CN116299176A - Target space feature extraction and fusion positioning method based on Hough transformation - Google Patents
Target space feature extraction and fusion positioning method based on Hough transformation Download PDFInfo
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Abstract
The invention discloses a target space feature extraction and fusion positioning method based on Hough transformation, which comprises the following steps: acquiring and processing time domain data of each array element, constructing a pilot response power spectrogram of all sub-bands in each array bandwidth, and extracting sub-band peak values; constructing a data matrix according to non-zero points on a subband peak distribution diagram, carrying out Hough transformation on the data matrix, constructing a subband accumulation matrix and a subband mapping relation matrix, carrying out secondary accumulation on the subband accumulation matrix in the bandwidth to obtain a broadband accumulation matrix, and merging corresponding elements in the subband mapping relation matrix to obtain a broadband mapping relation matrix; detecting the maximum value in the broadband accumulation matrix, and recovering the value corresponding to the point where the maximum value is located according to the mapping relation to obtain a broadband peak distribution diagram; and superposing the broadband peak distribution diagrams of the matrixes to obtain a cross structure, and determining a target positioning result through the cross structure. And by means of feature fusion and Hough transformation, positioning accuracy is improved.
Description
Technical Field
The invention relates to the technical field of underwater acoustic array signal processing, in particular to a target space feature extraction and fusion positioning method based on Hough transformation.
Background
The underwater distributed passive detection positioning mechanism mainly comprises two types, namely a two-step positioning method and a direct positioning method. The principle of the direct positioning method is that the original received data of each array is transmitted to a processing center, and each array is regarded as a related large array to be processed in a centralized way. This positioning method requires that each array be kept synchronized in time when data is sampled, and that the correlation of the received data is high. However, the transmission requirement of such raw sampled data limits the application of the direct localization method in underwater distributed passive detection systems.
The two-step positioning method belongs to decision-level fusion, and the principle is that the intermediate parameters (also called measurement) such as the arrival angle, the arrival time and the like, which are estimated by each observation node, are transmitted to a processing center, and then the estimation result of the target position is obtained through the statistical methods such as a least square method and the like. Although the data transmission amount is small, the method needs to assume that the measurement amount independent of each other is large and the error is small, so that an asymptotic unbiased estimation result can be obtained. Therefore, the method is mostly applied to the fields of radars or large-scale sensor networks and the like. Unlike these fields, the number of sonar platforms that can be cooperatively detected in underwater attack and defense is limited, so that the two-step method cannot achieve ideal positioning performance in an underwater distributed positioning system.
Therefore, in order to solve the problem of poor positioning performance in the underwater distributed detection system in the prior art, a corresponding solution is needed to be proposed.
Disclosure of Invention
The embodiment of the invention provides a method, a system, equipment and a medium for extracting, fusing and positioning target space features based on Hough transformation, which aim to solve the problem of poor positioning performance in an underwater distributed detection system in the prior art.
In a first aspect, an embodiment of the present invention provides a method for extracting, fusing and positioning target spatial features based on hough transform, where the method includes:
acquiring time domain received data of each array element, processing the time domain received data, constructing a guide response power spectrogram of all sub-bands in each array bandwidth according to the processed data, and extracting sub-band peak values of the guide response power spectrogram to obtain a sub-band peak value distribution map;
constructing a data matrix according to non-zero points on the sub-band peak distribution diagram, carrying out Hough transformation on the data matrix, constructing a sub-band accumulation matrix and a sub-band mapping relation matrix according to the Hough transformed data, carrying out secondary accumulation on the sub-band accumulation matrix in a bandwidth to obtain a broadband accumulation matrix, and merging corresponding elements in the sub-band mapping relation matrix to obtain a broadband mapping relation matrix;
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 relation marked in the broadband mapping relation matrix to obtain a broadband peak distribution diagram;
and superposing broadband peak distribution diagrams of the matrixes to obtain a multi-matrix space characteristic crossing structure, and determining a target positioning result through the multi-matrix space characteristic crossing structure.
In a second aspect, an embodiment of the present invention provides a system for extracting and fusing and positioning target spatial features based on hough transform, where the system includes:
the characteristic extraction module is used for obtaining the time domain received data of each array element, processing the time domain received data, constructing and obtaining the guiding response power spectrogram of all the sub-bands in each array bandwidth according to the processed data, extracting sub-band peak values of the guiding response power spectrogram to obtain a sub-band peak value distribution map,
constructing a data matrix according to non-zero points on the sub-band peak distribution diagram, carrying out Hough transformation on the data matrix, constructing a sub-band accumulation matrix and a sub-band mapping relation matrix according to the Hough transformed data, carrying out secondary accumulation on the sub-band accumulation matrix in a bandwidth to obtain a broadband accumulation matrix, and merging corresponding elements in the sub-band mapping relation matrix to obtain a broadband mapping relation matrix;
the recovery module is used for detecting and acquiring 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 positioned according to the mapping relation marked in the broadband mapping relation matrix to obtain a broadband peak distribution diagram;
and the positioning module is used for superposing broadband peak distribution diagrams of the matrixes to obtain a multi-matrix space feature intersection structure, and determining a target positioning result through the multi-matrix space feature intersection structure.
In a third aspect, an embodiment of the present invention further provides a computer apparatus, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the method described in the first aspect.
In a fourth aspect, embodiments of the present invention also provide a computer readable storage medium, wherein the computer readable storage medium stores a computer program, which when executed by a processor causes the processor to perform the method according to the first aspect.
The embodiment of the invention provides a target space feature extraction and fusion positioning method and system based on Hough transformation. Acquiring time domain receiving data of each array element, processing the time domain receiving data, constructing a pilot response power spectrogram of all sub-bands in each array bandwidth according to the processed data, and extracting sub-band peak values of the pilot response power spectrogram to obtain a sub-band peak value distribution map; then constructing a data matrix according to non-zero points on the subband peak distribution diagram, carrying out Hough transformation on the data matrix, constructing a subband accumulation matrix and a subband mapping relation matrix according to the Hough transformed data, carrying out secondary accumulation on the subband accumulation matrix in the bandwidth to obtain a broadband accumulation matrix, and merging corresponding elements in the subband mapping relation matrix to obtain a broadband mapping relation matrix; then, 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 relation marked in the broadband mapping relation matrix to obtain a broadband peak distribution diagram; and finally, superposing broadband peak distribution diagrams of the matrixes to obtain a multi-matrix space characteristic crossing structure, and determining a target positioning result through the multi-matrix space characteristic crossing structure.
Compared with the prior art, the method can provide richer target space position information for the processing center by extracting the inherent acoustic characteristics capable of reflecting different types of targets to perform information fusion, meanwhile, the balance between the system performance and the communication requirement can be realized without original data transmission, and the method is a fusion strategy meeting the actual application requirement of underwater sound. The method also utilizes Hough transformation to detect straight line characteristics in the beam output energy spectrum, judges the effective characteristics and the interference characteristics through the consistency of main lobe directions to different frequencies, retains the effective characteristics and eliminates the interference characteristics, thereby improving the robustness of the algorithm in a low signal-to-noise ratio environment and greatly improving the accuracy of a target positioning estimation result.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a method for extracting and fusing and positioning target spatial features based on hough transform according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an underwater receiving system and a target location according to an embodiment of the present application;
fig. 3 is a schematic diagram of a target spatial feature extracted by the matrix 1 when hough transform is not used according to an embodiment of the present application;
fig. 4 is a schematic diagram of the target spatial features extracted by the matrix 1 when using hough transform according to an embodiment of the present application;
fig. 5 is a schematic diagram of a multi-matrix cross structure obtained when hough transform is not used according to an embodiment of the present application;
fig. 6 is a schematic diagram of a multi-matrix cross structure obtained when hough transform is used according to an embodiment of the present application;
FIG. 7 is a root mean square error comparison diagram according to an embodiment of the present application;
fig. 8 is a schematic block diagram of a target spatial feature extraction and fusion positioning system based on hough transform according to an embodiment of the present invention;
fig. 9 is a schematic block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
The embodiment provides a method for extracting and fusing target spatial features based on hough transform, and fig. 1 is a flow chart of the method for extracting and fusing target spatial features based on hough transform, as shown in fig. 1, and the method comprises steps S110 to S140.
S110, obtaining time domain received data of each array element, processing the time domain received data, constructing a pilot response power spectrogram of all sub-bands in each array bandwidth according to the processed data, and extracting sub-band peak values of the pilot response power spectrogram to obtain a sub-band peak value distribution map.
Firstly, acquiring time domain receiving data of each array element of each array in an underwater distributed detection system;
and then, carrying out data processing on the acquired time domain received data, and constructing a pilot response power spectrogram of all the sub-bands in each matrix bandwidth according to the processed data. In one embodiment, the method specifically includes:
s111, converting the acquired time domain received data into frequency domain data.
Specifically, assuming that the distributed system includes L receiving matrices, the first (1. Ltoreq.l) matrix contains N l The time domain received signal of the ith array element is marked as x l,i . Let the array element in the observation time be at the sampling frequency f s The obtained time domain sampling point number is T, and the time domain receiving signal is divided into N by 50% overlapping rate sec Segments, each segment having T sec A number of sampling points are used to sample the sample,
wherein T is sec Parameters are customized for the user.
For each segment length T sec T is performed on time domain sampled data of (1) sec Point fast Fourier transform, then frequency resolution F s ＝f s /T sec . Assume that the center frequency of the time domain received signal is f c The bandwidth is B, the lower limit frequency is f l ＝f c -B/2, upper limit frequency f h ＝f c +B/2. The number of the sub-band corresponding to the lower limit frequency is
Sub-band number corresponding to upper limit frequency
Wherein []The representation is rounded to the center, and the total number of subbands to be considered is N, as known from the upper and lower frequency ranges f ＝B/F s +1。
The segmented time domain received signal of the kth segment is recorded as
The time domain receiving signal is subjected to frequency domain conversion, and a specific calculation formula is shown as the following formula (1):
wherein, the liquid crystal display device comprises a liquid crystal display device,
the spectrum of the time domain received signal on the jth subband is segmented for the kth segment, n is the sampling point sequence number,
s112, constructing a frequency domain data vector matrix according to the frequency domain data.
Specifically, N is sec Spectrum of the individual segmented time domain signals on the j-th subband is extracted and according to segment number 1,2 sec Is placed in the order of columns in the spectral line vector X l,i (j) In, i.e
The same operation is carried out on all the sub-bands in the signal bandwidth, so that N can be obtained f 1 XN sec Dimension vector->
N for the first matrix l The time domain received signals of each array element are subjected to frequency domain conversion processing in step S111, and N is determined l Spectral row vector { X } of each array element on the jth subband l,i (j),1≤i≤ N l 1,2 in matrix number order, & gt, l, & gt, N l Sequentially arranging according to rows to obtain a frequency domain data vector matrix of the first matrix on the jth subband
Wherein [ (S)] T Representing the transpose. The dimension of the frequency domain data vector matrix is N l ×N sec Each column corresponds to a spectrum on a j-th subband obtained from the k-th segment of time-domain signal data.
Finally, the L matrixes are subjected to the operation, and L frequency domain data vector matrixes { X } can be finally obtained l (j),1≤l≤L}。
S113, constructing a subband frequency domain sampling covariance matrix through a frequency domain data vector matrix.
Specifically, the frequency domain data of the jth sub-band of the ith matrix is utilized to construct a sub-band frequency domain sampling covariance matrix of the ith matrix, and the construction formula is as follows (2):
wherein [ (S)] H Representing the conjugate transpose.
Similarly, N in bandwidth can be constructed f Sampling covariance matrix of sub-band.
And S114, meshing an observation area according to a preset step length, and calculating a guiding response power spectrogram of each base matrix sub-band to each grid point in the observation area according to the sub-band frequency domain sampling covariance matrix.
Specifically, the observation area G is meshed along the x coordinate axis (east-west direction) and the y coordinate axis (north-south direction) according to a certain step distance Δ, wherein the calculation amount and the algorithm precision need to be comprehensively considered in step selection, the calculation amount is smaller as the step is larger, but the algorithm precision is lower, so that the step size needs to be set according to actual conditions.
From the subband-frequency domain sampling covariance matrix, the j-th subband of the first matrix is calculated, and the Capon beamformer is used for the G-th grid point r in the observation area G g ＝[x g ,y g ] T The guided response power spectrum of (2) is calculated as shown in the following formula (3):
wherein, the liquid crystal display device comprises a liquid crystal display device,
an inverse matrix, a, representing the j-th subband sample covariance matrix l (j，r g ) A steering vector representing the g-th grid point in the observation area,/->
f j Frequency value representing the jth subband, +.>
Representing the signal from grid point r g Propagation delay to the i-th element of the i-th matrix, i=1,.. l C=1500m/s is the sound velocity in water.
Traversing all grid points in the observation area G, and calculating to obtain a guide response power spectrum { P ] of the jth sub-band of the ith matrix l (j,r g ),r g E G }. Similarly, N of L matrixes can be obtained by performing the above operation on all sub-bands of all matrixes f The pilot response power spectrum of each subband.
After the pilot response power spectrograms of all the sub-bands in each matrix bandwidth are obtained through the calculation in the steps, sub-band peak extraction is performed on the obtained pilot response power spectrograms, and in an embodiment, the specific implementation steps include:
s115, constructing a sub-band peak value distribution matrix of each base matrix sub-band, and initializing the sub-band peak value distribution matrix, wherein elements in the matrix correspond to grid points in the observation area one by one.
Specifically, the sub-band peak distribution matrix PD of the jth sub-band of the ith matrix is constructed l (j) And initialized to 0, the elements of the matrix correspond one-to-one to grid points within the observation area G.
S116, selecting extreme points of the guide response power spectrogram according to preset conditions, and assigning 1 to the corresponding positions of the selected extreme points on the sub-band peak distribution matrix to obtain a sub-band peak distribution map.
Specifically, the pilot response power spectrum { P } of the jth subband from the ith matrix l (j,r g ),r g E, selecting all extreme points meeting preset conditions from G, wherein the preset conditions comprise: the point with the value greater than or equal to the value of the peripheral 8 points is the extreme point which meets the condition. The selected extreme points are distributed in the sub-band peak value distribution matrix PD l (j) And assigning 1 to the corresponding position on the sub-band peak distribution map.
Similarly, peak value extraction is carried out on all sub-bands of all matrixes to obtain N of L matrixes f Peak profile of the sub-band.
S120, constructing a data matrix according to non-zero points on the subband peak distribution diagram, carrying out Hough transformation on the data matrix, constructing a subband accumulation matrix and a subband mapping relation matrix according to the Hough transformed data, carrying out secondary accumulation on the subband accumulation matrix in the bandwidth to obtain a broadband accumulation matrix, and merging corresponding elements in the subband mapping relation matrix to obtain the broadband mapping relation matrix.
Constructing a data matrix according to non-zero point on the peak distribution diagram of the sub-band, specifically, constructing the peak distribution diagram PD of the j sub-band of the first matrix l (j) The row and column subscripts corresponding to the non-zero elements in the data matrix D are arranged in a preset arrangement sequence l (j) Is a kind of medium. The preferred ordering method is as follows: slave matrix PD l (j) The first element starts to search by column, if the (x, y) coordinate value of the observation area corresponding to the first non-zero element is [ x ] 1 ,y 1 ]Matrix D l (j) Column 1 elements are x respectively 1 And y 1 。
Hypothesis matrix PD l (j) K non-zero points in total, then data matrix
The dimension is 2 xk.
By the method, N of L matrixes are respectively constructed f A data matrix of subbands.
Then, carrying out Hough transformation on the constructed data matrix, and constructing a subband accumulation matrix and a subband mapping relation matrix according to the Hough transformed data. In one embodiment, the method specifically includes:
s121, transforming the data matrix from the data space to the parameter space through Hough transformation to obtain a parameter space matrix.
Specifically, assuming that the observation area is a data space, the data matrix D is transformed by the transformation matrix l (j) The elements in (a) are subjected to Hough transformation, and are transformed from a data space x-y (observation area) to a parameter space rho-theta, and a transformation matrix is shown in the following formula (4):
wherein θ 1 ,θ 2 ,…,
The discrete angle value obtained after uniformly quantizing the parameter theta of the parameter space is expressed in radian. In practical application, the theta is generally 0:delta theta and pi-delta theta. It should be noted that, the calculation amount and the algorithm precision need to be considered simultaneously in the selection of the parameter space quantization interval, the larger the quantization interval is, the smaller the calculation amount is, but the lower the algorithm precision is, so the selection needs to be performed according to the actual situation.
Realizing Hough transformation through matrix multiplication to obtain a transformed parameter space matrix Y l (j) The calculation formula is shown as the following formula (5):
it follows that the parameter space matrix Y l (j) Any element of
S122, calculating the maximum value of the parameter space matrix of all the subbands, setting a dividing range according to the maximum value, and carrying out quantization division of a preset threshold value on the parameter space matrix to obtain a corresponding division interval number.
Specifically, the parameter space matrix Y of all sub-bands of the first matrix is calculated l (j) Maximum value ρ of (1) max Setting the dividing range as- ρ according to the maximum value max ～ρ max Then the rho dimension in the parameter space is quantized and divided according to a certain quantization interval delta rho, which is-rho max ～ρ max Divided into N R Intervals.
S123, setting a subband accumulation matrix and a subband mapping relation matrix according to the dividing interval number, and initializing the subband accumulation matrix and the subband mapping relation matrix, wherein each element in the subband accumulation matrix and the subband mapping relation matrix corresponds to the quantized parameter space.
Specifically, N is set according to the number of dividing intervals T ×N R Subband accumulation matrix A of dimension l (j) It is initialized to 0, with each element corresponding to the quantized parameter space. Similarly, construct N T ×N R Subband mapping relation matrix M of dimension l (j) It is initialized to 0 and each of its elements also corresponds to the quantized parameter space.
S124, all elements in the parameter space matrix are corresponding to elements in the subband accumulation matrix according to columns, 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 corresponding element values are stored in the subband mapping relation matrix.
Specifically, the parameter space matrix Y l (j) All elements in the list and subWith accumulation matrix A l (j) The elements in (3) are corresponding. If the parameter space matrix Y l (j) Any element ρ k,θr The following relationship is satisfied:
matrix a is accumulated in the subband l (j) Corresponding element [ r, k ]]Add 1 and add [ x ] r ,y r ]Stored in subband mapping relation matrix M l (j) [ r, k ]]Of the elements.
Similarly, N of L matrixes can be obtained by performing the above operation on all sub-bands of all matrixes f A subband accumulation matrix and a subband mapping relationship matrix.
After constructing the sub-band accumulation matrix and the sub-band mapping relation matrix through the steps, carrying out secondary accumulation on the sub-band accumulation matrix in the bandwidth to obtain the broadband accumulation matrix, and simultaneously merging corresponding elements in the sub-band mapping relation matrix to obtain the broadband mapping relation matrix. Specifically, N in the first matrix f Subband accumulating matrix A l (j) Adding to obtain a broadband accumulation matrix, wherein the calculation formula is shown in the following formula (6):
at the same time, N of the first matrix f Subband mapping relation matrix M l (j) Corresponding elements in the two are combined to obtain a broadband mapping relation matrix M l . And similarly, performing the operation on all the matrixes to obtain broadband accumulation matrixes and broadband mapping relation matrixes of the L matrixes.
In the embodiment, the linear characteristics in the extracted target space characteristics are detected through Hough transformation, and then the effective characteristics and the ineffective characteristics in the extracted target space characteristics are distinguished through secondary accumulation of the accumulation matrix, so that the effective characteristics are reserved, the interference characteristics are removed, noise interference items are removed for the subsequent target positioning results, and the improvement of accurate determination is facilitated.
S130, 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 relation marked in the broadband mapping relation matrix to obtain a broadband peak distribution diagram.
First, for the first matrix, the wideband accumulation matrix A is detected l Maximum of all elements in (a).
And then, recovering the value of the data space corresponding to the point where the maximum value is located according to the marked mapping relation in the broadband mapping relation matrix. In one embodiment, the specific steps include:
s131, constructing a recovery matrix with the same dimension as the subband peak distribution matrix, and initializing the recovery matrix.
Specifically, the subband peak distribution matrix PD is constructed l (j) Recovery matrix RE of the same dimension l And initializes it to 0.
S132, locating and acquiring a row and column sequence number of the maximum value in the broadband accumulation matrix, extracting elements in a data matrix stored in the broadband mapping relation matrix corresponding to the row and column sequence number, and assigning 1 to the elements in the recovery matrix corresponding to the elements.
Specifically, to wideband accumulation matrix A l The row and column serial number of the maximum value of the elements in the matrix, extracting the elements in the data matrix stored in the corresponding broadband mapping relation matrix, and restoring the matrix RE corresponding to the elements l The element in (2) is assigned a value of 1 to recover the peak on the main lobe of the subband.
And finally, carrying out the operation on the L matrixes, and recovering the main lobe peak values of all the matrixes to obtain a broadband peak value distribution diagram.
In the embodiment, only the detected effective features can be recovered by the inverse transformation method, so that the background noise of a cross structure among the subsequent multi-array can be reduced, and the robustness of the system in a low signal-to-noise ratio environment is improved.
And S140, superposing broadband peak distribution graphs of the matrixes to obtain a multi-matrix space feature cross structure, and determining a target positioning result through the multi-matrix space feature cross structure.
Specifically, the broadband peak distribution diagrams of the L matrixes are added to obtain a multi-matrix space characteristic crossing structure, and a calculation formula is shown in the following formula (7):
and then selecting an element maximum value from the multi-matrix space feature crossing structure RE, wherein the coordinate where the element maximum value is located is the target position estimation result.
In the target space feature extraction and fusion positioning method based on the Hough transform, which is provided by the embodiment of the invention, not only is the balance between the system performance and the communication requirement realized by adopting a feature fusion mode, but also the straight line features in the beam output energy spectrum are detected by using the Hough transform, the effective features and the interference features are judged through the consistency of the main lobe directions to different frequencies, the effective features are reserved, the interference features are removed, so that the robustness of the algorithm in a low signal-to-noise ratio environment is improved, and the accuracy of the target positioning estimation result is greatly improved.
Fig. 2 is a schematic view of a certain underwater receiving system and a target position according to an embodiment of the present application, fig. 3 is a schematic view of a target space feature extracted by the matrix 1 when hough transform is not used according to an embodiment of the present application, fig. 4 is a schematic view of a target space feature extracted by the matrix 1 when hough transform is used according to an embodiment of the present application, fig. 5 is a schematic view of a multi-matrix cross structure obtained when hough transform is not used according to an embodiment of the present application, fig. 6 is a schematic view of a multi-matrix cross structure obtained when hough transform is not used according to an embodiment of the present application, and fig. 7 is a root mean square error comparison schematic view according to an embodiment of the present application. An application example of the above-mentioned target space feature extraction and fusion positioning method based on hough transform is specifically described below by taking a typical underwater target positioning scene as an example:
as shown in fig. 2, the receiving system has a total of 3 16-element horizontal uniform linear arrays, and the distance between adjacent arrays is 1500 meters. The target location is [5000,0] meters, which is located in the near field of the overall receiving system, but in the far field with respect to each receiving array. The center frequency of the wideband signal transmitted by the target is 250Hz.
The underwater positioning is carried out through the receiving system shown in fig. 2, the time domain receiving data of each array element is obtained, the obtained data is processed through the method, and the target space characteristics extracted by the array 1 when the Hough transformation is not used, the target space characteristics extracted by the array 1 when the Hough transformation is used, shown in fig. 4, the multi-array cross structure obtained when the Hough transformation is not used, shown in fig. 5, and the multi-array cross structure obtained when the Hough transformation is used are obtained in the processing process. As can be seen by comparing fig. 3 and fig. 4, the hough transform is used to retain effective spatial features, reject interference spatial features, reduce noise interference, and improve positioning accuracy. Comparing fig. 5 and fig. 6, it can be known that after eliminating the interference space features, the effective features are recovered by inverse transformation, so that the background noise of the multi-matrix cross structure is obviously reduced.
Finally, the root mean square errors obtained under the two methods of Hough transformation and Hough transformation are compared under different signal-to-noise environments, and the obtained results are shown in fig. 7, so that it is obvious that after Hough transformation is used, the robustness of the algorithm under the low signal-to-noise environment is improved.
The embodiment of the invention also provides a system for extracting and fusing target spatial features based on Hough transform, and fig. 8 is a schematic block diagram of the system for extracting and fusing target spatial features based on Hough transform, which is provided by the embodiment of the invention, as shown in fig. 8, and comprises a feature extraction module 810, a recovery module 820 and a positioning module 830.
The feature extraction module 810 is configured to obtain time domain received data of each array element, process the time domain received data, construct a pilot response power spectrum of all sub-bands in each array bandwidth according to the processed data, extract sub-band peak values of the pilot response power spectrum, obtain a sub-band peak value distribution map,
constructing a data matrix according to non-zero points on a subband peak distribution diagram, carrying out Hough transformation on the data matrix, constructing a subband accumulation matrix and a subband mapping relation matrix according to the Hough transformed data, carrying out secondary accumulation on the subband accumulation matrix in the bandwidth to obtain a broadband accumulation matrix, and merging corresponding elements in the subband mapping relation matrix to obtain the broadband mapping relation matrix.
And the recovery module 820 is configured to detect and acquire a maximum value in the wideband accumulation matrix, and recover a value of a data space corresponding to a point where the maximum value is located according to a mapping relationship marked in the wideband mapping relationship matrix, so as to obtain a wideband peak distribution diagram.
And the positioning module 830 is configured to superimpose the broadband peak distribution graphs of the matrixes to obtain a multi-matrix spatial feature intersection structure, and determine a target positioning result according to the multi-matrix spatial feature intersection structure.
By the system, the robustness of the algorithm in a low signal-to-noise ratio environment is improved, and the accuracy of the target positioning estimation result is greatly improved.
The above-described method for extracting and fusing spatial features of a target based on hough transform may be implemented in the form of a computer program, which may be run on a computer device as shown in fig. 9.
Referring to fig. 9, fig. 9 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device may be configured to perform a hough transform-based target spatial feature extraction and fusion localization method to extract underwater target spatial features and perform fusion localization using the spatial features.
With reference to FIG. 9, the computer device 500 includes a processor 502, a memory, and a network interface 505, which are connected by a system bus 501, wherein the memory may include a storage medium 503 and an internal memory 504.
The storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, may cause the processor 502 to perform a hough transform-based target spatial feature extraction and fusion localization method, wherein the storage medium 503 may be a volatile storage medium or a non-volatile storage medium.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the execution of a computer program 5032 in the storage medium 503, which computer program 5032, when executed by the processor 502, causes the processor 502 to perform the hough transform-based target spatial feature extraction and fusion localization method.
The network interface 505 is used for network communication, such as providing for transmission of data information, etc. It will be appreciated by those skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting of the computer device 500 to which the present inventive arrangements may be implemented, as a particular computer device 500 may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
The processor 502 is configured to run a computer program 5032 stored in a memory, so as to implement the corresponding functions in the above-mentioned hough transform-based target spatial feature extraction and fusion positioning method.
Those skilled in the art will appreciate that the embodiment of the computer device shown in fig. 9 is not limiting of the specific construction of the computer device, and in other embodiments, the computer device may include more or less components than those shown, or certain components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may include only a memory and a processor, and in such embodiments, the structure and function of the memory and the processor are consistent with the embodiment shown in fig. 9, and will not be described again.
It should be appreciated that in an embodiment of the invention, the processor 502 may be a central processing unit (Central Processing Unit, CPU), the processor 502 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a volatile or nonvolatile computer readable storage medium. The computer readable storage medium stores a computer program, wherein the computer program when executed by a processor implements the steps included in the above-mentioned method for extracting and fusing the spatial features of the object based on the hough transform.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus, device and unit described above may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein. Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the units is merely a logical function division, there may be another division manner in actual implementation, or units having the same function may be integrated into one unit, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present invention.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention is essentially or part of what contributes to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a computer-readable storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. The method for extracting, fusing and positioning the target space features based on Hough transformation is characterized by comprising the following steps:
acquiring time domain received data of each array element, processing the time domain received data, constructing a guide response power spectrogram of all sub-bands in each array bandwidth according to the processed data, and extracting sub-band peak values of the guide response power spectrogram to obtain a sub-band peak value distribution map;
constructing a data matrix according to non-zero points on the sub-band peak distribution diagram, carrying out Hough transformation on the data matrix, constructing a sub-band accumulation matrix and a sub-band mapping relation matrix according to the Hough transformed data, carrying out secondary accumulation on the sub-band accumulation matrix in a bandwidth to obtain a broadband accumulation matrix, and merging corresponding elements in the sub-band mapping relation matrix to obtain a broadband mapping relation matrix;
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 relation marked in the broadband mapping relation matrix to obtain a broadband peak distribution diagram;
and superposing broadband peak distribution diagrams of the matrixes to obtain a multi-matrix space characteristic crossing structure, and determining a target positioning result through the multi-matrix space characteristic crossing structure.
2. The method of claim 1, wherein processing the time-domain received data and constructing a pilot response power spectrum for all subbands in each matrix bandwidth based on the processed data comprises:
converting the time domain received data into frequency domain data, constructing a frequency domain data vector matrix according to the frequency domain data, and constructing a subband frequency domain sampling covariance matrix through the frequency domain data vector matrix;
and dividing the observation area into grids according to a preset step length, and calculating a guiding response power spectrogram of each base matrix sub-band to each grid point in the observation area according to the sub-band frequency domain sampling covariance matrix.
3. The method according to claim 1 or 2, wherein sub-band peak extraction is performed on the pilot response power spectrum to obtain a sub-band peak distribution map, comprising:
constructing a sub-band peak value distribution matrix of each basic array sub-band, and initializing the sub-band peak value distribution matrix, wherein elements in the matrix correspond to grid points in an observation area one by one;
and selecting extreme points according to preset conditions, and assigning 1 to the corresponding positions of the selected extreme points on the sub-band peak distribution matrix to obtain a sub-band peak distribution diagram.
4. The method of claim 1, wherein constructing a data matrix from non-zero points on the subband peak profile comprises:
and arranging row and column subscripts corresponding to the non-zero elements in the sub-band peak distribution diagram according to a preset arrangement sequence, and constructing to obtain a data matrix.
5. The method of claim 1, wherein performing a hough transform on the data matrix and constructing a subband accumulation matrix and a subband mapping relationship matrix from the hough transformed data comprises:
transforming the data matrix from a data space to a parameter space through Hough transformation to obtain a parameter space matrix;
calculating the maximum value of the parameter space matrix of all the sub-bands, setting a dividing range according to the maximum value, and carrying out quantization division of a preset threshold value on the parameter space matrix to obtain a corresponding dividing interval number;
setting a sub-band accumulation matrix and a sub-band mapping relation matrix according to the dividing interval number, and initializing the sub-band accumulation matrix and the sub-band mapping relation matrix, wherein each element in the sub-band accumulation matrix and the sub-band mapping relation matrix corresponds to the quantized parameter space;
and (3) corresponding all elements in the parameter space matrix with elements in the subband accumulation matrix according to columns, adding 1 to the corresponding elements of the subband accumulation matrix if any element in the parameter space matrix meets a preset condition, and storing corresponding element values into the subband mapping relation matrix.
6. The method according to claim 1, wherein recovering the value of the data space corresponding to the point where the maximum value is located according to the mapping relation marked in the broadband mapping relation matrix, comprises:
constructing a recovery matrix with the same dimension as the subband peak distribution matrix, and initializing the recovery matrix;
and locating and acquiring a row and column serial number of the maximum value in the broadband accumulation matrix, extracting elements in a data matrix stored in a broadband mapping relation matrix corresponding to the row and column serial number, and assigning 1 to the elements in a recovery matrix corresponding to the elements.
7. The method of claim 1, wherein determining target positioning results from the multi-matrix spatial signature intersection structure comprises:
and selecting an element maximum value from the multi-matrix space characteristic cross structure, wherein the coordinate where the element maximum value is located is the target position estimation result.
8. A hough transform-based target spatial feature extraction and fusion positioning system, the system comprising:
the characteristic extraction module is used for obtaining the time domain received data of each array element, processing the time domain received data, constructing and obtaining the guiding response power spectrogram of all the sub-bands in each array bandwidth according to the processed data, extracting sub-band peak values of the guiding response power spectrogram to obtain a sub-band peak value distribution map,
constructing a data matrix according to non-zero points on the sub-band peak distribution diagram, carrying out Hough transformation on the data matrix, constructing a sub-band accumulation matrix and a sub-band mapping relation matrix according to the Hough transformed data, carrying out secondary accumulation on the sub-band accumulation matrix in a bandwidth to obtain a broadband accumulation matrix, and merging corresponding elements in the sub-band mapping relation matrix to obtain a broadband mapping relation matrix;
the recovery module is used for detecting and acquiring 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 positioned according to the mapping relation marked in the broadband mapping relation matrix to obtain a broadband peak distribution diagram;
and the positioning module is used for superposing broadband peak distribution diagrams of the matrixes to obtain a multi-matrix space feature intersection structure, and determining a target positioning result through the multi-matrix space feature intersection structure.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the method according to any of claims 1 to 7.
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