequence is used as typical spread spectrum sequence, and the invention is more suitable for spread spectrum communication system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a spatial channel characteristic prediction method based on m sequences;
FIG. 2 is a schematic diagram of a fast parallel capture algorithm according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a proportional peak decision provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of a time-frequency two-dimensional search provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram of a delay-angle distribution according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an m-sequence-based spatial channel characteristic prediction system according to 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. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
On the one hand, referring to fig. 1, the embodiment of the invention discloses a spatial channel characteristic prediction method based on m sequences, wherein the spatial channel characteristic comprises time delay, incidence angle and Doppler analysis, and the specific steps are as follows:
s100: setting array parameters and transmitting signal parameters of a space channel;
s200: intercepting sampling signals according to set array parameters and transmitting signal parameters;
s300: carrying out delay summation beam forming on the sampling signals;
s400: capturing the data obtained in the step S300 to obtain a capturing result;
s500: generating a delay angle distribution diagram according to the capturing result, and acquiring a path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram;
s600: and carrying out Doppler analysis on the path signals according to the m sequences and the wave beam formation to complete the prediction of the spatial channel characteristics.
Specifically, in this embodiment, the spreading sequence selects the m-sequence to perform time-frequency two-dimensional search, and because the m-sequence has a better auto-correlation characteristic, when the m-sequence performs sliding correlation, the local m-sequence is aligned with the m-sequence in the input signal, so that a higher amplitude gain can be obtained, and the arrival time of each path can be known.
More specifically, within one period of the m-sequence, i.e., 0.ltoreq.τ.ltoreq.NT c Wherein N is the length of the m-sequence, T c For a time width of one symbol, the m-sequence is a rectangular wave signal with amplitudes +1 and-1, and then the autocorrelation function of the m-sequence is expressed as:
in a specific embodiment, in order to meet the condition that the signal source is in the far field, the array is set to be a one-dimensional, two-dimensional or high-dimensional uniform linear array, and further, uniform linear array parameters and transmission signal parameters need to be set.
Specifically, setting uniform linear array parameters includes:
for the far field of a receiving array of a required sound source, the curvature change of wave fronts generated at different array elements when the sound waves reach the receiving array is negligible, so that the sound waves propagated according to spherical waves can be reasonably assumed to be plane waves, and the definition of the far field (Fraunhofer) area of the array is that:
r>>2D 2 /λ (2)
where r is the distance from the source to the reference element, D is the array aperture, and λ is the signal wavelength of the source.
Specifically, in this embodiment, a uniform linear array is adopted, in order to avoid spatial leakage caused by direction ambiguity, grating lobes do not need to appear in the range of the incident angle [ -90 °,90 ° ], and at this time, the array element spacing d needs to satisfy:
specifically, for narrowband sources, the communication signal is generally a wideband source, where each frequency of the signal corresponds to a different wavelength λ, where the theoretical λ in the formula should take the smallest wavelength λ min The invention uses m sequence to do DOA prediction, the m sequence can be used as broadband signal after spreading single frequency signal, lambda can be the wavelength lambda of the central frequency of communication signal fc At least half of the frequencies will not generate grating lobes, the signals coming through the grating lobes will be deformed seriously, the correlation of m sequences in the modulated signals will be greatly faded, the m sequences are used as matched filters to receive, no great peak value will be generated, and the propagation characteristics of the high-frequency signals are affected, so that the requirement of the array element spacing d can be relaxed.
More specifically, the number of array elements is also set in this embodiment, and a method of combining delay summation beam forming with m-sequences is adopted, so that the more array elements of the array are, the greater the beam forming gain is. However, in practice, due to the existence of the spatial coherence radius of the signal, the correlation between the signals received by the array elements of the array is weakened under the condition of large array aperture, at this time, the array gain is not increased linearly with the increase of the number of the array elements, at the same time, the number of the array elements is not too small, the beam width is too wide due to too small, and the DOA prediction generates an angle ambiguity, so that the number of the array elements needs to be reasonably determined according to the actual use condition, and the number of the array elements is selected to be 10 in this embodiment.
In a specific embodiment, setting the parameters of the transmit signal includes: the transmitting signal is a section of signal which spreads the single frequency signal by m sequence, and the formula is:
C(t)＝c(t)cos(ω c t)-c(t)sin(ω c t) (4)
where c (t) is the spread spectrum signal,c i e (-1, 1) is the ith symbol of m sequence, T c For symbol interval, N m For m sequence period, +.>Is a symbol pulse shaping filter, the present embodiment takes a root raised cosine filter, the roll-off coefficient is alpha, then the signal bandwidth b= (1+alpha)/T c Carrier frequency omega c And the selection is needed to be reasonably carried out according to the requirement of the array element spacing.
In a specific embodiment, intercepting the sampled signal includes: collecting multiple array element signals in the transmitting signal, and intercepting a signal with a length of N from each array element signal, wherein N=N m T c Is the length of the spread spectrum signal c (t);
in one particular embodiment, delay-and-sum beamforming the truncated sampled signal includes:
FIR filtering is carried out on the sampling signal intercepted by the mth path (m=1, 2, …, M), and the output after the mth path of signal filtering is u m (t)。
For u m (t) Appropriate time delays are performed and then summed to obtain beamformed data for steering angle θ:
in which θ belongs to the angular range [ θ ] of the scan min ,θ max ]τ is one period of the m sequence, and all angles in the range are scanned sequentially to obtain the data of the formed wave beams in each direction;
specifically, the scanning range is set to be-90 degrees to 90 degrees, the acoustic path difference between the array elements is utilized to delay the array elements, the time delay between the array elements is controlled, the scanning angle can be controlled, and the purpose of rotating the directivity of the array wave beam is achieved.
More specifically, before delay, the signal will pass through an FIR filter, where a band-pass filter is selected, so as to filter out-of-band interference and improve the signal-to-interference-and-noise ratio of the signal. Carrying out time delay on each direction of-90 degrees to 90 degrees, and finally summing to obtain data after wave beam formation of each direction of-90 degrees to 90 degrees;
in a specific embodiment, capturing the beamformed data includes:
since there is only one segment of the transmitted signal and the receiving end does not know whether the signal is coming or not, this embodiment has a different part from the conventional DOA method, adding one acquisition stage.
Specifically, referring to the schematic diagram of the fast parallel capturing algorithm shown in fig. 2, the beamformed data is detected one by one as an input signal, and then the maximum value is selected and output;
more specifically, referring to fig. 3, which is a schematic diagram of proportional peak decision, the output result is sent to proportional peak decision to see if the threshold is exceeded, if not, the input data is stepped by N/2 length, and steps S200, S300 and S400 are repeated until the threshold is exceeded, and the capturing is successful.
More specifically, the detailed principle of the fast parallel capture is:
because the signal of the transmitting terminal is compressed or expanded to cause the carrier frequency of the transmitting signal to deviate due to the influence of Doppler effect, the local m-sequence is multiplied with the cosine signal and the sine signal respectively to obtain two paths of signals, the formula is as follows,
C I (t)＝c(t)cos(ω 0 t) (5)
C Q (t)＝c(t)sin(ω 0 t) (6)
wherein omega 0 Is the local carrier frequency.
Since the theory of cyclic correlation is used, a formula of cyclic correlation is given, assuming that the cross-correlation expression of the signal to be processed x (m) and the local matching signal h (m) is as follows:
the output y (n) of the cross-correlation is fourier transformed into:
Y(k)＝H(k)X * (k) (8)
therefore, the cross-correlation of x (m) and h (m) can be obtained by changing x (m) and h (m) into frequency domains, multiplying one of the frequency domains after conjugate is taken, and then carrying out inverse Fourier transform to obtain a correlation value.
Assuming that the channel is an additive white gaussian noise channel, the signal r (t) input by a single channel can be expressed as (note that r (t) here is not beamformed data, the single channel is derived first, and then the delay summed data):
r(t)＝A(c(t-τ)cos(ω' c (t-τ))-c(t-τ)sin(ω' c (t-τ)))+n(t) (9)
wherein A represents signal amplitude, τ represents signal time delay, ω' c Representing the carrier frequency of the received signal, N (t) is additive white Gaussian noise, and the bilateral power spectral density is N 0 /2. R (T) is represented as r (n) in discrete signals with a sampling interval T s 。
r(n)＝A(c(n-τ n )cos(ω' c (n-τ n ))-c(n-τ n )sin(ω' c (n-τ n )))+N(n) (10)
Omega 'at this time' c And omega 0 Digital angular frequency representing the received signal, will C I (t) is also expressed in discrete form:
C I (n)＝c(n)cos(ω 0 n) (11)
that C I (n) and r (n) are subjected to Fourier transform to obtain C I (k) With R (k), C I (k) Conjugate is multiplied by R (k) and then is subjected to inverse Fourier transform to obtain C I (n) is related to the cycle of r (n), and the time domain is:
wherein N' (N) is C I (n) correlation value with noise, θ is carrier phase of the received signal, due to ω' c +ω 0 Is a high frequency component and the summed value is smaller than the previous one, so neglecting its effect, and scoring it into the noise can be simplified as follows:
c can be obtained in the same way Q (n) is circularly related to r (n) and has the following expression:
the carrier frequency is unchanged when one-dimensional search is performed, the maximum value of the correlator output is found from all phases of the spread spectrum code, and obviously when tau n The first two terms of equations (13) and (14) take the maximum value when = -n, and the variable that eventually enters the arbiter is the maximum value when one-dimensional searching.
Let τ n = -n, assuming that the spreading sequence multiplies it by 1 and ignores the noise term, while being represented by euler equation e jx =cosx+jsnx, equations (13) and (14) can be changed to complex domainRow summation:
by converting equations (15) and (16) back to real numbers, it is possible to obtain:
final decision variable Y s The method comprises the following steps:
equation (19) already contains no carrier phase parameter θ, the magnitude of the decision variable is determined by the carrier frequency offset Δω and the spreading sequence length, and if the carrier frequency offset Δω=0, n is fixed, the final decision variable Y s The maximum value will be taken.
According to formula (19), the search frequency of the local carrier is set, and the maximum value can be searched for.
Referring to fig. 4, in the time-frequency two-dimensional search graph provided in this embodiment, a specific frequency-domain search step is set as follows:
let the relative radial velocity of both communication parties be v at maximum max (m/s), sound velocity c (m/s), center frequency f of transmitter 0 (Hz), the minimum frequency f of the search min The method comprises the following steps:
f min ＝f 0 (1-v max /c) (20)
a spread spectrum chip time width of t chip Expansion ofThe period of the frequency sequence is (2 n -1), the frequency minimum resolution Δf of the spread spectrum signal is:
Δf＝1/[t chip ×(2 n -1)] (21)
the step size of the frequency search can be set as Δf, and it can be seen that the longer the spreading sequence period, the more sensitive to doppler shift, and the smaller the search step size. Let the channel number of the frequency coarse search be N f Then:
N f ＝2×(f 0 -f min )/Δf (22)
in one embodiment, the data of successfully captured delay and sum beam forming is combined with m sequences to obtain a delay angle distribution diagram, and the signal delay and the incidence angle are predicted.
Specifically, the local signal C is obtained by using the data after the delay and sum beamforming and using the formulas (5) and (6) I (n) and C Q (n) performing cyclic correlation, wherein the input signal y (n) is:
wherein r (n-i:. Tau θ ) The data of a single channel after delay compensation is made by utilizing the acoustic path difference between array elements, wherein a first array element is taken as a reference array element, if a signal is incident from 0 DEG and is compensated according to an angle theta, the delay deviation tau of a second array element signal compared with the first array element is caused θ The M-th array element has delay deviation (M-1) tau compared with the first array element θ 。
Substituting r (n) in the formula (12) with the beamformed data y (n), while including the high frequency component in noise, gives a formula similar to the formula (13):
if the starting point of the reference array element input signal and the carrier frequency of the received signal can be obtained by utilizing the time-frequency two-dimensional search, τ in the above formula can be calculated n With frequency offsetΔω is eliminated, resulting in a simplified formula as follows:
equation (25) is also changed to complex number using the euler equation:
the above equation is similar to conventional beamforming except that the correlation function R (τ) of the m-sequence is added before, and the array output equation for conventional beamforming is given below for comparison:
as can be seen from a comparison of equations (26) and (27), the combination of the delay and sum beam forming and the m-sequence adds a R (n-iτ) with similar weight θ ) The correlation function of the m sequence can bring a series of benefits, firstly, other interference signals are not correlated with the m sequence due to the pseudo-randomness of the m sequence, the influence of strong interference signals can be avoided, and only the signal of a transmitting terminal is predicted; secondly, the high gain of the correlation function R (tau) can also enable the signal to be predicted under a very low signal-to-noise ratio; at the same time, when the predicted angle deviates, R (n-iτ θ ) Will decay faster than conventional beamforming and therefore be more stable than conventional beamforming. Also, since the times of arrival of the different paths are often different, the incident signal can find a unique time to angle correspondence, which is also critical to the observation of multipath clusters.
More specifically, referring to fig. 5, a time delay-angle distribution diagram provided in this embodiment is shown, according to the above principle, the process of jointly predicting the time delay and the incident angle is to use a time-frequency two-dimensional search to predict the carrier frequency, eliminate carrier frequency offset, then perform time delay summation beamforming in all directions, and perform rapid parallel capturing on beamformed data to obtain the time delay-angle distribution diagram.
In one embodiment, observing the time delay-angle distribution map, each cluster peak is a cluster of signals of different paths, and a signal of one path can be selected, and since the arrival angle and the time delay can be seen from the map, the signal of the path can be extracted by utilizing the correlation of beam forming and m-sequence for Doppler analysis.
On the other hand, referring to fig. 6, the present embodiment further provides an m-sequence based spatial channel characteristic prediction system, which includes:
the presetting module is used for setting array parameters and transmitting signal parameters;
the intercepting module is connected with the preset module and used for intercepting sampling signals according to the set array parameters and the transmitting signal parameters;
the processing module is connected with the interception module and is used for carrying out delay summation beam forming on intercepted sampling signals;
the capturing module is connected with the processing module and used for capturing the data obtained in the step S300 to obtain a capturing result;
the generation module is connected with the capture module and is used for generating a delay angle distribution diagram according to a capture result and acquiring a path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram;
and the prediction module is connected with the generation module and is used for carrying out Doppler analysis on the path signals according to the m-sequence and the wave beam formation to complete the prediction of the spatial channel characteristics.
In yet another aspect, the present embodiment further provides a non-transitory computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the m-sequence based spatial channel characteristic prediction method described above.
Compared with the prior art, the invention discloses a spatial channel characteristic prediction method, a spatial channel characteristic prediction system and a storage medium based on m sequences, solves the problems of traditional DOA prediction, time delay and Doppler, and improves the spatial channel characteristic prediction performance, and particularly:
(1) By combining delay and summation beam forming and rapid parallel capturing, the signal to noise ratio of an input signal can be improved, the capturing performance is better than that of a single array element, and more accurate delay and Doppler frequency offset estimation can be obtained;
(2) The time delay-angle distribution diagram can be obtained by the invention, the space channel characteristic can be better analyzed, and a foundation is laid for the development of a communication system;
(3) Compared with the common delay and sum beam forming, the method has the advantages that the method inherits the excellent characteristics, is simple in structure, is not influenced by estimating a coherent signal source, solves the problem of resolution, enables the resolution to be higher by rising a spatial spectrum to a delay-angle spectrum, is not influenced by the number of the coherent sources, suppresses other strong interference by the pseudo-random characteristic of m sequences, is not influenced by interference signals, and only estimates the signal incidence direction, delay and Doppler of a transmitting terminal;
(4) The m-sequence excellent correlation can stably estimate the signal incidence direction, the time delay and the Doppler under the condition of low signal-to-noise ratio;
(5) The invention does not need to estimate the number of signal sources and the coherent sources generated by multipath have no influence on the signal sources, and can maintain the aperture of the array unchanged in different channel scenes, thereby having more adaptability;
(6) In special environment, when the communication is not suitable for adding single frequency signal, the m sequence is used as typical spread spectrum sequence, and the invention is more suitable for spread spectrum communication system.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The spatial channel characteristic prediction method based on the m sequence is characterized by comprising the following specific steps:
s100: setting array parameters and transmitting signal parameters;
s200: intercepting sampling signals according to set array parameters and transmitting signal parameters;
s300: carrying out delay summation beam forming on the sampling signals;
s400: capturing the data obtained in the step S300 to obtain a capturing result;
s500: generating a delay angle distribution diagram according to the capturing result, and acquiring a path signal, an angle corresponding to the path signal and delay information according to the delay angle distribution diagram;
s600: and carrying out Doppler analysis on the path signals according to the m sequences and the wave beam formation to complete the prediction of the spatial channel characteristics.
2. The m-sequence based spatial channel characteristic prediction method according to claim 1, wherein in S100, setting an array parameter comprises:
s110: the distance between the confidence source and the reference array element is set as follows:
r>>2D 2 /λ
wherein r is the distance between the information source and the reference array element, D is the array aperture, and lambda is the signal wavelength of the information source;
s120: setting array element spacing as follows:
wherein, d is the array element interval;
s130: the number of array elements is set to be 5-20.
3. The m-sequence based spatial channel characteristic prediction method according to claim 1, wherein in S100, setting parameters of a transmission signal comprises: the transmitting signal is a section of signal which spreads the single frequency signal by m sequence, and the formula is:
C(t)＝c(t)cos(ω c t)-c(t)sin(ω c t)
where c (t) is the spread spectrum signal,c i e (-1, 1) is the ith symbol of m sequence, T c For symbol interval, N m For m sequence period, +.>Is a symbol pulse shaping filter omega c Is the carrier frequency.
4. The m-sequence based spatial channel characteristic prediction method according to claim 3, wherein said S200 interception of the sampling signal comprises: collecting multiple array element signals in the transmitting signal, and intercepting a signal with a length of N from each array element signal, wherein N=N m T c Is the length of the spread signal c (t).
5. The m-sequence based spatial channel characteristic prediction method according to claim 1, wherein the specific process of performing delay and sum beamforming on the intercepted sampled signal in S300 comprises:
s310: FIR filtering is carried out on the sampling signal intercepted by the mth path (m=1, 2, …, M), and the output after the mth path of signal filtering is u m (t)；
S320: for u m (t) time-delaying and then summing to obtain beamformed data of steering angle θ:
in which θ belongs to the angular range [ θ ] of the scan min ,θ max ]τ is one period of the m sequence, and all angles in the range are scanned sequentially to obtain the data after the beam forming in each direction.
6. The m-sequence based spatial channel characteristic prediction method according to claim 4, wherein the specific process of capturing the data obtained in S300 in S400 comprises:
s410: detecting the data after beam forming one by one, and judging whether the maximum value in capturing exceeds a preset threshold value;
s420: if the maximum value does not exceed the preset threshold value, returning to S200, delaying the starting point of the intercepted signal by N/2 compared with the last time, and intercepting the signal with the length of N from the starting point;
s430: if the maximum value exceeds the preset threshold value, the capturing is successful.
7. The m-sequence based spatial channel characteristic prediction method according to claim 1, wherein said S500 comprises: combining the successfully captured delay summation beam formed data with the m sequence to obtain a delay angle distribution diagram, and acquiring a path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram.
8. The m-sequence based spatial channel characteristic prediction method according to claim 1, wherein said S600 comprises: and (3) taking out the signal of the path by utilizing the delay summation beam formed data successfully captured and the autocorrelation function of the m sequence according to the data acquired in the step (S500) for Doppler analysis, and completing the prediction of the spatial channel characteristics.
9. An m-sequence based spatial channel characteristic prediction system using the m-sequence based spatial channel characteristic prediction method according to any one of claims 1 to 8, comprising:
the presetting module is used for setting array parameters and transmitting signal parameters;
the intercepting module is connected with the preset module and used for intercepting sampling signals according to set array parameters and emission signal parameters;
the processing module is connected with the intercepting module and is used for carrying out delay summation beam forming on intercepted sampling signals;
the capturing module is connected with the processing module and is used for capturing the data obtained in the step S300 to obtain a capturing result;
the generation module is connected with the capture module and is used for generating a delay angle distribution diagram according to a capture result and acquiring a path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram;
and the prediction module is connected with the generation module and is used for carrying out Doppler analysis on the path signals according to the m-sequence and the wave beam formation to complete the prediction of the spatial channel characteristics.
10. A non-transitory computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the m-sequence based spatial channel characteristic prediction method according to any of claims 1-8.
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