A multi-target direct positioning ghost identification method based on frequency coloring technology

Abstract

The present application discloses a method for direct positioning of multiple targets and ghost identification based on frequency coloring technology. Through this method, considering the differences in the spectral structures of different targets, the energy information and frequency information of the received signal can be used simultaneously. By designing a frequency coloring strategy, each sub-band is recolored, and the mapped color space spectrum is used to distinguish between real targets and ghosts. Energy information is used to confirm the possible location of the target, and frequency information is used for data association. Without reducing the positioning accuracy, the system's utilization of signal information is improved, and effective discrimination of targets with different frequency components, ghosts, and weak targets near strong interference is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of array signal processing, and in particular to a multi-target direct positioning ghost identification method based on frequency coloring technology. Background Art

[0002] In the passive positioning of targets in distributed arrays, the existing ghost removal methods mainly include enumeration methods, clustering methods and cyclic MUSIC methods. The traditional enumeration method lists all matching possibilities and compares them, and finally selects the ones with the highest matching degree as targets. It has the advantages of high association accuracy and low algorithm complexity. However, the computational complexity of this type of method is directly related to the number of nodes and the number of targets. Therefore, this type of method is more suitable for application scenarios with a small number of arrays and targets. For application scenarios with a large number of observation nodes and targets, it is generally considered to use LOB (line-of-bearing) intersection clustering to estimate the target position. Among them, using the K-means algorithm to process LOB measurements with noise is a typical clustering method, but this type of method often requires the number of targets as prior information. The cyclic MUSIC method separates LOB measurements according to the unique transmission model characteristics of the received signal, thereby alleviating or eliminating the data association problem. This type of method needs to use the inherent cyclostationary characteristics of the communication signal to obtain a specific periodic frequency, which is not suitable for general scenarios of passive detection of underwater targets. In addition, some data association methods are not suitable for underwater target detection scenarios due to the existence of NP-hard problems, low robustness and efficiency when processing complex environments containing multiple targets in real time.

[0003] The existing technology includes a positioning algorithm that uses the cross structure of azimuth lines to distinguish real targets from false targets by the number of azimuth lines at the intersection and the signal energy at the intersection, which successfully avoids the data association problem and has the effect of removing "ghosts". However, this method only uses the energy information of the signal when the target strength is different or the background noise and interference are strong, which is prone to misjudgment. Summary of the invention

[0004] The embodiment of the present application provides a multi-target direct positioning and ghost identification method based on frequency coloring technology, which improves the utilization rate of signal information without reducing the positioning accuracy, and realizes the effective identification of targets with different frequency components, ghosts, and weak targets near strong interference.

[0005] In order to achieve the above object, the technical solution of the embodiment of the present invention is:

[0006] In the first aspect, an embodiment of the present invention provides a multi-target direct positioning ghost identification method based on frequency coloring technology, including: dividing the received signal of the distributed array into multiple data segments according to the receiving time, and performing fast Fourier transform on the time domain signal of each data segment to convert the time domain signal into the frequency domain; calculating the sampling covariance matrix of the received signal according to the frequency domain signal corresponding to the time domain signal of each data segment; based on the sampling covariance matrix, using the DPD-MVDR algorithm, positioning each sub-band signal to obtain the spatial spectrum of each sub-band; constructing a color value mapping matrix, mapping RGB color values ​​to each sub-band, frequency coloring the spatial spectrum of each sub-band, and obtaining the RGB color space spectrum of each sub-band; wherein different sub-band mappings represent RGB components of different colors; superimposing the RGB color space spectra of all sub-bands to obtain the RGB color space spectrum of the broadband signal; according to the RGB color space spectrum of the broadband signal, distinguishing each target position and ghosts.

[0007] In some possible implementations, before dividing the received signal of the distributed array into a plurality of data segments according to the receiving time and performing a fast Fourier transform on the time domain signal of each data segment to convert the time domain signal into the frequency domain, the method further includes: constructing a signal model of the received signal; the signal model is expressed as:

[0008] ;

[0009] in, For the The array output signal of arrays, the number of arrays is L, the The number of array elements is , t is time, K is the number of incident signals received by the array, k is the target position number, For The moment from far field incident to the The first goals, For the target locations, It is The first Under the far-field assumption, the signal attenuation is regarded as a scalar coefficient, which is only related to the relative position of the array and the target and has nothing to do with time. for Moment The additive noise received on the array is a wide-sense stationary zero-mean complex Gaussian white noise. is the array receiving signal vector, represents a complex set, express for dimensional complex matrix, For the The signal to The propagation delay of the array.

[0010] In some possible implementations, the received signal of the distributed array is divided into a plurality of data segments according to the receiving time, and a time domain signal of each data segment is subjected to a fast Fourier transform to convert the time domain signal into a frequency domain, including:

[0011] The receiving time interval of the received signal is expressed as , dividing the time interval into data segments, the length of each data segment is , for each data segment Point fast Fourier transform, the number of subbands is ; By performing fast Fourier transform on both sides of the equation of the signal model, the time domain signal is converted to the frequency domain, and in the In the data segment, The frequency domain output at each frequency point is expressed as:

[0012] ,

[0013] in, It corresponds to The frequency of the sampling points, is the sampling frequency, ; , and They are , and No. The Fourier coefficients of the sampling points are Relative to the The incident signal of an array; is the length of the sampling time interval of the received signal;

[0014] Indicates that for The signal at frequency The array response of are statistically independent complex Gaussian vectors.

[0015] In some possible implementations, the sampling covariance matrix of the received signal is expressed as:

[0016] ,

[0017] in, For the The array receiving signal of the array is The sampling covariance matrix at the frequency points is for The conjugate transpose of .

[0018] In some possible implementations, the spatial spectrum of the nth subband is expressed as:

[0019] ,

[0020] in, For the n The spatial spectrum of the subbands, For the The array in The target position solution formula on the sub-band is: , For a signal at frequency The array response is for The conjugate transpose of For the matrix Find the inverse operation.

[0021] In some possible implementations, the color value mapping matrix is ​​expressed as:

[0022] ,

[0023] in, is the color value mapping matrix in RGB components on subbands, represents a real matrix, express for dimensional real matrix, They are three RGB component values ​​respectively. Indicates that C is dimensional real matrix, It is the number of sub-bands.

[0024] In some possible implementations, RGB color values ​​are mapped to each sub-band, and the spatial spectrum of each sub-band is frequency-colored to obtain the RGB color space spectrum of each sub-band, including:

[0025] Record The RGB value corresponding to the subband is , the DPD-MVDR method The spatial spectrum of the sub-band output is mapped to the RGB space and expressed by the following formula:

[0026] ,

[0027] in, The DPD-MVDR method is The spatial spectrum of the subband output, express for -dimensional real matrix.

[0028] In some possible implementations, the RGB color space spectra of all sub-bands are superimposed to obtain the RGB color space spectrum of the broadband signal, including:

[0029] The sub-band output spatial spectra mapped to the RGB space are summed to obtain the broadband spatial spectrum output by the DPD-MVDR method in the RGB space, which is expressed by the following formula:

[0030] .

[0031] In some possible implementations, the target positions and ghosts are distinguished according to the RGB color space spectrum of the broadband signal, including:

[0032] In the RGB color space spectrum of the broadband signal, intersections with the same or similar colors are determined as target locations for recognition, and intersections with different colors are determined as ghosts.

[0033] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0034] In the embodiment of the present invention, considering the differences in the spectrum structures of different targets, the energy information and frequency information of the received signal can be used simultaneously, and each sub-band can be recolored by designing a frequency coloring strategy, and the mapped color space spectrum can be used to distinguish between real targets and ghosts. Energy information is used to confirm the possible location of the target, and frequency information is used for data association. Without reducing the positioning accuracy, the system's utilization of signal information is improved, and effective discrimination of targets with different frequency components, ghosts, and weak targets near strong interference is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention, the accompanying drawings required for use in the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0036] Figure 1 A schematic flow chart of an embodiment of a method for directly locating and distinguishing ghost images of multiple targets based on frequency coloring technology provided for the implementation of the present invention;

[0037] Figure 2 The spatial spectrum for target localization by the existing DPD-MVDR method;

[0038] Figure 3 An RGB color space spectrum for target positioning by the method provided by an embodiment of the present invention;

[0039] Figure 4 It is the estimated spatial spectrogram under three targets by the existing DPD-MVDR method;

[0040] Figure 5 It is the estimated spatial spectrum under three target conditions by the method provided by the present invention;

[0041] Figure 6 It is a spatial spectrum for target localization based on the existing DPD-CBF method;

[0042] Figure 7 RGB color space spectrum to improve the existing DPD-CBF method using frequency colorization technology;

[0043] Figure 8 It is a spatial spectrum for target localization based on the existing DPD-MUSIC method;

[0044] Fig. 9 RGB color space spectrum that improves the existing DPD-MUSIC method using frequency colorization technology. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] In the relevant description of this embodiment, the terms "including, containing, having" and the like are open terms and are generally understood to include but not be limited to; the term "at least one" is generally understood to mean one or more, where "plurality" refers to two or more; the term "at least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items, for example, "at least one of a, b or c", or "at least one of a, b and c", can all represent: a, b, c, ab (i.e., a and b), ac, bc, or abc, where a, b, c can be single or multiple, respectively; the symbol "A / B" is used to describe the selection relationship of associated objects, generally indicating an "or" relationship before and after.

[0047] In the following description of the present embodiment, the terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0048] Those skilled in the art should understand that in the following description of the embodiments of the present application, the order of serial numbers does not mean the order of execution, some or all of the steps can be executed in parallel or sequentially, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0049] Those skilled in the art will appreciate that the numerical ranges in the embodiments of the present application are to be construed as also specifically disclosing each intermediate value between the upper and lower limits of the scope. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the range is also included in the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded in the scope.

[0050] Unless otherwise specified, the technical / scientific terms used herein have the same meanings as those generally understood by those skilled in the art to which this application belongs. Although this application only describes preferred methods and materials, any methods and materials similar or equivalent to these may also be used in the implementation or testing of this application. All documents mentioned in this specification are incorporated by reference to disclose and describe methods and / or materials related to the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0051] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0052] In the passive positioning of targets in distributed arrays, the existing ghost removal methods mainly include enumeration methods, clustering methods and cyclic MUSIC methods. The traditional enumeration method lists all matching possibilities and compares them, and finally selects the ones with the highest matching degree as targets. It has the advantages of high association accuracy and low algorithm complexity. However, the computational complexity of this type of method is directly related to the number of nodes and the number of targets. Therefore, this type of method is more suitable for application scenarios with a small number of arrays and targets. For application scenarios with a large number of observation nodes and targets, it is generally considered to use LOB (line-of-bearing) intersection clustering to estimate the target position. Among them, using the K-means algorithm to process LOB measurements with noise is a typical clustering method, but this type of method often requires the number of targets as prior information. The cyclic MUSIC method separates LOB measurements according to the unique transmission model characteristics of the received signal, thereby alleviating or eliminating the data association problem. This type of method needs to use the inherent cyclostationary characteristics of the communication signal to obtain a specific periodic frequency, which is not suitable for general scenarios of passive detection of underwater targets. In addition, some data association methods are not suitable for underwater target detection scenarios due to the existence of NP-hard problems, low robustness and efficiency when processing complex environments containing multiple targets in real time.

[0053] The existing technology includes a positioning algorithm that uses the cross structure of azimuth lines to distinguish real targets from false targets by the number of azimuth lines at the intersection and the signal energy at the intersection, which successfully avoids the data association problem and has the effect of removing "ghosts". However, this method only uses the energy information of the signal when the target strength is different or the background noise and interference are strong, which is prone to misjudgment.

[0054] Frequency coloring technology can recolor the azimuth spectrum by correlating the frequency information of broadband signals with RGB information, thereby distinguishing targets with different frequency structures. Existing frequency coloring technology is mainly used for target azimuth estimation, but has not yet been used for target positioning.

[0055] The existing Direct Position Determination (DPD) algorithm converts the received signals of each array into multiple sub-bands through Discrete Fourier Transformation (DFT). At this time, the signal in each sub-band is regarded as a narrowband signal. Then, a cost function about the target position is constructed in each sub-band, and the spatial spectrum of each sub-band is solved and obtained, and then simply superimposed to obtain a broadband spatial spectrum. The peak in the broadband spatial spectrum corresponds to the target position. The existing DPD algorithm only uses the energy information of the signal to perform a full-domain scan and determine the target position by the energy strength. When the target energy strength is different, weak targets are easily interfered or even covered up.

[0056] Based on this, an embodiment of the present invention provides a multi-target direct positioning ghost identification method based on frequency coloring technology, which improves the system's utilization of signal information without reducing positioning accuracy, and achieves effective discrimination of targets with different frequency components, ghosts, and weak targets near strong interference.

[0057] Figure 1 A schematic diagram of an embodiment of a method for directly locating and distinguishing ghost images of multiple targets based on frequency coloring technology provided by the present invention is shown in FIG. Figure 1 As shown, the above-mentioned multi-target direct positioning ghost identification method based on frequency coloring technology may include:

[0058] S101, dividing the received signal of the distributed array into multiple data segments according to the receiving time, and performing a fast Fourier transform on the time domain signal of each data segment to convert the time domain signal into the frequency domain;

[0059] As can be understood, a distributed array is usually composed of multiple sensors or receivers distributed at different geographical locations or spatial positions to capture signals from different directions or sources. These signals may contain various frequency components and change over time.

[0060] In some embodiments, before the above step S101, the above method may include: constructing a signal model of the received signal.

[0061] Specifically, consider The receiving arrays are far apart, where The number of array elements is The position coordinates are expressed as:

[0062] .

[0063] in, For the The position coordinates of an array, T is the transposition symbol.

[0064] At this time, In the array The position coordinates of each array element are expressed as:

[0065] .

[0066] in, For the In the array The position coordinates of the array elements, ; Array receiving An uncorrelated far-field broadband incident signal, covering a frequency range of , No. Target locations It is expressed as:

[0067] ,

[0068] in, .exist The moment from far field incident to the The first The goal is , , then The array output signal model of the array is dimensional vector, which can be expressed as:

[0069] ;

[0070] in, For the The array output signal of the array, It is The first Under the far-field assumption, the signal attenuation can be regarded as a scalar coefficient, which is only related to the relative position of the array and the target and has nothing to do with time. for Moment The additive noise received on the array is a wide-sense stationary zero-mean complex Gaussian white noise. , is the array receiving signal vector, represents a complex set, express for dimensional complex matrix, It can be expressed as:

[0071] ,

[0072] in, For the The signal to The propagation delay of the array reflects the The array receives the The array response corresponding to the signal. ,in, For the The signal to In the array The propagation delay of an array element can be expressed as:

[0073] ,

[0074] in, is the propagation speed, For the k signal x Axis coordinates, For the Array No. The array element position is x The coordinates on the axis, For the k signal y Axis coordinates, For the Array No. The array element position is y Coordinates on the axis.

[0075] Through the above content, a signal model of the received signal of the receiving array can be constructed. Further, the above step S101 can specifically include:

[0076] The receiving time interval of the received signal is expressed as , dividing the time interval into data segments, the length of each data segment is , for each data segment Point fast Fourier transform, the number of subbands is ;in, is the length of the sampling time interval of the received signal.

[0077] It should be noted that FFT is an efficient algorithm for converting signals from the time domain to the frequency domain. It converts the signal into multiple frequency components by sampling and decomposing the signal. These frequency components are divided into N sub-bands in the frequency domain, and each sub-band corresponds to a specific frequency range. When the received signal is subjected to an N-point FFT transform, it is equivalent to dividing the signal into N equal-length sub-sequences in the time domain, and performing a Fourier transform on each sub-sequence to obtain multiple sub-bands. At this time, the signal in each sub-band can be regarded as a narrowband signal. Since the number of FFT points is equal to the length of the input signal, the number of sub-bands is equal to N. Each sub-band contains the information of the signal in the corresponding frequency range, thereby realizing the frequency domain decomposition of the signal.

[0078] In some embodiments, determining the number of divisions Q of the signal receiving time interval and the number of fast Fourier transform (FFT) points N directly affects the resolution and accuracy of frequency domain analysis. Therefore, before performing the fast Fourier transform, the values ​​of Q and N need to be determined first.

[0079] Exemplarily, when determining the value of Q, the characteristics and requirements of the received signal may be considered first. For example, if the signal is periodic, the length of the data segment should match the period of the signal so that each period of the signal can be fully captured. If the signal is non-periodic or contains multiple frequency components, the length of the data segment should be long enough to contain enough signal information for FFT analysis.

[0080] Or it can also be based on the trade-off between time resolution and frequency resolution. For example, the number of data segments Q is inversely proportional to the time resolution, that is, the larger the Q, the shorter the time length of each data segment, the higher the time resolution, but the frequency resolution will decrease. Conversely, the smaller the Q, the longer the time length of each data segment, the lower the time resolution, but the frequency resolution will increase.

[0081] In some embodiments, the value of Q may also be determined by actual application requirements. For example, in situations where high time resolution is required (such as real-time monitoring), a larger Q may be selected; in situations where high frequency resolution is required (such as spectrum analysis), a smaller Q may be selected.

[0082] When determining the value of N, it can also be determined based on a variety of demand factors.

[0083] For example, according to the sampling theorem, the sampling frequency Fs is at least twice the highest frequency component of the signal to avoid aliasing. The number of FFT points N is usually related to the sampling rate Fs and the required analysis bandwidth. In addition, the number of FFT points N determines the resolution in the frequency domain. Therefore, in order to obtain higher frequency resolution, the number of FFT points N can be increased. Or N can be determined according to the needs of the actual application. For example, in situations where high-resolution spectrum analysis is required, a larger N can be selected; in situations where a large amount of data needs to be processed quickly, a smaller N may need to be selected to reduce the amount of calculation.

[0084] In addition, in practical applications, the matching problem between data length and FFT points needs to be considered. If the data length is not an integer multiple of the FFT points, zero padding or truncation may be required.

[0085] In some embodiments, the time domain signal is converted to the frequency domain by performing a fast Fourier transform on both sides of the equation of the signal model. In the data segment, The frequency domain output at each frequency point is expressed as:

[0086] ,

[0087] in, It corresponds to The frequency of the sampling points, is the sampling frequency, ; , and They are , and No. The Fourier coefficients of the sampling points are Relative to the The incident signal of an array;

[0088] Indicates that for The signal at frequency The array response of are statistically independent complex Gaussian vectors.

[0089] S102, calculating a sampling covariance matrix of a received signal according to a frequency domain signal corresponding to a time domain signal of each data segment;

[0090] Specifically, after the frequency domain signal on the subband corresponding to each data segment in step S101 is obtained, each frequency domain signal is multiplied by its own conjugate transpose, and then each data segment is summed and averaged to obtain the sampling covariance matrix of the received signal.

[0091] In some embodiments, the sampling covariance matrix of the received signal can be expressed as:

[0092] .

[0093] in, For the The array receiving signal of the array is n The sampling covariance matrix at the frequency points, H is the conjugate transposed symbol, for The conjugate transpose of .

[0094] S103, based on the sampling covariance matrix, using the DPD-MVDR algorithm, positioning each sub-band signal to obtain a spatial spectrum of each sub-band;

[0095] Among them, the DPD-MVDR algorithm combines the Digital Pre-Distortion (DPD) algorithm and the Minimum Variance Distortionless Response (MVDR) algorithm, which can be used to improve the performance of signal processing.

[0096] Specifically, in a distributed system with multiple arrays, the MVDR weight vector used in the DPD method should be The arrays satisfy the following expressions at the same time:

[0097] ,

[0098] in, is the MVDR weight vector, To calculate the cost function of the optimal weight vector, is a dimensional vector, is the position of the target to be measured, is the optimal weight vector, It is the abbreviation of "subject to", which means "satisfy...". The above formula means:

[0099] For the above equation Solve to find the equation that satisfies of The minimum value of .

[0100] in,

[0101] ,

[0102] ,

[0103] in, is a dimensional block diagonal matrix, It is The sampling covariance matrix of arrays, It is The above expression is a minimization problem of a quadratic function under linear constraints. The negative gradient operator can be used to solve this problem, and its solution can be expressed as:

[0104] ,

[0105] in, is the MVDR weight vector of the nth subband, for The inverse operation of for The conjugate transpose of .

[0106] Therefore, by traversing and searching the area of ​​interest, the spatial spectrum of the DPD method based on the MVDR estimator can be obtained, which can be expressed as:

[0107] ,

[0108] in, For the n The spatial spectrum of the subbands, for The conjugate transpose of .

[0109] Furthermore, the expression of the spatial spectrum of the above DPD method can be equivalent to:

[0110] ,

[0111] in, , is the The array in The target position solution formula on the sub-band is: For a signal at frequency The array response is for The conjugate transpose of For the matrix Find the inverse operation.

[0112] By performing a two-dimensional spatial scan on the function of the above equivalent expression, the first The spatial spectrum of multi-target positioning on the sub-band. At this time, in the prior art, the local peaks of multiple energies in the obtained spatial spectrum are regarded as the positioning coordinates of the multiple targets. However, the local peaks generated during target estimation are not necessarily all real targets. This is the "ghost" problem that the present invention needs to consider, which will affect the accuracy of positioning.

[0113] S104, constructing a color value mapping matrix, mapping the RGB color values ​​to each sub-band, frequency coloring the spatial spectrum of each sub-band, and obtaining the RGB color space spectrum of each sub-band; wherein different sub-band mappings represent RGB components of different colors;

[0114] Specifically, by constructing a color value mapping matrix , realizing the mapping from frequency to color. The function of this matrix is ​​to make each color represent a sub-band frequency. Through such a mapping relationship, the energy information and frequency information of the signal are used at the same time.

[0115] In some embodiments, the color value mapping matrix It can be expressed as:

[0116] ,

[0117] in, , is the color value mapping matrix in RGB components on subbands, represents the set of real numbers, express for dimensional real matrix, They are three RGB component values ​​respectively. Indicates that C is dimensional real matrix, is the number of subbands. Through the above color value mapping matrix, each subband can be mapped to the RGB component representing a color.

[0118] Specifically, the above step S104 may include:

[0119] Record The RGB value corresponding to the subband is , the DPD-MVDR method The spatial spectrum of the sub-band output is mapped to the RGB space, which can be expressed by the following formula:

[0120] ,

[0121] in, The DPD-MVDR method is The spatial spectrum of the subband output, express for -dimensional real matrix.

[0122] S105, superimposing the RGB color space spectra of all sub-bands to obtain the RGB color space spectrum of the broadband signal;

[0123] In some embodiments, step S105 may include:

[0124] The sub-band output spatial spectra mapped to the RGB space are summed to obtain the broadband spatial spectrum output by the DPD-MVDR method in the RGB space, which is expressed by the following formula:

[0125] .

[0126] It is understandable that since each sub-band represents a different frequency, and each frequency is colored by a different RGB color value, when locating the target, the "ghost" part of the data association error will gather a variety of different colors, which is obviously different from the real target color. In this way, it is easy to distinguish the required target position and the ghost that causes the problem.

[0127] S106 , distinguishing target positions and ghost images according to the RGB color space spectrum of the broadband signal.

[0128] Specifically, through the above steps S101 to S105, a broadband spatial spectrum output by the DPD-MVDR method in the RGB space can be plotted. According to the RGB color space spectrum of the broadband signal, distinguishing each target position and the ghost can be that in the RGB color space spectrum of the broadband signal, the intersection position with the same or similar color is determined as the target position to be identified, and the intersection position with different colors is determined as the position where the ghost exists.

[0129] As can be understood, in the RGB color space spectrum, the intersections with the same or similar colors are first identified. These intersections usually correspond to signals with the same or similar frequency structure, that is, they are considered to be the target positions. In contrast, intersections with different colors may represent signals with different frequency structures, which may be caused by propagation loss, noise interference or scattering effects, and are therefore considered to be ghosts.

[0130] In practical applications, due to factors such as attenuation, scattering, and reflection on the propagation path, the same type of target may have subtle frequency structure differences. These differences may appear as subtle color differences in the RGB color space spectrum. In order to accurately distinguish the target position and ghost images, a certain fault tolerance mechanism can be introduced. For example, a color similarity threshold can be set. When the color difference between two intersections is less than the threshold, they are still considered to be the same type of target.

[0131] In the embodiment of the present invention, considering the differences in the spectrum structures of different targets, the energy information and frequency information of the received signal can be used simultaneously, and each sub-band can be recolored by designing a frequency coloring strategy, and the mapped color space spectrum can be used to distinguish between real targets and ghosts. Energy information is used to confirm the possible location of the target, and frequency information is used for data association. Without reducing the positioning accuracy, the system's utilization of signal information is improved, and effective discrimination of targets with different frequency components, ghosts, and weak targets near strong interference is achieved.

[0132] The following is a specific embodiment to illustrate a multi-target direct positioning ghost identification method based on frequency coloring technology provided by the present invention.

[0133] Take a distributed array as an example, where the number of array elements in both arrays is 21 and the uniform linear array is 3m. The array element spacing is 3m and the sound speed is 1500m / s. The spacing between the two arrays is 1500m, and the sampling frequency is 2.5kHz. The first array element position of array 1 is [0,780]m, and the first array element position of array 2 is [0,-720]m. The scanning range of the target of interest is set as follows: the x-axis is [0,3000]m, and the grid interval is set to 50m; the y-axis is [-2000,2000]m, and the grid interval is set to 50m. In the case of multiple targets, in the coloring strategy of each sub-band frequency corresponding to the RGB value, is the lowest frequency among multiple signals, is the highest frequency among multiple signals.

[0134] When performing dual-target positioning, consider two target signals incident from the far field to two arrays. The position of target 1 is [600,700]m, the frequency is [250,500]Hz, and the signal power received by array 1 is The value is -5dB. The position of target 2 is [1000, -200]m, the frequency is [125, 250]Hz, and the received signal power of array 1 is The value is -10dB. Assuming a uniform noise field, the noise power of each array is available Indicates that the value is 0dB.

[0135] Under the same simulation conditions, the conventional DPD-MVDR method and the method proposed in this invention are used to locate the target. The positioning effect can be seen in Figures 2 to 3 As shown. Among them, Figure 2 To obtain the spatial spectrum of the target through the existing DPD-MVDR method, Figure 3 The RGB color space spectrum for target positioning by the method provided by the embodiment of the present invention.

[0136] pass Figures 2 to 3It can be seen that the two arrays estimate 4 intersection points, of which only 2 are real targets and the other 2 are ghosts. Figure 2 As shown in , when the target strength is different, the three intersection points with similar energy in the white box include ghosts and targets, but it is impossible to directly determine which is the target and which is the ghost, which will lead to a decrease in the accuracy of target estimation. Figure 3 As shown, the method provided by the present invention can effectively distinguish between ghosts and targets by utilizing the frequency information of the signal, and accurately determine that only the intersection of two red rays among the intersections in the three white boxes is the target, while the intersection of rays of different colors is a ghost that affects the target positioning.

[0137] Furthermore, the same simulation conditions are used to perform multi-target positioning. Under the same simulation conditions, a new target 3 is added, and its received signal power is -10dB. The position of target 3 is set close to target 1, with coordinates of [800,500]m, and the signal frequency is set to [250,750]Hz, which includes the frequency bandwidth of target 1.

[0138] In the case of multiple targets, there will be more points with stronger energy in the spatial spectrum, which are easily mistaken for targets. Figures 4 to 5 As shown, Figure 4 is the estimated spatial spectrum under three targets by the existing DPD-MVDR method, Figure 5 It is the estimated spatial spectrum under three target conditions by the method provided by the present invention. Figures 4 to 5 As shown in the figure, when there are three targets, there will be nine obvious energy intersections in the spatial spectrum. Figures 4 to 5 Five intersections with strong energy are marked in the figure. The existing DPD-MVDR spatial spectrum cannot directly identify which of these intersections are real targets. Figure 5 The RGB color space spectrum of the method proposed by the present invention is shown, which distinguishes different targets by using the frequency information of the signal. The design strategy of the target corresponds to the intersection points generated by the same color rays, while the intersection points generated by different color rays are ghosts. Figure 5 Target 1 corresponds to green, target 2 corresponds to red, and target 3 corresponds to blue. In this way, it is possible to directly identify which of the five intersections with stronger energy are real targets.

[0139] Based on the above, it can be seen that the existing methods will generate false targets when estimating multiple targets, which easily leads to the situation where the positioning results are misjudged. However, the method provided by the embodiment of the present invention can achieve accurate positioning of the targets without knowing the number of targets in advance. Different from the simple frequency domain information superposition in the traditional method, the proposed method effectively distinguishes between false targets and real targets through multi-dimensional information coloring of energy and frequency without losing the positioning accuracy and resolution of the existing direct positioning method, thereby improving the accuracy of multi-target positioning.

[0140] In some embodiments, the multi-target direct positioning ghost identification method based on frequency coloring technology provided by the embodiment of the present invention, in addition to the above improvement based on the DPD-MVDR algorithm for target positioning, can also make similar improvements to other positioning methods as described above. For example, existing DPD methods, such as the DPD-CBF method and the DPD-MUSIC method, can be improved using frequency coloring technology to achieve the distinction between real targets and ghosts.

[0141] Specifically, taking the case of two targets, the position of target 1 is [600,700]m, the frequency is [250,500]Hz, and the received signal power of array 1 is The value is -5dB. The position of target 2 is [1000, -200]m, the frequency is [125, 250]Hz, and the received signal power of array 1 is The value is -10dB. Figure 6 To obtain the spatial spectrum of target positioning based on the existing DPD-CBF method, Figure 7 RGB color space spectrum that improves the existing DPD-CBF method using frequency colorization technology. Figure 8 To obtain the spatial spectrum for target localization based on the existing DPD-MUSIC method, Fig. 9 RGB color space spectrum that improves the existing DPD-MUSIC method using frequency colorization technology.

[0142] join Figures 6 to 9 As shown, based on the multi-target direct positioning ghost identification method based on frequency coloring technology provided by an embodiment of the present invention, the frequency coloring technology is used to improve the existing different positioning methods. When performing target positioning, the intersection positions of rays with the same or similar colors are determined as the target positions that need to be identified, and the intersection positions of different colors are determined as the positions where ghosts exist.

[0143] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments.

[0144] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present application.

Claims

1. A multi-target direct positioning ghost identification method based on frequency coloring technology, characterized in that: include: The received signal of the distributed array is divided into a plurality of data segments according to the receiving time, and the time domain signal of each data segment is transformed into the frequency domain by fast Fourier transform; Calculating a sampling covariance matrix of the received signal according to a frequency domain signal corresponding to a time domain signal of each data segment; Based on the sampling covariance matrix, each sub-band signal is located by using a DPD-MVDR algorithm to obtain a spatial spectrum of each sub-band; Construct a color value mapping matrix, map the RGB color value to each sub-band, perform frequency coloring on the spatial spectrum of each sub-band, and obtain the RGB color space spectrum of each sub-band; where different sub-band mappings represent RGB components of different colors; Superimpose the RGB color space spectra of all sub-bands to obtain the RGB color space spectrum of the broadband signal; According to the RGB color space spectrum of the broadband signal, each target position and ghost image are distinguished.

2. The method according to claim 1, characterized in that Before dividing the received signal of the distributed array into a plurality of data segments according to the receiving time and performing a fast Fourier transform on the time domain signal of each data segment to convert the time domain signal into the frequency domain, the method further includes: constructing a signal model of the received signal; the signal model is expressed as: ； in, For the The array output signal of arrays, the number of arrays is L, the The number of array elements is , t is time, K is the number of incident signals received by the array, k is the target position number, For The moment from far field incident to the The first goals, For the target locations, It is The first The signal attenuation is considered as a scalar coefficient under the far-field assumption, which is only related to the relative position of the array and the target and has nothing to do with time; for Moment The additive noise received on the array is a wide-sense stationary zero-mean complex Gaussian white noise. is the array receiving signal vector, represents a complex set, express for dimensional complex matrix, For the The signal to The propagation delay of the array.

3. The method according to claim 2, characterized in that The receiving signal of the distributed array is divided into a plurality of data segments according to the receiving time, and the time domain signal of each data segment is fast Fourier transformed to convert the time domain signal into the frequency domain, including: The receiving time interval of the received signal is expressed as , dividing the time interval into data segments, the length of each data segment is , for each data segment Point fast Fourier transform, the number of subbands is ; By performing fast Fourier transform on both sides of the equation of the signal model, the time domain signal is converted to the frequency domain, and in the In the data segment, The frequency domain output at each frequency point is expressed as: ， in, It corresponds to The frequency of the sampling points, is the sampling frequency, ; , and They are , and No. The Fourier coefficients of the sampling points are Relative to the The incident signal of an array; is the length of the sampling time interval of the received signal; Indicates that for The signal at frequency The array response of are statistically independent complex Gaussian vectors.

4. The method according to claim 3, characterized in that The sampling covariance matrix of the received signal is expressed as: ， in, For the The array receiving signal of the array is n The sampling covariance matrix at the frequency points is for The conjugate transpose of .

5. The method according to claim 4, characterized in that No. n The spatial spectrum of a subband is expressed as: ， in, For the n The spatial spectrum of the subbands, For the The array in The target position solution formula on the sub-band is: , For a signal at frequency The array response is for The conjugate transpose of For the matrix Find the inverse operation.

6. The method according to claim 5, characterized in that The color value mapping matrix is ​​expressed as: ， in, is the color value mapping matrix in RGB components on subbands, represents the set of real numbers, express for dimensional real matrix, They are three RGB component values ​​respectively. Indicates that C is dimensional real matrix, It is the number of sub-bands.

7. The method according to claim 6, characterized in that The step of mapping the RGB color values ​​to each sub-band and frequency coloring the spatial spectrum of each sub-band to obtain the RGB color space spectrum of each sub-band includes: Record The RGB value corresponding to the subband is , the DPD-MVDR method The spatial spectrum of the sub-band output is mapped to the RGB space and expressed by the following formula: ， in, The DPD-MVDR method is The spatial spectrum of the subband output, express for -dimensional real matrix.

8. The method according to claim 7, characterized in that The step of superimposing the RGB color space spectra of all sub-bands to obtain the RGB color space spectrum of the broadband signal includes: The sub-band output spatial spectra mapped to the RGB space are summed to obtain the broadband spatial spectrum output by the DPD-MVDR method in the RGB space, which is expressed by the following formula: 。 9. The method according to claim 8, characterized in that The step of distinguishing target positions and ghost images according to the RGB color space spectrum of the broadband signal includes: In the RGB color space spectrum of the broadband signal, intersections with the same or similar colors are determined as target positions for recognition, and intersections with different colors are determined as positions where ghost images exist.

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