A DOA estimation technology implementation method and system based on a spatial null steering algorithm

Abstract

A DOA estimation technology implementation method and system based on a spatial domain zero algorithm, comprising: obtaining an array flow type and preprocessing it, then calibrating the amplitude and phase of the obtained zero intermediate frequency signal; calculating adaptive weights by using an anti-interference processing algorithm on the calibrated signal; calculating all spectral peak maximum values by using a DOA estimation algorithm on the calibrated signal; setting a spatial domain search range, and calculating interference null power in the range according to the adaptive weights and the array flow type; setting an interference power threshold as a reference value, and comparing the interference null power and the interference power threshold at the spatial domain search range and the angle corresponding to each spectral peak maximum value; according to the comparison result, if the interference null power is less than the interference power threshold, then the spectral peak maximum value is a determined maximum value point, and the corresponding angle is the incident direction of the interference signal; if not, perform spectral peak zero processing to make the angle information invalid.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite navigation technology, and more particularly, to a DOA estimation technology implementation method and system based on a spatial nulling algorithm. BACKGROUND

[0002] In actual working environment, satellite navigation terminal equipment may be affected by various electromagnetic interferences existing in space, which will seriously affect the positioning accuracy, and even cause the equipment to be unable to position. In view of these interference effects, there are generally two methods in the prior art: one is to actively defend through anti-interference technology. For general narrowband interference, simple time domain filtering or frequency domain filtering can be used for suppression. For wideband interference, an array antenna is needed, and a spatial nulling algorithm or a beam pointing algorithm is used for suppression. The other method is to use DOA technology (direction of arrival estimation technology) based on an array antenna to identify the direction of interference, and after identifying the direction of interference, to avoid it or provide the direction information of the interference to other equipment to assist other equipment to remove the interference source through a physical way. At present, many satellite navigation terminal equipment are equipped with array antennas to ensure normal work in complex electromagnetic environment, and have single anti-interference function or DOA estimation function, or both functions.

[0003] The main shortcomings of the above-mentioned prior art are as follows: (1) the satellite navigation array antenna has both anti-interference function and DOA estimation function, but generally the two functions are independent of each other without information interaction, and various information in the anti-interference processing process is not fully utilized; (2) the existing DOA estimation technology based on satellite navigation array antenna has high accuracy requirements for the antenna array, but is limited by the number of array elements (satellite navigation array antenna generally uses 4-7 array elements), array mutual coupling, physical installation error, hardware channel error, antenna array element non-circularity and other problems. When a strong interference signal is incident, many false peaks (false peaks) will be caused in the DOA estimation, so that the direction of the interference signal cannot be accurately estimated.

[0004] Therefore, in view of the shortcomings of the prior art, the present application adopts a new array antenna digital signal processing architecture and DOA estimation technology method to jointly process the weight information with amplitude and phase information output by the anti-interference function and the original information output by the DOA function, so as to effectively improve the accuracy of the DOA estimation angle. SUMMARY

[0005] The present application aims to overcome at least one of the above-mentioned defects of the prior art, and provides a DOA estimation technology implementation method and system based on a spatial nulling algorithm, which further improves the accuracy of DOA estimation angle by using anti-interference output weight.

[0006] The technical scheme adopted by the present application is a DOA estimation technology implementation method based on a spatial nulling algorithm, which comprises the following steps:

[0007] S1: acquiring an array flow type and performing ADC sampling to obtain a digital intermediate frequency signal, then performing down-conversion of the digital intermediate frequency signal to a zero intermediate frequency signal, and then performing amplitude and phase calibration on the zero intermediate frequency signal;

[0008] S2: processing the calibrated signal by using an anti-interference algorithm to obtain a converged adaptive weight;

[0009] S3: calculating all spectral peak maximum values of the calibrated signal by using a DOA estimation algorithm;

[0010] S4: setting a spatial search range, and calculating an interference nulling power in the range according to the adaptive weight and the array flow type;

[0011] S5: setting an interference power threshold as a reference value, and comparing the interference nulling power and the interference power threshold at the spatial search range and the angle corresponding to each spectral peak maximum value;

[0012] S6: according to the comparison result, if the interference nulling power is less than the interference power threshold, then the spectral peak maximum value is a determined maximum value point, and the corresponding angle is the incident direction of the interference signal; if not, the spectral peak is set to zero to make the angle information invalid.

[0013] In the present application, by integrating anti-interference and DOA estimation, the weight information calculated by using anti-interference is used to make a secondary judgment on the basis of each maximum value obtained by DOA search spectrum peak, to determine whether the interference nulling power at the interference angle corresponding to the maximum value is lower than the preset interference power threshold, as a basis for judging whether the maximum value is valid, so as to assist the DOA function to achieve more accurate incoming direction estimation, and without changing the original anti-interference and DOA algorithm implementation architecture, only using the existing information can complete more accurate incoming direction estimation, so that the actual resource consumption is less.

[0014] Preferably, in the step S4, the calculation formula of the interference nulling power is:

[0015] P_jam=20*log10(w′*A)

[0016] Wherein, P jam represents interference null power, and the result is in decibel value; w' represents the conjugate transpose of adaptive weight; A represents array flow pattern.

[0017] By calculating the interference null power by using the weight, and then comparing it with the interference power threshold, it can be determined whether the current spectrum peak maximum value is a determined maximum point, and accurate spectrum peak angle information can be obtained.

[0018] Preferably, in the step S2, an adaptive nulling algorithm is further adopted to complete the interference suppression processing of the input signal, and then the processed intermediate frequency signal is converted to digital intermediate frequency, and the noise signal without interference is output after DAC digital analog conversion.

[0019] In the present application, the interference suppression processing of the input signal is completed by using the adaptive nulling algorithm, active defense is realized, interference signals from the outside or the inside are suppressed or eliminated, the input signal is filtered, adjusted or optimized, the influence of the interference sources on the signal is reduced, and thus the signal noise ratio and the signal quality are improved.

[0020] Preferably, the adaptive nulling algorithm can adopt a pure spatial architecture or a space-time architecture or a space-frequency architecture.

[0021] Further preferably, the adaptive nulling algorithm can adopt a space-time anti-interference architecture based on the LMS algorithm, and the process includes:

[0022] S21: receiving the calibrated input signal X1-X M The time domain delay taps are performed on each signal.

[0023] S22: calculating space-time filtering, and then subtracting the calculated space-time filtering from the reference signal to obtain an estimation error, wherein the reference signal is a selected tap signal.

[0024] S23: calculating the adaptive weight according to the estimation error.

[0025] Further preferably, in the step S22, the calculation formula of the space-time filtering is:

[0026]

[0027] Wherein, y(n) represents space-time filtering; n represents the n th sample number or time; i represents the i th channel, and the number is 1-M; j represents the j th tap, and the number is 1-N; The weighted vector is represented as w; u ij The input signal vector is represented as x; and the superscript H represents the conjugate transpose.

[0028] Further preferably, in the step S23, the calculation formula of the adaptive weight is:

[0029]

[0030] wherein, w represents an adaptive weight value; e(n) is an estimation error, and the superscript * represents a complex conjugate; μ represents a step factor, and is a parameter that can be adaptively adjusted according to real-time data size or error size.

[0031] Preferably, the step S3 comprises:

[0032] S31: receiving the calibrated input signals X1-XN; M , and calculating a spatial covariance matrix R therefrom;

[0033] S32: performing eigenvalue decomposition on the covariance matrix R, judging the number of signal sources according to eigenvalues of R, determining a signal subspace and a noise subspace;

[0034] S33: performing spectral peak searching using the signal subspace and the noise subspace, searching for maximum points to obtain all spectral peak maximum values.

[0035] In the present application, the DOA estimation algorithm is used to complete accurate estimation of the directions of arrival of interference signals, and the direction angle values of each interference are outputted, so that the interference directions can be identified for avoidance, or interference direction information can be provided for other devices to assist other devices in removing interference sources through physical means.

[0036] Preferably, in the step S6, the spectral peak zeroing processing comprises adjusting the amplitude-phase weighting of each channel of the antenna array input using the adaptive weight value, so as to perform spectral peak zeroing in the spatial domain for the interference direction.

[0037] In the present application, only the spectral peaks with accurate angle information are retained by performing zeroing processing on the spectral peaks that do not meet the requirements, so that the estimation of the directions of arrival is more accurate.

[0038] On the other hand, the present application also provides a DOA estimation system based on a spatial domain zeroing algorithm, the system comprising:

[0039] a pre-processing module: performing sampling, frequency conversion and amplitude-phase calibration processing on the obtained array stream type;

[0040] an anti-interference processing module: obtaining an adaptive weight value, adjusting the amplitude-phase weighting of each channel of the antenna array input using the adaptive weight value, and performing zeroing for the interference direction;

[0041] a DOA estimation module: performing eigenvalue decomposition on the covariance matrix of the input signal, searching for maximum points of the spatial spectrum, and obtaining spectral peaks of the interference signal direction;

[0042] Joint processing module: using the adaptive weight value obtained by the anti-interference processing module to calculate the interference null power, and then comparing it with the interference power threshold, judging whether the spectral peak maximum value is a certain maximum point according to the comparison result, so as to obtain the real interference direction.

[0043] The system provides a joint processing architecture of anti-interference function and DOA estimation function, uses the weight value information obtained by the anti-interference solution to assist the DOA function to realize more accurate incoming wave direction estimation; when the platform simultaneously has the anti-interference function and the DOA function, the original implementation architecture of the anti-interference algorithm and the DOA algorithm does not need to be changed, only the existing information is used to increase the spatial angle search and logical judgment of the joint processing part, so that more accurate angle estimation can be obtained, the actual resource consumption is reduced, and certain engineering realizability is achieved.

[0044] Preferably, in the joint processing module, the calculation formula of the interference null power is:

[0045] P_jam=20*log10(w'*A)

[0046] Wherein, P_jam represents the interference null power, and the result is in decibel value; w' represents the conjugate transpose of the weight value; A represents the array flow type.

[0047] In the joint processing module, on the basis of each maximum value obtained by the DOA estimation module through searching the spectral peak, secondary judgment is performed through the interference null power calculated according to the weight value information obtained by the anti-interference processing module, so that more accurate spectral peak angle information can be extracted, and the accuracy of DOA estimation is improved.

[0048] Compared with the prior art, the beneficial effects of the present application are:

[0049] (1) a joint processing architecture integrating anti-interference and DOA estimation is proposed, the anti-interference output weight value and the DOA spectral peak are output to the joint processing module, and the anti-interference information is used to assist the DOA algorithm to complete more accurate angle estimation;

[0050] (2) in the joint processing module, the anti-interference output weight value and the DOA estimation spectral peak are jointly processed in the spatial domain, on the basis of each maximum value obtained by the DOA searching spectral peak, secondary judgment is added, whether the interference null power value under the interference angle corresponding to the maximum value is lower than the preset interference power threshold is judged as the basis for whether the maximum value is valid, so that invalid information is eliminated, and more accurate DOA estimation angle information is obtained. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 The method flowchart provided by the present application is shown.

[0052] Figure 2 This is a processing block diagram of the space-time anti-interference algorithm provided by the present invention.

[0053] Figure 3 This is a flowchart of the DOA estimation function processing provided by the present invention.

[0054] Figure 4 This is a schematic diagram of the joint processing algorithm provided by the present invention.

[0055] Figure 5 This is the anti-interference null map provided by the present invention.

[0056] Figure 6 This is a schematic diagram of the original spectrum peak estimated by DOA provided by the present invention.

[0057] Figure 7 This is a schematic diagram of the original spectrum peak including angle identification after DOA estimation provided by the present invention.

[0058] Figure 8 This is a schematic diagram of the spectrum peak after the joint processing of anti-interference and DOA estimation provided by the present invention.

[0059] Figure 9 This is a schematic diagram of the system structure provided by the present invention. DETAILED DESCRIPTION

[0060] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting the present invention. To better illustrate the following embodiments, some components in the accompanying drawings may be omitted, enlarged, or reduced in size, and do not represent actual product dimensions. Those skilled in the art will appreciate that some well-known structures and their descriptions may be omitted from the accompanying drawings.

[0061] Example 1

[0062] like Figure 1 As shown, this embodiment provides a method for implementing a DOA estimation technology based on a spatial nulling algorithm, the method comprising:

[0063] S1: Obtain the array flow pattern and perform ADC sampling on it to obtain a digital intermediate frequency signal, then down-convert the digital intermediate frequency signal to a zero intermediate frequency signal, and then perform amplitude and phase calibration on the zero intermediate frequency signal;

[0064] S2: Calculate the adaptive weights using the anti-interference processing algorithm on the calibrated signal;

[0065] S3: Calculate all spectral peak maxima of the calibrated signal using the DOA estimation algorithm;

[0066] S4: Set the spatial search range and calculate the interference null power within this range based on the adaptive weight and array flow pattern;

[0067] S5: setting an interference power threshold as a reference value, comparing the interference nulling power with the interference power threshold at the spatial search range and the angle corresponding to each spectral peak maximum value;

[0068] S6: according to the comparison result, if the interference nulling power is less than the interference power threshold, the spectral peak maximum value is a determined maximum value point, and the corresponding angle is the incident direction of the interference signal; if not, the spectral peak is set to zero, and the angle information is invalid.

[0069] The method provided by the embodiment is suitable for a satellite navigation array antenna, and is used for implementing a DOA estimation technology of a basic adaptive spatial nulling algorithm. The spatial nulling algorithm is not limited to pure spatial nulling, space-time nulling, and space-frequency nulling. The DOA estimation technology is not limited to a MUSIC algorithm and a Capon algorithm. The array antenna frequency point is not limited to Beidou, GPS, GLONASS, and the like. The array antenna configuration is not limited to a linear array, a square array, a circular array, a Y-shaped array, a planar array, a slant array, and a spherical array, and the like. The number of array antenna elements is not less than 4. Through integrated anti-interference and DOA estimation, weight information completed by anti-interference calculation is used. On the basis of each maximum value obtained by searching a spectral peak in DOA, a secondary judgment is added. Whether the interference nulling power value at the interference angle corresponding to the maximum value is lower than a preset interference power threshold is judged as a basis for whether the maximum value is valid. Therefore, the DOA function is assisted to achieve more accurate incoming wave direction estimation. Moreover, the original anti-interference and DOA algorithm implementation architecture does not need to be changed. More accurate incoming wave direction estimation can be completed only by using existing information. The amount of actually consumed resources is less.

[0070] Preferably, in the step S1, the obtained array stream type is first preprocessed, and sampling, frequency conversion, and amplitude and phase calibration processing are performed on the array stream type. Therefore, signal distortion is reduced, and signal quality is improved.

[0071] Preferably, in the step S2, an adaptive nulling algorithm is further used to complete interference suppression processing of the input signal. Then, the processed intermediate frequency signal is frequency-converted to a digital intermediate frequency. After DAC digital-to-analog conversion, a noise signal without interference is output.

[0072] For anti-interference processing, a typical adaptive nulling anti-interference algorithm such as a spatial anti-interference algorithm, a space-time anti-interference algorithm, and a space-frequency anti-interference algorithm can be used to complete interference suppression processing of the input signal, to achieve active defense, to suppress or eliminate interference signals from the outside or the inside, and to perform filtering, adjustment, or optimization processing on the input signal. Therefore, the influence of the interference sources on the signal is reduced, the signal-to-noise ratio of the signal is improved, and the signal quality is improved.

[0073] In the prior art, the space-time anti-interference algorithm is commonly used. Its algorithm processing block diagram is as follows:Figure 2 As shown from Figure 2 It can be seen that X1~X M M signals output by the amplitude and phase calibration module, {w mn} is a space-time weight vector, where n=1, 2, 3...N is the filter order, the number is 1~N; m=1, 2, 3...M is the number of array elements, the number is 1~M. The data delay of each beat is T, T<1 / B (B is the signal bandwidth), and the total delay length of each array element signal is (N-1)T.

[0074] Space-time weight vector: (MN×1 dimension).

[0075] Where w i =[w i (0), w i (0),..., w i (N-1)] T Under the constraint condition, according to the linear constraint minimum variance (LCMV) criterion, the optimization problem about W can be described as:

[0076]

[0077] s.t.W H S=1

[0078] Where S is a two-dimensional space-time steering vector, R=E[XX H ] is the covariance matrix of the input signal, and is MN×MN dimension. The optimal solution of space-time processing can be obtained by using the Lagrange multiplier method:

[0079]

[0080] The optimal solution of the above formula weight needs to be inversed, and the calculation amount is huge. In order to solve the above problem, the space-time anti-interference algorithm based on LMS is taken as an example, the matrix inversion is converted into the iterative calculation to reduce the calculation complexity, and the process includes:

[0081] Step S21: receiving the calibrated input signals X1~X M , each signal is time domain delay tap;

[0082] Step S22: calculating space-time filtering, and then subtracting the calculated space-time filtering from the reference signal to obtain the estimation error, wherein the reference signal is a selected tap signal;

[0083] Further preferably, the calculation formula of the space-time filtering is:

[0084]

[0085] Wherein, y(n) represents space-time filtering; n represents the number of the n-th sample or time; i represents the i-th channel, the number is 1~M; j represents the j-th tap, the number is 1~N; represents a weight vector; u ij represents an input signal vector; the superscript H represents conjugate transpose.

[0086] The estimation error calculation formula is:

[0087] e(n) = d(n) - y(n)

[0088] Wherein, e(n) represents an estimation error, and d(n) represents a reference signal.

[0089] Step S23: adaptive weight is calculated according to the estimation error.

[0090] Further preferably, the calculation formula of the adaptive weight is:

[0091]

[0092] Wherein, w represents an adaptive weight; e(n) is an estimation error, and the superscript * represents complex conjugate; μ represents a step factor, which is a parameter that can be adaptively adjusted according to the real-time data size or error size.

[0093] The space domain null interference algorithm mentioned in the embodiment is not limited to space-time, space-frequency or pure space domain, and is not limited to various space domain null interference algorithms realized based on LMS algorithm or other algorithms.

[0094] Preferably, in the step S3, a DOA estimation algorithm such as MUSIC algorithm or Capon algorithm can be used for DOA estimation, and the DOA estimation function processing flow based on the MUSIC algorithm in the embodiment is as shown in Figure 3 , including:

[0095] Step S31: receiving calibrated input signals X1~X M , and calculating a spatial covariance matrix R;

[0096] Assuming that the mathematical model of a narrowband far-field signal is:

[0097] X(t) = A(θ) s(t) + N(t)

[0098] X(t) is an M*1 dimensional snapshot data vector of the array, N(t) is an M*1 dimensional noise data vector of the array, s(t) is an N*1 dimensional vector of a spatial signal, and A is an M*N dimensional flow matrix (direction vector array) of a spatial array.

[0099] The array covariance matrix is:

[0100] R=E(XX H )=AE[SS H ]A H +σ 2 I=AR s A H +σ 2 I

[0101] R s is the signal covariance matrix, σ 2 is the ideal white noise power, I is the unit matrix, X represents the received signal, X H is the conjugate transpose of X, and S is the covariance matrix of the signal part, which is usually determined by the statistical characteristics of the received signal.

[0102] Since the signal and noise are independent of each other, the data covariance matrix R can be decomposed into two parts related to the signal and noise, AR s A H It is the signal part.

[0103] Step S32: performing eigendecomposition on the covariance matrix R, judging the number of signal sources based on the eigenvalues ​​of R, and determining the signal subspace and the noise subspace;

[0104] Performing eigendecomposition on R, we can get:

[0105]

[0106] In the above formula, U S is the subspace spanned by the eigenvectors corresponding to the large eigenvalues, i.e. the signal subspace; U N The subspace spanned by the eigenvectors corresponding to large eigenvalues ​​is the noise subspace; the signal subspace and the noise subspace satisfy:

[0107] Step S33: Based on the signal subspace and the noise subspace, the spectrum estimation formula of the MUSIC algorithm is used to search for spectrum peaks and find the maximum points, thereby obtaining all spectrum peak maxima. The angle corresponding to each maximum point is the incident direction of the signal.

[0108] Since the data length is limited, the maximum likelihood estimate of the data covariance matrix is:

[0109]

[0110] right Perform eigendecomposition to obtain the noise subspace eigenvector matrix Since the array response of the signal belongs to the signal subspace and is orthogonal to the noise subspace, the direction vector is perpendicular to the eigenvectors that constitute the noise subspace. By maximizing the spatial spectrum function, the spectrum estimation formula of the MUSIC algorithm can be obtained as follows:

[0111]

[0112] is the array flow pattern direction vector, and θ is the direction angle, is the pitch angle. For the direction angle θ, a two-dimensional search is performed, and the direction where the peak value is located is the estimated value of the incoming wave direction, so that the corresponding angle can be obtained.

[0113] Thus, in the embodiment, the accurate estimation of the incoming wave direction of the interference signal is completed by the DOA estimation algorithm, and the direction angle value of each interference is output, so that the interference direction can be identified and evaded, or the interference direction information is provided for other devices to assist other devices to remove the interference source by physical means.

[0114] And in order to obtain more accurate incoming wave direction estimation value, it is also necessary to determine how many signal sources are contained in the observation data, and in the embodiment, a signal source estimation algorithm is also used to estimate the number of signal sources, such as AIC criterion (Akaike information criterion), MDL criterion (minimum description length criterion), EDC criterion (effective detection criterion), etc., which can effectively estimate the number of signal sources, and further improve the accuracy of DOA estimation. These criteria have their own characteristics. For the AIC criterion, it is more suitable for the case that the signal model is relatively simple and the sample size is relatively small, and it can quickly give a preliminary estimation of the number of signal sources; while the MDL criterion is suitable for the case that the signal is complex and the noise is large, and it can balance the model fitting degree and complexity to avoid overfitting; the EDC criterion is very important in array signal processing, and it can effectively detect signal sources, especially in a noisy environment. It has good performance, and in actual application, a suitable criterion can be selected according to the specific signal environment, noise level and system complexity, or the number of signal sources can be estimated by combining multiple criteria to obtain more reliable results.

[0115] Preferably, in step S4 of the embodiment, the spatial search range is set as: the direction angle θ is taken as [0°, 359°], and the pitch angle is taken as [-90°, 90°];

[0116] The calculation formula of the interference null power is:

[0117] P_jam=20*log10(w′*A)

[0118] Wherein, P_jam represents the interference null power, and the result is in decibel value; w' represents the conjugate transpose of the adaptive weight; A represents the array flow pattern.

[0119] The current spectral peak maximum value is determined whether it is a determined maximum point by calculating the interference null power by using the weight value and comparing it with the interference power threshold, and the more accurate spectral peak searching is performed for the auxiliary DOA estimation, so that the more accurate spectral peak angle information is obtained.

[0120] Preferably, as shown in the step S5, the interference power threshold P_thr is set, which is an empirical value and positive. For the actual application of satellite navigation, the interference power affecting the normal operation of the general receiver is converted as the interference power threshold; the value is an empirical value and can also be set according to the specific application scenario. Then the interference null power P_jam obtained in the step S4 is compared with the set interference power threshold P_thr. Figure 4

[0121] Preferably, as shown in the step S6, if the following condition is met according to the comparison result in the step S5: P_jam<-P_thr, it is determined that the maximum value searched by the spectral peak is a determined maximum point, and the corresponding angle is the incident direction of the interference signal. If not, the spectral peak is zeroed and the angle information is invalid. Figure 4

[0122] For the spectral peak not meeting the above condition, the spectral peak zero processing is performed in the embodiment, including adjusting the amplitude and phase weighting of each channel of the antenna array input by using the adaptive weight, so as to perform the spectral peak zero processing in the spatial domain for the interference direction.

[0123] Thus, in the embodiment, only the spectral peak with accurate angle information is reserved by performing the zero processing on the spectral peak not meeting the requirement, so that the estimation of the wave direction is more accurate.

[0124] In order to verify the effectiveness of the joint processing algorithm, the matlab simulation is also used for verification in the embodiment, assuming that the array antenna uses a 7-element uniform circular array with an array spacing of 0.5 wavelength. The interference source is set to 6 directions, the interference uses a wideband interference modulated by Gaussian white noise, the interference bandwidth is 20MHz, the jamming-to-signal ratio is 70dB-110dB, and the interference angles are shown in Table 1:

[0125] Pitch angle 20 20 35 20 20 40 Azimuth angle 20 80 140 200 260 320

[0126] Table 1

[0127] The interference power threshold P_thr is set to 60, and the simulation result is shown in Figures 5-8 Figure 5 is the anti-interference null diagram, and it can be seen from Figure 5 that 6 nulls for the interference direction are formed after the anti-interference algorithm processing for the 6 interferences; Figure 6 and Figure 7 ​​​P0 is the original spectrum peak estimated by DOA, for easy observation, Figure 6 No angle mark, Figure 7 Angle mark, by Figure 6 And Figure 7 It can be seen that the original spectrum peak estimated by DOA has a large number of false peaks, and the true spectrum peak is submerged in various false peaks and cannot be accurately extracted. Figure 8 The spectrum peak after joint processing of anti-interference and DOA estimation can be clearly observed, and the angle information of the detected spectrum peak is accurate.

[0128] Embodiment 2

[0129] As Figure 9 shown, the embodiment provides a DOA estimation system based on a spatial nulling algorithm, which comprises:

[0130] The pre-processing module: sampling, frequency conversion and amplitude and phase calibration processing are performed on the obtained array stream type;

[0131] The anti-interference processing module: adaptive weights are obtained, and the adaptive weights are used to adjust the amplitude and phase weighting of the input channels of the antenna array, and the interference direction is zeroed;

[0132] The DOA estimation module: the eigenvalue decomposition is performed on the covariance matrix of the input signal, and the maximum value point of the spatial spectrum is searched to obtain the spectrum peak of the interference signal direction;

[0133] The joint processing module: the adaptive weights obtained by the anti-interference processing module are used to calculate the interference null power, which is then compared with the interference power threshold, and according to the comparison result, it is judged whether the spectrum peak maximum value is a certain maximum value point, so as to obtain the real interference direction.

[0134] Among them, in the anti-interference processing module and the DOA estimation module, the input is the amplitude and phase calibrated zero intermediate frequency signal, in the anti-interference module, the spatial anti-interference processing algorithm such as spatial, space-time, space-frequency is adopted, the key of this algorithm is to obtain the adaptive weight w, which has amplitude and phase information, which can adjust the amplitude and phase weighting of each channel of the input of the antenna array, so as to zero in the spatial domain for the interference direction. The DOA estimation utilizes the eigenvalue decomposition of the covariance matrix of the input signal, and the maximum value point of the spatial spectrum is searched to obtain the interference signal direction. According to the principle of the two kinds of function algorithms, the algorithms are all in the spatial domain, and the interference signal direction is processed. The anti-interference algorithm is zeroed in space for the interference direction, and the DOA estimation forms a spectrum peak in the interference direction.

[0135] Therefore, in the system, two algorithms are used for the same input and the processing method of the spatial signal, the two algorithms are jointly processed in the joint processing module, the spatial null formed by the anti-interference output weight is used to assist the DOA estimation to perform more accurate spectrum peak search, and the real interference direction is determined.

[0136] Therefore, the system provides a joint processing architecture of anti-interference function and DOA estimation function, uses the weight information completed by the anti-interference solution to assist the DOA function to realize more accurate incoming wave direction estimation; when the platform has the anti-interference function and the DOA function, the original implementation architecture of the anti-interference algorithm and the DOA algorithm does not need to be changed, only the existing information is used to increase the spatial angle search and logical judgment of the joint processing part, and more accurate angle estimation can be obtained, the amount of resources actually consumed is reduced, and certain engineering realizability is obtained.

[0137] Preferably, in the joint processing module, the calculation formula of the interference null power is:

[0138] P_jam=20*log10(w'*A)

[0139] Wherein, P_jam represents the interference null power, the result is in decibel value; w' represents the conjugate transpose of the adaptive weight; A represents the array flow type.

[0140] In the joint processing module, on the basis of each maximum value obtained by the DOA estimation module through searching the spectrum peak, the interference null power calculated according to the weight is used for secondary judgment, whether the interference null power under the interference angle corresponding to the maximum value is lower than the preset interference power threshold is judged as the basis whether the maximum value is valid, so that more accurate spectrum peak angle information can be extracted, and the accuracy of the DOA estimation is improved.

[0141] Obviously, the above embodiments of the present application are only examples for clearly illustrating the technical scheme of the present application, and are not intended to limit the specific implementation manner of the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for implementing DOA estimation technology based on spatial nulling algorithm, characterized in that: The method comprises: S1: Obtain the array flow pattern and perform ADC sampling on it to obtain a digital intermediate frequency signal, then down-convert the digital intermediate frequency signal to a zero intermediate frequency signal, and then perform amplitude and phase calibration on the zero intermediate frequency signal; S2: Process the calibrated signal using an adaptive zeroing algorithm to obtain converged adaptive weights; S3: Calculate all spectral peak maxima of the calibrated signal using the DOA estimation algorithm; S4: Set the spatial search range and calculate the interference null power within this range based on the adaptive weight and array flow pattern; S5: Setting an interference power threshold and using it as a reference value, comparing the interference null power with the interference power threshold within a spatial search range and at an angle corresponding to each spectrum peak maximum; S6: According to the comparison result, if the interference null power is less than the interference power threshold, the spectrum peak maximum is determined as the maximum point, and the corresponding angle is the incident direction of the interference signal; if not, the spectrum peak is set to zero, making the angle information invalid.

2. The method for implementing DOA estimation technology based on spatial nulling algorithm according to claim 1, characterized in that: In step S4, the interference nulling power is calculated as follows: in, It represents the interference null power, and the result is expressed in decibels; represents the conjugate transpose of the adaptive weight; Indicates the array flow pattern.

3. The method for implementing DOA estimation technology based on spatial nulling algorithm according to claim 1, characterized in that: The step S2 also includes: using an adaptive zeroing algorithm to complete interference suppression processing of the input signal, then converting the processed zero intermediate frequency signal to a digital intermediate frequency, and outputting an interference-free noise signal after DAC digital-to-analog conversion.

4. The method for implementing DOA estimation technology based on spatial nulling algorithm according to claim 3, characterized in that: The adaptive zeroing algorithm may adopt a pure spatial architecture, a space-time architecture, or a space-frequency architecture.

5. The method for implementing DOA estimation technology based on spatial nulling algorithm according to claim 4, characterized in that: The adaptive zeroing algorithm may adopt a space-time anti-interference architecture based on the LMS algorithm, and the process includes: S21: Receive the calibrated input signal X1~X M , perform time domain delay tapping on each signal; S22: Calculate the space-time filter, and then use the reference signal to subtract the calculated space-time filter to obtain an estimated error, where the reference signal is a selected tap signal; wherein the calculation formula of the space-time filter is: in, Indicates space-time filtering; n indicates the nth sample number or time; i indicates the i-th channel, the number is 1 to M; j indicates the j-th tap, the number is 1 to N; represents the weight vector; represents the input signal vector; superscript stands for conjugate transpose; S23: Calculate the adaptive weight according to the estimation error.

6. The method for implementing DOA estimation technology based on spatial nulling algorithm according to claim 5, characterized in that: In step S23, the calculation formula of the adaptive weight is: in, w represents the adaptive weight; e(n) is the estimation error, and the superscript * represents the complex conjugate; Represents the step size factor, which is a parameter that can be adaptively adjusted according to the size of real-time data or error.

7. The method for implementing DOA estimation technology based on spatial nulling algorithm according to claim 1, characterized in that: The step S3 includes: S31: Receive calibrated input signals X1~X M , calculate the spatial covariance matrix R; S32: Perform eigendecomposition on the covariance matrix R, determine the number of signal sources based on the eigenvalues ​​of R, and determine the signal subspace and noise subspace; S33: Spectral peak search is performed using the signal subspace and the noise subspace to search for the maximum value point, thereby obtaining all spectral peak maxima.

8. The method for implementing DOA estimation technology based on a spatial nulling algorithm according to any one of claims 1 to 7, wherein: In step S6, the spectrum peak nulling process includes adjusting the amplitude and phase weighting of each channel of the antenna array input using adaptive weights, thereby performing spectrum peak nulling in the spatial domain with respect to the interference direction.

9. A DOA estimation system based on a spatial nulling algorithm, characterized in that: The system comprises: Pre-processing module: performs sampling, frequency conversion, and amplitude and phase calibration on the acquired array flow patterns; Anti-interference processing module: obtains adaptive weights and uses them to adjust the amplitude and phase weighting of each channel of the antenna array input to zero the interference direction; DOA estimation module: It uses the covariance matrix of the input signal to perform eigenvalue decomposition and obtains the interference signal to form the spectrum peak by searching for the maximum point of the spatial spectrum; Joint processing module: uses the adaptive weights obtained by the anti-interference processing module to calculate the interference null power, then compares it with the interference power threshold. Based on the comparison result, it determines whether the spectrum peak maximum is the determined maximum point, thereby obtaining the true interference direction.

10. The DOA estimation system based on spatial nulling algorithm according to claim 9, characterized in that: In the joint processing module, the interference nulling power is calculated as follows: in, It represents the interference null power, and the result is expressed in decibels; represents the conjugate transpose of the adaptive weight; Indicates the array flow pattern.

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