DOA estimation technology implementation method and system based on airspace zeroing algorithm

Abstract

The invention discloses a DOA estimation technology implementation method and system based on an airspace zeroing algorithm, and the method comprises the steps: obtaining an array flow pattern, carrying out the preprocessing of the array flow pattern, and carrying out the amplitude and phase calibration of an obtained zero intermediate frequency signal; calculating the calibrated signal by adopting an anti-interference processing algorithm to obtain a self-adaptive weight; calculating maximum values of all spectrum peaks of the calibrated signal by using a DOA estimation algorithm; setting an airspace search range, and calculating interference null power within the range according to the adaptive weight and the array flow pattern; setting an interference power threshold and taking the interference power threshold as a reference value, and comparing the interference null power with the interference power threshold in an airspace search range and at an angle corresponding to each spectrum peak maximum value; according to the comparison result, if the interference null power is smaller than the interference power threshold, the spectrum peak maximum value is the determined maximum value point, and the corresponding angle is the incident direction of the interference signal; and if not, carrying out spectrum peak zero setting processing, so that the angle information is invalid.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation technology, and more specifically, to a method and system for implementing DOA estimation technology based on a spatial nulling algorithm. Background Art

[0002] In the actual working environment, satellite navigation terminal equipment may be affected by various electromagnetic interferences in space, which will seriously affect the positioning accuracy and even cause the equipment to be unable to locate. In view of these interference effects, there are generally two methods in the prior art: one is to use anti-interference technology and active defense. For general narrowband interference, it can be suppressed by simple time domain filtering or frequency domain filtering; for broadband interference, it is necessary to use array antennas and adopt spatial nulling algorithms or beam pointing algorithms to suppress it. Another method is based on array antennas, using DOA technology (direction of arrival estimation technology) to identify the direction of interference. After identifying the interference direction, it is avoided or provided to other devices with interference direction information to assist other devices to remove the interference source by physical means. At present, many satellite navigation terminal equipment are equipped with array antennas to ensure normal operation in complex electromagnetic environments, with a single anti-interference function or DOA estimation function, or both functions. The anti-interference function can handle various interferences in space, and the DOA estimation function can identify the direction of spatial interference and provide interference source direction information to the carrier equipped with array antennas.

[0003] The main shortcomings of the above-mentioned existing technologies are: (1) Satellite navigation array antennas have devices that have both anti-interference and DOA estimation functions, but the two functions are generally designed to be 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 antennas has high requirements on the accuracy of the antenna array, but is limited by the number of array elements (satellite navigation array antennas generally use 4 to 7 array elements), array mutual coupling, physical installation errors, hardware channel errors, antenna array non-circularity and other problems. When a strong interference signal is incident, it will cause the DOA estimation to have many pseudo peaks (false peaks), making it impossible to accurately estimate the direction of the interference signal.

[0004] Therefore, in view of the shortcomings of the prior art, the present invention adopts a new array antenna digital signal processing architecture and DOA estimation technical 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, thereby effectively improving the accuracy of the DOA estimation angle. Summary of the invention

[0005] The present invention aims to overcome at least one defect (shortcoming) of the above-mentioned prior art, and provides a method and system for implementing DOA estimation technology based on a spatial nulling algorithm, which further improves the accuracy of the DOA estimation angle by utilizing the output weights for anti-interference.

[0006] The technical solution adopted by the present invention is a method for implementing DOA estimation technology based on a spatial domain nulling algorithm, the method comprising:

[0007] S1: Obtain the array flow pattern, 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;

[0008] S2: Process the calibrated signal using an anti-interference algorithm to obtain the converged adaptive weights;

[0009] S3: Calculate all spectral peak maxima of the calibrated signal using the DOA estimation algorithm;

[0010] S4: Set the spatial search range, and calculate the interference null power within this range according to the adaptive weight and array flow type;

[0011] S5: setting an interference power threshold and taking it as a reference value, comparing the interference null power with the interference power threshold in the spatial search range and at the angle corresponding to each spectrum peak maximum value;

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

[0013] In the present application, by integrating anti-interference and DOA estimation, and utilizing the weight information obtained through anti-interference solution, a secondary judgment is added based on each maximum value obtained by the DOA search spectrum peak to determine whether the interference null power value is lower than the preset interference power threshold at the interference angle corresponding to the maximum value, which serves as the basis for whether the maximum value is valid, thereby assisting the DOA function to achieve more accurate estimation of the direction of arrival of waves. In addition, there is no need to change the original anti-interference and DOA algorithm implementation architecture, and more accurate estimation of the direction of arrival of waves can be achieved by using only the existing information, thereby reducing the actual amount of resources consumed.

[0014] Preferably, in step S4, the interference nulling power is calculated as follows:

[0015] P_jam=20*log10(w′*A)

[0016] Wherein, P_jam represents the interference nulling power, and the result is expressed in decibels; w′ represents the conjugate transpose of the adaptive weight; and A represents the array flow type.

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

[0018] Preferably, 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.

[0019] In the present application, an adaptive zeroing algorithm is used to complete interference suppression processing of the input signal, realize active defense, suppress or eliminate interference signals from the outside or inside, filter, adjust or optimize the input signal, reduce the impact of these interference sources on the signal, thereby improving the signal-to-noise ratio of the signal and improving the signal quality.

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

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

[0022] S21: Receive the calibrated input signal X1~X M , perform time domain delay tapping on each signal;

[0023] S22: Calculate the space-time filtering, and then use the reference signal to subtract the calculated space-time filtering to obtain an estimated error, wherein the reference signal is a selected tap signal of a certain channel;

[0024] S23: Calculate 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 nth sample number or time; i represents the i-th channel, the number is 1 to M; j represents the j-th tap, the number is 1 to N; represents the weight vector; u ij Represents the input signal vector; the superscript H represents the conjugate transpose.

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

[0029]

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

[0031] Preferably, step S3 includes:

[0032] S31: Receive calibrated input signals X1~X M , and obtain the spatial covariance matrix R;

[0033] S32: Perform eigendecomposition on the covariance matrix R, determine the number of signal sources according to the eigenvalues ​​of R, and determine the signal subspace and the noise subspace;

[0034] S33: Use the signal subspace and the noise subspace to search for spectrum peaks, search for maximum value points, and thus obtain all spectrum peak maxima.

[0035] In this application, the DOA estimation algorithm is used to accurately estimate the direction of the interference signal and output the directional angle value of each interference, so that the interference direction can be avoided according to the identified interference direction, or the interference direction information can be provided to other devices to assist other devices in removing the interference source by physical means.

[0036] Preferably, in step S6, the spectrum peak nulling process includes adjusting the amplitude and phase weighting of each channel of the antenna array input by using adaptive weights, so as to perform spectrum peak nulling in the spatial domain with respect to interference.

[0037] In the present application, by setting the spectrum peaks that do not meet the requirements to zero, only the spectrum peaks with accurate angle information are retained, so that the estimation of the incoming wave direction is more accurate.

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

[0039] Pre-processing module: sampling, frequency conversion and amplitude and phase calibration of the acquired array flow pattern;

[0040] Anti-interference processing module: obtain adaptive weights, use the adaptive weights to adjust the amplitude and phase weights of each channel of the antenna array input, and set the interference direction to zero;

[0041] DOA estimation module: Use the covariance matrix of the input signal to perform eigenvalue decomposition, and obtain the interference signal to form a spectrum peak by searching for the maximum point of the spatial spectrum;

[0042] 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. Based on the comparison result, it is determined whether the spectrum peak maximum is the determined maximum point, thereby obtaining the real interference direction.

[0043] This system provides a joint processing architecture for anti-interference and DOA estimation functions, and uses the weight information obtained from the anti-interference solution to assist the DOA function in achieving more accurate estimation of the direction of incoming waves. When the platform has both anti-interference and DOA functions, there is no need to change the original implementation architecture of the anti-interference algorithm and the DOA algorithm. Only by using the existing information to increase the spatial angle search and logical judgment of the joint processing part can a more accurate angle estimation be obtained, thereby reducing the actual amount of resources consumed and having a certain degree of engineering feasibility.

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

[0045] P_jam=20*log10(w′*A)

[0046] Among them, P_jam represents the interference null power, and the result is in decibels; w′ represents the conjugate transpose of the weight; A represents the array flow type.

[0047] In the joint processing module, based on each maximum value obtained by searching the spectrum peak in the DOA estimation module, a secondary decision is made by calculating the interference null power according to the weight information obtained by the anti-interference processing module, so that more accurate spectrum peak angle information can be extracted and the accuracy of DOA estimation can be improved.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] (1) A joint processing architecture integrating anti-interference and DOA estimation is proposed. The anti-interference output weights and DOA spectrum peaks are output to the joint processing module, and the anti-interference information is used to assist the DOA algorithm to achieve more accurate angle estimation.

[0050] (2) In the joint processing module, the anti-interference output weights and the DOA estimation spectrum peak are jointly processed in the spatial domain. Based on each maximum value obtained by the DOA search spectrum peak, a secondary judgment is added to determine whether the interference null power value is lower than the preset interference power threshold at the interference angle corresponding to the maximum value, which serves as the basis for whether the maximum value is valid, thereby eliminating invalid information and obtaining more accurate DOA estimation angle information. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention provides a flow chart of the method.

[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 flow chart 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 Schematic diagram of the original spectrum peak estimated by DOA provided by the present invention.

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

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

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

[0060] The drawings of the present invention are only for illustrative purposes and should not be construed as limiting the present invention. In order to better illustrate the following embodiments, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; it is understandable to those skilled in the art that some well-known structures and their descriptions in the drawings may be omitted.

[0061] Example 1

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

[0063] S1: Obtain the array flow pattern, 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 according to the adaptive weight and array flow type;

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

[0068] S6: According to the comparison result, if the interference null power is less than the interference power threshold, the spectrum peak maximum is the determined maximum point, and the corresponding angle is the incident direction of the interference signal; if not, the spectrum peak is set to zero to make the angle information invalid.

[0069] The method provided in this embodiment is applicable to satellite navigation array antennas, and is used to implement the DOA estimation technology of the 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 the MUSIC algorithm, the Capon algorithm, etc., and the frequency of the array antenna is not limited to Beidou, GPS, GLONASS, etc. The array antenna configuration is not limited to linear array, square array, circular array, Y-array, plane array, inclined array, and spherical array, etc. The number of array antenna elements is not less than 4. By integrating anti-interference and DOA estimation, using the weight information completed by anti-interference solution, on the basis of each maximum value obtained by the DOA search spectrum peak, a secondary judgment is added to judge whether the interference nulling power value is lower than the preset interference power threshold under the interference angle corresponding to the maximum value, as a basis for whether the maximum value is valid, thereby assisting the DOA function to achieve more accurate wave direction estimation, and there is no need to change the original anti-interference and DOA algorithm implementation architecture, and only using the existing information can complete a more accurate wave direction estimation, so that the actual amount of resources consumed is less.

[0070] Preferably, in step S1, the acquired array flow pattern is first preprocessed by sampling, frequency conversion and amplitude and phase calibration, so as to reduce signal distortion and improve signal quality.

[0071] Preferably, 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.

[0072] For anti-interference processing, typical adaptive zeroing anti-interference algorithms such as spatial domain anti-interference algorithm, space-time anti-interference algorithm, and space-frequency anti-interference algorithm can be used to complete the interference suppression processing of the input signal, realize active defense, suppress or eliminate interference signals from outside or inside, filter, adjust or optimize the input signal, reduce the impact of these interference sources on the signal, thereby improving the signal-to-noise ratio of the signal and improving the signal quality.

[0073] In the prior art, the application of space-time anti-interference algorithm is relatively common, and its algorithm processing block diagram is as follows: Figure 2 As shown, from Figure 2 It can be seen that X1~X M is the M-channel signal output by the amplitude and phase calibration module, {w mn} is the space-time weight vector, where n = 1, 2, 3...N is the filter order, 1 to N; m = 1, 2, 3...M is the number of array elements, 1 to M. The delay of each data shot 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] Among them, w i =[w i (0),w i (0),...,w i (N-1)] T , under the constraints, according to the linear constrained minimum variance (LCMV) criterion, the optimization problem about W can be described as:

[0076]

[0077] wxya H S=1

[0078] Where S is the space-time two-dimensional steering vector, R = E[XX H ] is the covariance matrix of the input signal, which is MN×MN dimension. Using the Lagrange multiplier method, the optimal solution for space-time processing is:

[0079]

[0080] The optimal solution of the weights in the above formula requires matrix inversion, which requires huge calculations. In order to solve the above problems, this solution takes the LMS-based space-time anti-interference algorithm as an example, and transforms the matrix inversion into an iterative calculation method to reduce the computational complexity. The process includes:

[0081] Step S21: receiving calibrated input signals X1-X M , perform time domain delay tapping on each signal;

[0082] Step S22: Calculate the space-time filter, and then use the reference signal to subtract the calculated space-time filter to obtain an estimated 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 nth sample number or time; i represents the i-th channel, the number is 1 to M; j represents the j-th tap, the number is 1 to N; represents the weight vector; u ij Represents the input signal vector; the superscript H represents the conjugate transpose.

[0086] The estimation error calculation formula is:

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

[0088] Where e(n) represents the estimation error and d(n) represents the reference signal.

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

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

[0091]

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

[0093] The spatial nulling anti-interference algorithm mentioned in this embodiment is not limited to space-time, space-frequency or pure space, and is not limited to various spatial nulling anti-interference algorithms implemented based on the LMS algorithm or other algorithms.

[0094] Preferably, in step S3, a DOA estimation algorithm such as a MUSIC algorithm or a Capon algorithm may be used to perform DOA estimation. In this embodiment, the DOA estimation function processing flow based on the MUSIC algorithm is as follows: Figure 3 As shown, including:

[0095] Step S31: receiving calibrated input signals X1-X M , and obtain the spatial covariance matrix R;

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

[0097] X(t)=A(θ)s(t)+N(t)

[0098] X(t) is the M×1-dimensional snapshot data vector of the array, N(t) is the M×1-dimensional noise data vector of the array, s(t) is the N×1-dimensional vector of the spatial signal, and A is the M×N-dimensional manifold matrix (steering vector matrix) of the 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, 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 according to the eigenvalues ​​of R, and determining the signal subspace and the noise subspace;

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

[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: According to the signal subspace and the noise subspace, the spectrum estimation formula of the MUSIC algorithm is used to search for spectrum peaks, and the maximum value points are searched to obtain all spectrum peak maxima. The angle corresponding to each maximum value 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 Performing eigendecomposition, we can 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 characteristic vector that constitutes 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 direction vector, θ is the direction angle, is the pitch angle. For the direction angle θ, Perform a two-dimensional search, and the direction of the peak is the estimated value of the incoming wave direction, and its corresponding angle can be obtained.

[0113] Therefore, in this embodiment, the DOA estimation algorithm is used to accurately estimate the direction of the interference signal, and the directional angle value of each interference is output, so that avoidance can be performed based on the identified interference direction, or interference direction information can be provided to other devices to assist other devices in physically removing the interference source.

[0114] In order to obtain a more accurate estimate of the direction of arrival, it is also necessary to determine how many signal sources are contained in the observed data. In this embodiment, a source estimation algorithm is also used to estimate the number of sources, such as the AIC criterion (Akaike information theory criterion), the MDL criterion (minimum description length criterion), the EDC criterion (effective detection criterion), etc., which can effectively estimate the number of sources and thus improve the accuracy of DOA estimation. These criteria have their own characteristics. The AIC criterion is more suitable for situations where the signal model is relatively simple and the sample size is small, and can quickly give a preliminary estimate of the number of sources; the MDL criterion is suitable for situations where the signal is complex and the noise is large, and can balance the model fit and complexity to avoid overfitting; the EDC criterion is very important in array signal processing, and can effectively detect the signal source, especially in a noisy environment. It has good performance. In practical applications, it is usually possible to select a suitable criterion according to the specific signal environment, noise level and system complexity, or to estimate the number of sources by combining multiple criteria to obtain a more reliable result.

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

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

[0117] P_jam=20*log10(w′*A)

[0118] Wherein, P_jam represents the interference nulling power, and the result is expressed in decibels; w′ represents the conjugate transpose of the adaptive weight; and A represents the array flow type.

[0119] By using the weights to calculate the interference null power and then comparing it with the interference power threshold, it can be determined whether the current spectrum peak maximum is a certain maximum point, assisting DOA estimation to perform more accurate spectrum peak search, thereby obtaining more accurate spectrum peak angle information.

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

[0121] Preferably, in step S6, Figure 4 As shown, if the comparison result in step S5 satisfies the following condition: P_jam<-P_thr, the maximum value found by the spectrum peak search can be determined as the determined maximum value point, and the corresponding angle is the incident direction of the interference signal. If not satisfied, the spectrum peak is set to zero and the angle information is invalid.

[0122] For spectrum peaks that do not meet the above conditions, spectrum peak zeroing processing is performed on them in this embodiment, including using adaptive weights to adjust the amplitude and phase weighting of each channel of the antenna array input, so as to zero the spectrum peak in the spatial domain for interference.

[0123] Therefore, in this embodiment, by performing zeroing processing on the spectrum peaks that do not meet the requirements, only the spectrum peaks with accurate angle information are retained, so that the estimation of the incoming wave direction is more accurate.

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

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

[0126] Table 1

[0127] Set the interference power threshold P_thr to 60, and the simulation results are as follows Figure 5-8 As shown, Figure 5 For the anti-interference null map, Figure 5 It can be seen that for the 6 interferences, after being processed by the anti-interference algorithm, 6 null sinks are formed for the interference direction; Figure 6 and Figure 7is the original spectrum peak estimated by DOA. For the convenience of comparison and observation, Figure 6 No angle mark, Figure 7 There are angle marks, Figure 6 and Figure 7 It can be seen that there are a large number of pseudo peaks in the original spectrum peak estimated by DOA, and the real spectrum peak is submerged in various pseudo peaks and cannot be accurately extracted. Figure 8 For the spectrum peaks after joint processing of anti-interference and DOA estimation, six spectrum peaks can be clearly observed, and the angle information of the detected spectrum peaks is accurate.

[0128] Example 2

[0129] like Fig. 9 As shown, this embodiment provides a DOA estimation system based on a spatial nulling algorithm, the system comprising:

[0130] Pre-processing module: sampling, frequency conversion and amplitude and phase calibration of the acquired array flow pattern;

[0131] Anti-interference processing module: obtain adaptive weights, use the adaptive weights to adjust the amplitude and phase weights of each channel of the antenna array input, and set the interference direction to zero;

[0132] DOA estimation module: Use the covariance matrix of the input signal to perform eigenvalue decomposition, and obtain the interference signal to form a spectrum peak by searching for the maximum point of the spatial spectrum;

[0133] 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. Based on the comparison result, it is determined whether the spectrum peak maximum is the determined maximum point, thereby obtaining the real interference direction.

[0134] Among them, in the anti-interference processing module and the DOA estimation module, the inputs are all zero intermediate frequency signals that have been calibrated with amplitude and phase. In the anti-interference module, a spatial domain anti-interference processing algorithm is used, such as spatial domain, space-time, space-frequency, etc. The key to this algorithm is to obtain an adaptive weight w, which has amplitude and phase information. The amplitude and phase weighting of each channel of the antenna array input can be adjusted by w, so as to zero the interference direction in the spatial domain. For DOA estimation, the covariance matrix of the input signal is used for eigenvalue decomposition, and the interference signal direction is obtained by searching for the maximum point of the spatial spectrum. It can be seen from the principles of the two functional algorithms that the algorithms are in the spatial domain, processing the incident interference signal direction, the anti-interference algorithm performs spatial zeroing on the interference direction, and the DOA estimation forms a spectrum peak in the interference direction.

[0135] Therefore, in this system, two algorithms are used for the same input and processing methods for spatial signals. The two algorithms are jointly processed in a joint processing module, and the spatial null formed by the anti-interference output weights is used to assist DOA estimation in performing more accurate spectral peak search and determining the true interference direction.

[0136] Therefore, this system provides a joint processing architecture for anti-interference and DOA estimation functions, and uses the weight information obtained through anti-interference solution to assist the DOA function in achieving more accurate estimation of the direction of incoming waves. When the platform has both anti-interference and DOA functions, there is no need to change the original implementation architecture of the anti-interference algorithm and the DOA algorithm. Only by using the existing information to increase the spatial angle search and logical judgment of the joint processing part can a more accurate angle estimation be obtained, thereby reducing the actual amount of resources consumed and having a certain degree of engineering feasibility.

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

[0138] P_jam=20*log10(w′*A)

[0139] Wherein, P_jam represents the interference nulling power, and the result is expressed in decibels; w′ represents the conjugate transpose of the adaptive weight; and A represents the array flow type.

[0140] In the joint processing module, based on each maximum value obtained by searching the spectrum peak in the DOA estimation module, a secondary judgment is made by calculating the interference null-notch power according to the weights to determine whether the interference null-notch power is lower than the preset interference power threshold at the interference angle corresponding to the maximum value, which serves as the basis for whether the maximum value is valid. This allows more accurate spectrum peak angle information to be extracted, thereby improving the accuracy of DOA estimation.

[0141] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation methods of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the claims of the present invention shall be included in the protection scope of the claims of the present invention.

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, 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 anti-interference algorithm to obtain the 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 according to the adaptive weight and array flow type; S5: setting an interference power threshold and taking it as a reference value, comparing the interference null power with the interference power threshold in the spatial search range and at the angle corresponding to each spectrum peak maximum value; S6: According to the comparison result, if the interference null power is less than the interference power threshold, the spectrum peak maximum is the determined maximum point, and the corresponding angle is the incident direction of the interference signal; if not, the spectrum peak is set to zero to make 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: P_jam=20*log10(w′*A) Wherein, P_jam represents the interference nulling power, and the result is expressed in decibels; w′ represents the conjugate transpose of the adaptive weight; and A represents the array flow type.

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 or 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 filtering, and then use the reference signal to subtract the calculated space-time filtering to obtain the estimated error, wherein the reference signal is a selected tap signal of a certain channel; wherein the calculation formula of the space-time filtering is: Wherein, y(n) represents space-time filtering; n represents the nth sample number or time; i represents the i-th channel, the number is 1 to M; j represents the j-th tap, the number is 1 to N; represents the weight vector; u ij represents the input signal vector; the superscript H represents the 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: Wherein, w represents the adaptive weight; e(n) is the estimated error, the superscript * represents the complex conjugate; μ represents the step size factor, which is a parameter that can be adaptively adjusted according to the size of real-time data or the size of 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 , and obtain the spatial covariance matrix R; S32: Perform eigendecomposition on the covariance matrix R, determine the number of signal sources according to the eigenvalues ​​of R, and determine the signal subspace and the noise subspace; S33: Use the signal subspace and the noise subspace to search for spectrum peaks, search for maximum value points, and thus obtain all spectrum peak maxima.

8. A method for implementing DOA estimation technology based on a spatial nulling algorithm according to any one of claims 1 to 7, characterized in that: In step S6, the spectrum peak nulling process includes adjusting the amplitude and phase weighting of each channel of the antenna array input by using adaptive weights, so as to perform 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: sampling, frequency conversion and amplitude and phase calibration of the acquired array flow pattern; Anti-interference processing module: obtain adaptive weights, use the adaptive weights to adjust the amplitude and phase weighting of each channel of the antenna array input, and set the interference direction to zero; DOA estimation module: Use the covariance matrix of the input signal to perform eigenvalue decomposition, and obtain the interference signal to form a spectrum peak by searching for the maximum point of the spatial spectrum; 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. Based on the comparison result, it is determined whether the spectrum peak maximum is the determined maximum point, thereby obtaining the real interference direction.

10. A 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: P_jam=20*log10(w′*A) Wherein, P_jam represents the interference nulling power, and the result is expressed in decibels; w′ represents the conjugate transpose of the adaptive weight; and A represents the array flow type.

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