A target space feature extraction and fusion positioning method based on Hough transform

Abstract

The invention discloses a target space feature extraction and fusion positioning method based on Hough transform, and the method includes: acquiring and processing the time domain data of each base array element, constructing the guidance response power spectrum of all sub-bands within each base array bandwidth, and extracting the sub-band peak value; constructing a data matrix according to the non-zero points on the sub-band peak distribution diagram, performing Hough transform on the data matrix, constructing a sub-band accumulation matrix and a sub-band mapping relationship matrix, performing secondary accumulation on the sub-band accumulation matrix within the bandwidth to obtain a broadband accumulation matrix, merging the corresponding elements in the sub-band mapping relationship matrix to obtain a broadband mapping relationship matrix; detecting the maximum value in the broadband accumulation matrix, and restoring the value corresponding to the point where the maximum value is located according to the mapping relationship to obtain a broadband peak distribution diagram; superimposing the broadband peak distribution diagrams of each base array to obtain a cross structure, and determining the target positioning result through the cross structure. Through feature fusion and Hough transform, the positioning accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater acoustic array signal processing, and in particular to a target space feature extraction and fusion positioning method based on Hough transform. Background Art

[0002] There are two main underwater distributed passive detection and positioning mechanisms, namely the two-step positioning method and the direct positioning method. In theory, the direct positioning method is the optimal positioning method. It belongs to the data set fusion. The principle is to transmit the original received data of each array to the processing center and treat each array as a related large array for centralized processing. This positioning method requires that each array needs to maintain time synchronization during data sampling, and the correlation of the received data is high. However, the transmission requirement of this original sampling data limits the application of the direct positioning method in underwater distributed passive detection systems.

[0003] The two-step positioning method belongs to decision-level fusion. Its principle is to transmit the intermediate parameters (also called measurements) such as the wave angle and wave time estimated by each observation node to the processing center, and then obtain the estimated result of the target position through statistical methods such as the least squares method. Although this method has a small amount of data transmission, it is necessary to assume that there are a large number of independent measurements and the error is small in order to obtain an asymptotically unbiased estimation result. Therefore, this method is mostly used in fields such as radar or large-scale sensor networks. Unlike these fields, the number of sonar platforms that can be collaboratively detected in underwater attack and defense is limited, so the two-step method cannot achieve a more ideal positioning performance in underwater distributed positioning systems.

[0004] Therefore, it is urgent to propose corresponding solutions to the problem of poor positioning performance in the existing technology in underwater distributed detection systems. Summary of the invention

[0005] The embodiments of the present invention provide a method, system, device and medium for extracting target space features and fusion positioning based on Hough transform, aiming to solve the problem of poor positioning performance in the prior art in underwater distributed detection systems.

[0006] In a first aspect, an embodiment of the present invention provides a method for extracting and fusion positioning of target space features based on Hough transform, the method comprising:

[0007] Acquire time domain reception data of each base array element, process the time domain reception data, construct a steering response power spectrum of all sub-bands within each base array bandwidth according to the processed data, and extract sub-band peaks of the steering response power spectrum to obtain a sub-band peak distribution diagram;

[0008] Constructing a data matrix according to the non-zero points on the sub-band peak distribution diagram, performing Hough transform on the data matrix, constructing a sub-band accumulation matrix and a sub-band mapping relationship matrix according to the data after the Hough transform, performing secondary accumulation on the sub-band accumulation matrix within the bandwidth to obtain a broadband accumulation matrix, merging corresponding elements in the sub-band mapping relationship matrix to obtain a broadband mapping relationship matrix;

[0009] Detecting and acquiring the maximum value in the broadband accumulation matrix, and restoring the value of the data space corresponding to the point where the maximum value is located according to the mapping relationship marked in the broadband mapping relationship matrix, to obtain a broadband peak distribution diagram;

[0010] The broadband peak distribution diagrams of each base array are superimposed to obtain a multi-base array spatial feature cross structure, and the target positioning result is determined by the multi-base array spatial feature cross structure.

[0011] In a second aspect, an embodiment of the present invention provides a target space feature extraction and fusion positioning system based on Hough transform, the system comprising:

[0012] The feature extraction module is used to obtain the time domain reception data of each base array element, process the time domain reception data, construct a steering response power spectrum of all sub-bands within each base array bandwidth according to the processed data, and extract the sub-band peak value of the steering response power spectrum to obtain a sub-band peak distribution diagram.

[0013] Constructing a data matrix according to the non-zero points on the sub-band peak distribution diagram, performing Hough transform on the data matrix, constructing a sub-band accumulation matrix and a sub-band mapping relationship matrix according to the data after the Hough transform, performing secondary accumulation on the sub-band accumulation matrix within the bandwidth to obtain a broadband accumulation matrix, merging corresponding elements in the sub-band mapping relationship matrix to obtain a broadband mapping relationship matrix;

[0014] A recovery module, used for detecting and obtaining the maximum value in the broadband accumulation matrix, and recovering the value of the data space corresponding to the point where the maximum value is located according to the mapping relationship marked in the broadband mapping relationship matrix, to obtain a broadband peak distribution diagram;

[0015] The positioning module is used to superimpose the broadband peak distribution diagrams of each base array to obtain a multi-array spatial feature cross structure, and determine the target positioning result through the multi-array spatial feature cross structure.

[0016] In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the method described in the first aspect above.

[0018] The embodiment of the present invention provides a method and system for extracting and fusion positioning of target space features based on Hough transform. The time domain receiving data of each base array element is obtained, the time domain receiving data is processed, and the steering response power spectrum of all sub-bands in each base array bandwidth is constructed according to the processed data, and the sub-band peak value of the steering response power spectrum is extracted to obtain a sub-band peak distribution map; then, a data matrix is ​​constructed according to the non-zero points on the sub-band peak distribution map, and the data matrix is ​​subjected to Hough transform. According to the data after Hough transform, a sub-band accumulation matrix and a sub-band mapping relationship matrix are constructed, and the sub-band accumulation matrix in the bandwidth is accumulated twice to obtain a broadband accumulation matrix, and the corresponding elements in the sub-band mapping relationship matrix are merged to obtain a broadband mapping relationship matrix; then, the maximum value in the broadband accumulation matrix is ​​detected and obtained, and according to the mapping relationship marked in the broadband mapping relationship matrix, the value of the data space corresponding to the point where the maximum value is located is restored to obtain a broadband peak distribution map; finally, the broadband peak distribution maps of each base array are superimposed to obtain a multi-base array spatial feature cross structure, and the target positioning result is determined by the multi-base array spatial feature cross structure.

[0019] Compared with the existing technology, the information fusion method of extracting the inherent acoustic features that can reflect different types of targets can provide the processing center with richer target spatial position information. At the same time, it does not require the transmission of original data, and can achieve a balance between system performance and communication requirements. It is a fusion strategy that meets the actual application requirements of underwater acoustics. In addition, this method also uses the Hough transform to detect the straight line features in the beam output energy spectrum, and judges the effective features and interference features through the consistency of the main lobe direction at different frequencies, retains the effective features, and eliminates the interference features, thereby improving the robustness of the algorithm in a low signal-to-noise ratio environment and greatly improving the accuracy of the target positioning estimation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments 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.

[0021] Figure 1 A schematic diagram of a flow chart of a target space feature extraction and fusion positioning method based on Hough transform provided in an embodiment of the present invention;

[0022] Figure 2is a schematic diagram of an underwater receiving system and a target position according to an embodiment of the present application;

[0023] Figure 3 is a schematic diagram of target spatial features extracted by matrix 1 when Hough transform is not used according to an embodiment of the present application;

[0024] Figure 4 is a schematic diagram of target spatial features extracted by matrix 1 using Hough transform according to an embodiment of the present application;

[0025] Figure 5 is a schematic diagram of a multi-base array cross structure obtained when the Hough transform is not used according to an embodiment of the present application;

[0026] Figure 6 is a schematic diagram of a multi-base array cross structure obtained when using Hough transform according to an embodiment of the present application;

[0027] Figure 7 is a schematic diagram of root mean square error comparison according to an embodiment of the present application;

[0028] Figure 8 A schematic block diagram of a target space feature extraction and fusion positioning system based on Hough transform provided in an embodiment of the present invention;

[0029] Fig. 9 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0032] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0033] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0034] This embodiment provides a target space feature extraction and fusion positioning method based on Hough transform. Figure 1 A flow chart of a target space feature extraction and fusion positioning method based on Hough transform provided in an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method includes steps S110 to S140.

[0035] S110, acquiring time domain reception data of each base array element, processing the time domain reception data, constructing a steering response power spectrum of all sub-bands within each base array bandwidth according to the processed data, and extracting sub-band peaks of the steering response power spectrum to obtain a sub-band peak distribution diagram.

[0036] Firstly, the time domain receiving data of each base array element in the underwater distributed detection system is obtained;

[0037] Then, the acquired time domain received data is processed, and the steering response power spectrum of all sub-bands within each array bandwidth is constructed based on the processed data. In one embodiment, this step specifically includes:

[0038] S111 . Convert the acquired time-domain received data into frequency-domain data.

[0039] Specifically, assume that the distributed system includes L receiving arrays, and the lth (1≤l≤L) array contains N l The time domain received signal of the ith element of the lth base array is denoted as x l,i . Assume that the array element is sampled at a sampling frequency f during the observation time s The number of time domain sampling points is T, and the time domain received signal is divided into N sec Segments, each segment has T sec sampling points, Among them, T sec Customize parameters for users.

[0040] For each segment of length T sec The time domain sampling data is T sec Point fast Fourier transform, the frequency resolution F s =f s / T sec Assume that the center frequency of the received signal in the time domain is f c , the bandwidth is B, then the lower limit frequency is f l =f c -B / 2, upper limit frequency f h=f c +B / 2. The subband number corresponding to the lower limit frequency is The subband number corresponding to the upper limit frequency is Where [·] indicates rounding to the center. According to the upper and lower frequency limits, the total number of subbands to be considered is N. f =B / F s +1.

[0041] The k-th segment time domain received signal is recorded as The time domain received signal is converted into the frequency domain. The specific calculation formula is shown in the following formula (1):

[0042]

[0043] in, is the spectrum of the kth segment time domain received signal in the jth subband, n is the sampling point number,

[0044] S112. Construct a frequency domain data vector matrix according to the frequency domain data.

[0045] Specifically, N sec The spectrum of the segmented time domain signal in the jth subband is taken out and the spectrum is calculated according to the segment number 1, 2, ..., k, ..., N sec The order of columns is placed in the spectrum row vector X l,i (j), that is, Performing the same operation on all sub-bands within the signal bandwidth yields N f 1×N sec Dimensional vector

[0046] For the lth matrix N l The time domain received signals of the array elements are subjected to frequency domain conversion processing in step S111, and the N l The spectral row vector {X l,i (j),1≤i≤N l} According to the base array number sequence 1,2,...,l,...,N l , arranged row by row, to obtain the frequency domain data vector matrix of the lth matrix on the jth subband in,[·] T represents transpose. The dimension of the frequency domain data vector matrix is ​​N l ×N sec , each column corresponds to the spectrum on the j-th sub-band obtained from the k-th segment of time domain signal data.

[0047] Finally, the above operations are performed on L matrixes, and finally L frequency domain data vector matrices {X l(j),1≤l≤L}.

[0048] S113. Obtain a sub-band frequency domain sampling covariance matrix through frequency domain data vector matrix construction.

[0049] Specifically, the frequency domain data of the jth subband of the lth matrix is ​​used to construct the subband frequency domain sampling covariance matrix of the lth matrix. The construction formula is shown in the following (2):

[0050]

[0051] in,[·] H represents the conjugate transpose.

[0052] Similarly, we can construct a bandwidth N f The sampling covariance matrix of the subbands.

[0053] S114, gridding the observation area according to a preset step size, and calculating the steering response power spectrum of each base array subband for each grid point in the observation area according to the subband frequency domain sampling covariance matrix.

[0054] Specifically, the observation area G is divided into grids along the x-axis (east-west direction) and the y-axis (north-south direction) according to a certain step distance Δ, where the selection of the step size needs to comprehensively consider the amount of calculation and the accuracy of the algorithm. The larger the step size, the smaller the amount of calculation, but the lower the algorithm accuracy. Therefore, the step size needs to be set according to the actual situation.

[0055] According to the subband frequency domain sampling covariance matrix, the jth subband of the lth matrix is ​​calculated, and its Capon beamformer is for the gth grid point r in the observation area G. g =[x g ,y g ] T The power spectrum of the guidance response is calculated as follows:

[0056]

[0057] in, represents the inverse matrix of the j-th subband sampling covariance matrix, a l (j,r g ) represents the steering vector of the g-th grid point in the observation area, f j represents the frequency value of the jth subband, Represents the signal from grid point r g The propagation delay of the i-th array element to the l-th array, i = 1, ..., N l , c = 1500m / s is the speed of sound in water.

[0058] By traversing all grid points in the observation area G, the power spectrum of the steering response of the jth subband of the lth matrix {P l (j,r g ),r g ∈G}. Similarly, by performing the above operation on all subbands of all base matrices, we can obtain N f Power spectrum of the guided response of the sub-band.

[0059] After the steering response power spectra of all sub-bands within each array bandwidth are calculated through the above steps, sub-band peaks are extracted from the obtained steering response power spectra. In one embodiment, the specific implementation steps include:

[0060] S115, constructing a subband peak distribution matrix of each base array subband, and initializing the subband peak distribution matrix, wherein the elements in the matrix correspond one-to-one to the grid points in the observation area.

[0061] Specifically, construct the subband peak distribution matrix PD of the jth subband of the lth matrix l (j) and is initialized to 0. The elements of this matrix correspond one-to-one to the grid points in the observation area G.

[0062] S116. Select extreme points of the steering response power spectrum according to preset conditions, assign 1 to corresponding positions of the selected extreme points on the sub-band peak distribution matrix, and obtain a sub-band peak distribution map.

[0063] Specifically, from the power spectrum of the steered response of the jth subband of the lth matrix {P l (j,r g ),r g ∈G}, select all extreme points that meet the preset conditions. Preferably, the preset conditions include: the point whose value is greater than or equal to the value of the surrounding 8 points is the extreme point that meets the conditions. The selected extreme points are placed in the sub-band peak distribution matrix PD l The corresponding position on (j) is assigned a value of 1 to obtain the peak distribution diagram of the sub-band.

[0064] Similarly, peak values ​​of all subbands of all base arrays are extracted to obtain N f Peak distribution diagram of the sub-bands.

[0065] S120. Construct a data matrix according to the non-zero points on the sub-band peak distribution diagram, perform Hough transform on the data matrix, construct a sub-band accumulation matrix and a sub-band mapping relationship matrix according to the data after the Hough transform, perform secondary accumulation on the sub-band accumulation matrix within the bandwidth to obtain a broadband accumulation matrix, merge the corresponding elements in the sub-band mapping relationship matrix to obtain a broadband mapping relationship matrix.

[0066] Construct a data matrix based on the non-zero points on the sub-band peak distribution graph. Specifically, convert the peak distribution graph PD of the j-th sub-band of the l-th matrix into l The row and column subscripts corresponding to the non-zero elements in (j) are arranged in the data matrix D according to the preset order. l (j). The preferred sorting method is: from the matrix PD l (j) Start searching by column from the first element. If the (x,y) coordinate value of the observation region corresponding to the first non-zero element is [x1,y1], then the matrix D l (j) The elements in the first column are x1 and y1.

[0067] Assume that the matrix PD l (j) There are K non-zero points in total, so the data matrix The dimension is 2×K.

[0068] Use the above method to construct N L matrixes respectively. f The data matrix of the subbands.

[0069] Then, the constructed data matrix is ​​subjected to Hough transform, and a subband accumulation matrix and a subband mapping relationship matrix are constructed according to the data after Hough transform. In one embodiment, this step specifically includes:

[0070] S121. Transform the data matrix from the data space to the parameter space through Hough transform to obtain the parameter space matrix.

[0071] Specifically, assuming that the observation area is the data space, the data matrix D is transformed by the transformation matrix l The elements in (j) are transformed from the data space xy (observation area) to the parameter space ρ-θ. The transformation matrix is ​​shown in equation (4):

[0072]

[0073] Among them, θ1, θ2, …, It is the discrete angle value obtained by uniformly quantizing the parameter θ in the parameter space, in radians. In practical applications, the size of θ is generally 0:Δθ:π-Δθ. It should be noted that the selection of the quantization interval of the parameter space needs to consider both the amount of calculation and the accuracy of the algorithm. The larger the quantization interval, the smaller the amount of calculation, but the lower the accuracy of the algorithm, so it needs to be selected according to the actual situation.

[0074] The Hough transform is implemented through matrix multiplication to obtain the transformed parameter space matrix Y l (j), the calculation formula is shown in the following formula (5):

[0075]

[0076] It can be seen that the parameter space matrix Y l Any element in (j)

[0077] S122, calculating the maximum value of the parameter space matrix of all sub-bands, setting the division range according to the maximum value, performing quantization division of the parameter space matrix with a preset threshold value, and obtaining the corresponding number of division intervals.

[0078] Specifically, calculate the parameter space matrix Y of all subbands of the lth matrix l The maximum value of (j)ρ max , according to the maximum value, the division range is set to -ρ max ~ρ max , and then the ρ dimension in the parameter space is quantized according to a certain quantization interval Δρ, which is -ρ max ~ρ max Divide into N R interval.

[0079] S123. Set a subband accumulation matrix and a subband mapping relationship matrix according to the number of division intervals, and initialize the subband accumulation matrix and the subband mapping relationship matrix, wherein each element in the subband accumulation matrix and the subband mapping relationship matrix corresponds to a quantized parameter space.

[0080] Specifically, N is set according to the number of division intervals. T ×N R The subband accumulation matrix A l (j), initialize it to 0, and each element corresponds to the quantized parameter space. Similarly, construct N T ×N R Dimensional subband mapping relationship matrix M l (j), initialized to 0, each element of which also corresponds to the quantized parameter space.

[0081] S124. Match all elements in the parameter space matrix with elements in the subband accumulation matrix by column. If any element in the parameter space matrix meets the preset condition, add 1 to the corresponding element of the subband accumulation matrix and store the corresponding element value in the subband mapping relationship matrix.

[0082] Specifically, the parameter space matrix Y l All elements in (j) are accumulated in the matrix A by columns and subbands l (j) corresponds to the elements in the parameter space matrix Y l Any element ρ in (j) k,θr Satisfies the following relationship: Then in the subband accumulation matrix A l (j) adds 1 to the corresponding element [r,k] and replaces [x r ,yr ] is stored in the subband mapping relationship matrix M l (j) in the [r,k]th element.

[0083] Similarly, the above operation is performed on all subbands of all base arrays to obtain N f subband accumulation matrix and subband mapping relationship matrix.

[0084] After the subband accumulation matrix and subband mapping relationship matrix are constructed through the above steps, the subband accumulation matrix within the bandwidth is accumulated twice to obtain the broadband accumulation matrix. At the same time, the corresponding elements in the subband mapping relationship matrix are merged to obtain the broadband mapping relationship matrix. Specifically, the N in the lth basis matrix f Subband accumulation matrix A l (j) are added to obtain the broadband accumulation matrix, and the calculation formula is shown in the following formula (6):

[0085]

[0086] At the same time, the N of the lth matrix f Subband mapping relationship matrix M l The corresponding elements in (j) are merged to obtain the broadband mapping relationship matrix M l Similarly, the above operation is performed on all base matrices to obtain the broadband accumulation matrix and broadband mapping relationship matrix of L base matrices.

[0087] This embodiment detects the straight line features in the extracted target space features through Hough transform, and then distinguishes the valid features from the invalid features through the secondary accumulation of the accumulation matrix, thereby retaining the valid features and eliminating the interference features, eliminating the noise interference items for the subsequent target positioning results, which is conducive to improving the accuracy.

[0088] S130, detecting and acquiring the maximum value in the broadband accumulation matrix, and restoring the value of the data space corresponding to the point where the maximum value is located according to the mapping relationship marked in the broadband mapping relationship matrix, to obtain a broadband peak distribution diagram.

[0089] First, for the lth matrix, detect its broadband accumulation matrix A l The maximum value of all elements in .

[0090] Then, according to the mapping relationship marked in the broadband mapping relationship matrix, the value of the data space corresponding to the point where the maximum value is located is restored. In one embodiment, the specific steps include:

[0091] S131. Construct a recovery matrix with the same dimension as the subband peak distribution matrix, and initialize the recovery matrix.

[0092] Specifically, construct the subband peak distribution matrix PD l (j) The recovery matrix RE of the same dimension l , and initialize it to 0.

[0093] S132, locate and obtain the row and column numbers of the maximum value in the broadband accumulation matrix, extract the elements in the data matrix stored in the broadband mapping relationship matrix corresponding to the row and column numbers, and assign 1 to the elements in the recovery matrix corresponding to the elements.

[0094] Specifically, locate the broadband accumulation matrix A l The row and column numbers of the maximum value of the element in the matrix are obtained, and the elements in the data matrix stored in the corresponding broadband mapping relationship matrix are extracted, and the recovery matrix RE corresponding to these elements is converted to l The elements in are assigned a value of 1, thereby restoring the peak value on the main lobe of the subband.

[0095] Finally, the above operation is performed on L arrays to restore the main lobe peaks of all arrays and obtain a broadband peak distribution diagram.

[0096] In this embodiment, the inverse transformation method can be used to restore only the detected effective features, thereby reducing the background noise of the subsequent cross-structure between multiple arrays and improving the robustness of the system in a low signal-to-noise ratio environment.

[0097] S140, superimposing the broadband peak distribution diagrams of each base array to obtain a multi-array spatial feature cross structure, and determining the target positioning result through the multi-array spatial feature cross structure.

[0098] Specifically, the broadband peak distribution diagrams of L base arrays are added together to obtain the multi-base array spatial feature cross structure, and the calculation formula is shown in the following formula (7):

[0099]

[0100] Then, the maximum value of the element is selected from the multi-array spatial feature cross structure RE, and the coordinates of the maximum value of the element are the target position estimation results.

[0101] In the target spatial feature extraction and fusion positioning method based on Hough transform provided in the embodiment of the present invention, not only the feature fusion method is used to achieve a balance between system performance and communication requirements, but also the Hough transform is used to detect the straight line features in the beam output energy spectrum, and the consistency of different frequencies in the main lobe direction is used to judge the effective features and interference features, retain the effective features, and eliminate the interference features, thereby improving the robustness of the algorithm in a low signal-to-noise ratio environment and greatly improving the accuracy of the target positioning estimation results.

[0102] Figure 2is a schematic diagram of an underwater receiving system and a target position according to an embodiment of the present application, Figure 3 is a schematic diagram of target spatial features extracted by matrix 1 when Hough transform is not used according to an embodiment of the present application, Figure 4 is a schematic diagram of target spatial features extracted by matrix 1 using Hough transform according to an embodiment of the present application, Figure 5 is a schematic diagram of a multi-base array cross structure obtained when the Hough transform is not used according to an embodiment of the present application, Figure 6 is a schematic diagram of a multi-base array cross structure obtained when using Hough transform according to an embodiment of the present application, Figure 7 : This is a schematic diagram of the root mean square error comparison according to the embodiment of the present application. The following takes a typical underwater target positioning scenario as an example to specifically illustrate an application example of the above-mentioned target space feature extraction and fusion positioning method based on Hough transform:

[0103] like Figure 2 As shown in the figure, the receiving system has three 16-element horizontal uniform linear arrays, and the distance between adjacent arrays is 1500 meters. The target position is [5000,0] meters, which is located in the near field of the entire receiving system, but in the far field relative to each receiving array. The center frequency of the broadband signal emitted by the target is 250Hz.

[0104] pass Figure 2 The receiving system shown in the figure performs underwater positioning, obtains the time domain receiving data of each base array element, and processes the obtained data through the method of the present application. The following can be obtained during the processing: Figure 3 The target spatial features extracted by matrix 1 when Hough transform is not used are shown. Figure 4 The target spatial features extracted by matrix 1 using Hough transform are shown as follows, Figure 5 The multi-base inter-array cross structure obtained when the Hough transform is not used is shown, and Figure 6 The cross structure between multiple base arrays obtained when using Hough transform is shown in the figure. Figure 3 and Figure 4 It can be seen that the use of Hough transform can retain effective spatial features, eliminate interfering spatial features, reduce noise interference, and improve positioning accuracy. Figure 5 and Figure 6 It can be seen that after removing the interfering spatial features, the effective features are restored through inverse transformation, and the background noise of the cross-structure between multi-base arrays is significantly reduced.

[0105] Finally, the root mean square error obtained without using Hough transform and with using Hough transform is compared under different signal-to-noise ratio environments. The results are as follows: Figure 7 As shown, it can be clearly found that after using Hough transform, the robustness of the algorithm in low signal-to-noise ratio environment is improved.

[0106] The embodiment of the present invention also provides a target space feature extraction and fusion positioning system based on Hough transform, Figure 8 A schematic block diagram of a target space feature extraction and fusion positioning system based on Hough transform provided in an embodiment of the present invention, such as Figure 8 As shown, the system includes a feature extraction module 810 , a recovery module 820 , and a positioning module 830 .

[0107] The feature extraction module 810 is used to obtain the time domain reception data of each base array element, process the time domain reception data, construct a steering response power spectrum of all sub-bands within each base array bandwidth according to the processed data, and extract the sub-band peak value of the steering response power spectrum to obtain a sub-band peak distribution diagram.

[0108] A data matrix is ​​constructed according to the non-zero points on the sub-band peak distribution diagram, and a Hough transform is performed on the data matrix. A sub-band accumulation matrix and a sub-band mapping relationship matrix are constructed according to the data after the Hough transform, and the sub-band accumulation matrix within the bandwidth is accumulated twice to obtain a broadband accumulation matrix. The corresponding elements in the sub-band mapping relationship matrix are merged to obtain a broadband mapping relationship matrix.

[0109] The recovery module 820 is used to detect and obtain the maximum value in the broadband accumulation matrix, and restore the value of the data space corresponding to the point where the maximum value is located according to the mapping relationship marked in the broadband mapping relationship matrix to obtain a broadband peak distribution diagram.

[0110] The positioning module 830 is used to superimpose the broadband peak distribution diagrams of each base array to obtain a multi-array spatial feature cross structure, and determine the target positioning result through the multi-array spatial feature cross structure.

[0111] Through the above system, not only the robustness of the algorithm in low signal-to-noise ratio environments is improved, but also the accuracy of target positioning estimation results is greatly improved.

[0112] The above-mentioned target space feature extraction and fusion positioning method based on Hough transform can be implemented in the form of a computer program. The computer program can be used in Fig. 9 Runs on the computer device shown.

[0113] See also Fig. 9 , Fig. 9 : is a schematic block diagram of a computer device provided by an embodiment of the present invention. The computer device can be used to execute a target spatial feature extraction and fusion positioning method based on Hough transform to extract underwater target spatial features, and use the spatial features for fusion positioning.

[0114] See also Fig. 9The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a storage medium 503 and an internal memory 504 .

[0115] The storage medium 503 can store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, the processor 502 can execute a target space feature extraction and fusion positioning method based on Hough transform, wherein the storage medium 503 can be a volatile storage medium or a non-volatile storage medium.

[0116] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0117] The internal memory 504 provides an environment for the operation of the computer program 5032 in the storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute the target space feature extraction and fusion positioning method based on Hough transform.

[0118] The network interface 505 is used for network communication, such as providing data information transmission, etc. Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device 500 to which the solution of the present invention is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0119] The processor 502 is used to run the computer program 5032 stored in the memory to implement the corresponding functions in the above-mentioned target space feature extraction and fusion positioning method based on Hough transform.

[0120] Those skilled in the art will understand that Fig. 9 The embodiments of the computer device shown in the figure do not constitute a limitation on the specific composition of the computer device. In other embodiments, the computer device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, in some embodiments, the computer device may only include a memory and a processor. In such embodiments, the structure and function of the memory and the processor are the same as those of the embodiment of the present invention. Fig. 9 The embodiments shown are consistent and will not be described again here.

[0121] It should be understood that in the embodiment of the present invention, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0122] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium may be a volatile or non-volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps included in the above-mentioned target space feature extraction and fusion positioning method based on Hough transform are implemented.

[0123] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the above-described equipment, devices and units can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. Those of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0124] In the several embodiments provided by the present invention, it should be understood that the disclosed equipment, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. Units with the same function may also be combined into one unit. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices or units, or may be an electrical, mechanical or other form of connection.

[0125] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0126] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0127] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a computer-readable storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned computer-readable storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a magnetic disk or an optical disk.

[0128] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A target space feature extraction and fusion positioning method based on Hough transform, characterized in that: The method comprises: Acquire time domain reception data of each base array element, process the time domain reception data, construct a steering response power spectrum of all sub-bands within each base array bandwidth according to the processed data, and extract sub-band peaks of the steering response power spectrum to obtain a sub-band peak distribution diagram; Constructing a data matrix according to the non-zero points on the sub-band peak distribution diagram, performing Hough transform on the data matrix, constructing a sub-band accumulation matrix and a sub-band mapping relationship matrix according to the data after the Hough transform, performing secondary accumulation on the sub-band accumulation matrix within the bandwidth to obtain a broadband accumulation matrix, merging corresponding elements in the sub-band mapping relationship matrix to obtain a broadband mapping relationship matrix; Detecting and acquiring the maximum value in the broadband accumulation matrix, and restoring the value of the data space corresponding to the point where the maximum value is located according to the mapping relationship marked in the broadband mapping relationship matrix, to obtain a broadband peak distribution diagram; The broadband peak distribution diagrams of each base array are superimposed to obtain a multi-base array spatial feature cross structure, and the target positioning result is determined by the multi-base array spatial feature cross structure.

2. The method according to claim 1, characterized in that: The time domain received data is processed, and a steering response power spectrum diagram of all sub-bands within each array bandwidth is constructed based on the processed data, including: Converting the time domain received data into frequency domain data, constructing a frequency domain data vector matrix according to the frequency domain data, and obtaining a sub-band frequency domain sampling covariance matrix through the frequency domain data vector matrix; The observation area is grid-divided according to a preset step size, and the power spectrum of the guidance response of each base array sub-band to each grid point in the observation area is calculated according to the sub-band frequency domain sampling covariance matrix.

3. The method according to claim 1 or 2, characterized in that: Extracting sub-band peak values ​​from the steering response power spectrum to obtain a sub-band peak distribution diagram includes: Constructing a subband peak distribution matrix of each base array subband and initializing the subband peak distribution matrix, wherein the elements in the matrix correspond one-to-one to the grid points in the observation area; The extreme points of the steering response power spectrum are selected according to preset conditions, and the corresponding positions of the selected extreme points on the sub-band peak distribution matrix are assigned a value of 1 to obtain a sub-band peak distribution map.

4. The method according to claim 1, characterized in that Constructing a data matrix according to the non-zero points on the sub-band peak distribution diagram, including: The row and column subscripts corresponding to the non-zero elements in the sub-band peak distribution diagram are arranged in a preset arrangement order to construct a data matrix.

5. The method according to claim 1, characterized in that: Performing Hough transform on the data matrix, and constructing a subband accumulation matrix and a subband mapping relationship matrix according to the data after the Hough transform, including: By means of Hough transform, the data matrix is ​​transformed from the data space to the parameter space to obtain a parameter space matrix; Calculating the maximum value of the parameter space matrix of all sub-bands, setting a division range according to the maximum value, performing quantization division of the parameter space matrix with a preset threshold value, and obtaining a corresponding number of division intervals; Setting a subband accumulation matrix and a subband mapping relationship matrix according to the number of division intervals, and initializing the subband accumulation matrix and the subband mapping relationship matrix, wherein each element in the subband accumulation matrix and the subband mapping relationship matrix corresponds to a quantized parameter space; All elements in the parameter space matrix are matched with elements in the subband accumulation matrix by column. If any element in the parameter space matrix meets the preset condition, 1 is added to the corresponding element of the subband accumulation matrix, and the corresponding element value is stored in the subband mapping relationship matrix.

6. The method according to claim 1, characterized in that According to the mapping relationship marked in the broadband mapping relationship matrix, the value of the data space corresponding to the point where the maximum value is located is restored, including: Constructing a recovery matrix with the same dimension as the subband peak distribution matrix, and initializing the recovery matrix; Locate and obtain the row and column numbers of the maximum value in the broadband accumulation matrix, extract the elements in the data matrix stored in the broadband mapping relationship matrix corresponding to the row and column numbers, and assign 1 to the elements in the recovery matrix corresponding to the elements.

7. The method according to claim 1, characterized in that Determining the target positioning result by the multi-array spatial feature cross structure includes: The maximum value of an element is selected from the multi-array spatial feature intersection structure, and the coordinates of the maximum value of the element are the target position estimation results.

8. A target space feature extraction and fusion positioning system based on Hough transform, characterized in that: The system comprises: The feature extraction module is used to obtain the time domain reception data of each base array element, process the time domain reception data, construct a steering response power spectrum of all sub-bands within each base array bandwidth according to the processed data, and extract the sub-band peak value of the steering response power spectrum to obtain a sub-band peak distribution diagram. Constructing a data matrix according to the non-zero points on the sub-band peak distribution diagram, performing Hough transform on the data matrix, constructing a sub-band accumulation matrix and a sub-band mapping relationship matrix according to the data after the Hough transform, performing secondary accumulation on the sub-band accumulation matrix within the bandwidth to obtain a broadband accumulation matrix, merging corresponding elements in the sub-band mapping relationship matrix to obtain a broadband mapping relationship matrix; A recovery module, used for detecting and obtaining the maximum value in the broadband accumulation matrix, and recovering the value of the data space corresponding to the point where the maximum value is located according to the mapping relationship marked in the broadband mapping relationship matrix, to obtain a broadband peak distribution diagram; The positioning module is used to superimpose the broadband peak distribution diagrams of each base array to obtain a multi-array spatial feature cross structure, and determine the target positioning result through the multi-array spatial feature cross structure.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images