CN114781152B - Pseudo-target radiation source distinguishing method and system based on array factor characteristics - Google Patents
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Abstract
The invention discloses a pseudo target radiation source distinguishing method and system based on array factor characteristics, and belongs to the field of array signal processing and source positioning. Comprising the following steps: reconstructing an image of the target radiation source; constructing a distance matrix from each source point to other source points; establishing a multi-antenna array factor model diagram; determining a side lobe point with the maximum relative intensity in a certain error range on an array factor model diagram, taking the side lobe point as a suspicious source point, and determining the position information and the distribution characteristics of the suspicious source point in a model; and comparing the actual position information with the position information and the distribution characteristics obtained in the model diagram on the reconstructed target radiation source image, and finally determining the specific position of the pseudo target radiation source point on the reconstructed target radiation source image. The invention solves the detection and processing problems of false source points generated by large fluctuation range of the intensity of the interference source, reduces the false alarm probability of the radiation interference source detection in the discrete multi-target image, and has higher robustness.
Description
Technical Field
The invention belongs to the field of array signal processing and source positioning, and particularly relates to a pseudo target radiation source distinguishing method and system based on array factor characteristics.
Background
Radio frequency interference is an important factor affecting the scientificity and practicability of satellite-borne/airborne remote sensing data, and can restrict remote sensing application and development of geophysical parameter inversion, microwave imaging, target detection and the like. Whether the interference effect of radio frequency is relieved by coordinating the international electricity with local management to shut down an illegal target radiation source or directly processing polluted remote sensing data, the position information of the radiation source is generally required to be utilized.
In the prior art, the existing target radiation source detection method has various defects, such as incapability of detecting and positioning by mixing strong sources and weak sources, insufficient resolution of adjacent interference sources, incapability of detecting and positioning by detecting a position aggregation source and the like. Particularly, under the condition of mixing strong and weak sources, the positioning, judging and processing of the pseudo-target radiation source are the problems to be solved urgently, for example, the existing RFI detection algorithm usually sets a threshold value as a judgment standard, the false positive point cannot be detected in the mode, the weak source can be covered, and the precision of accurate measurement is not high.
Disclosure of Invention
Aiming at the defects and improvement demands of the prior art, the invention provides a pseudo-target radiation source distinguishing method and system based on array factor characteristics, and aims to screen out the pseudo-target radiation source under the condition of mixing strong and weak sources, so as to improve the detection precision.
To achieve the above object, according to one aspect of the present invention, there is provided a pseudo-target radiation source discriminating method based on array factor characteristics, including:
reconstructing an image of the target radiation source;
traversing each pixel point in a reconstructed image, determining a maximum value of each source point in the reconstructed image and the position of the maximum value, taking the position of the maximum value as the position of the source point, and constructing a distance matrix D from each source point to other source points;
establishing a multi-antenna array factor model diagram;
Determining a side lobe point with the maximum relative intensity in a certain error range on an array factor model diagram, taking the side lobe point as a suspicious source point, and determining the position information and the distribution characteristics of the suspicious source point in a model; calculating the distance d t from the suspicious source point to the main valve according to the position information and the position information of the main valve in the model, wherein t is a positive integer;
Traversing each column in the distance matrix D for each D t on the reconstructed image, and marking a source point with a distance within the error range of D t as a suspicious source point of the source point;
counting the distribution characteristics of suspicious source points corresponding to each source point, and determining the suspicious source points as pseudo-target radiation source points corresponding to the source points when the distribution characteristics are consistent with the distribution characteristics in the array factor model, so as to obtain a positioning result of the pseudo-target radiation source points.
Further, by obtaining a difference correlation array DIFFERENCE COARRAY of the original sparse array of multiple antennas, the multiple antenna array factor model map is constructed from the distribution of the difference correlation array DIFFERENCE COARRAY.
Further, a convex optimization algorithm is employed to acquire an image of the reconstructed target radiation source.
Further, the step of reconstructing an image of the target radiation source comprises:
step S1, acquiring a covariance matrix of a multi-antenna original sparse array, and carrying out redundant averaging and vectorization on the covariance matrix to construct a differential correlation array DIFFERENCE COARRAY signal receiving model of the original sparse array;
S2, dividing the incoming wave direction of the target radiation source into a network by taking the difference correlation array DIFFERENCE COARRAY of the original sparse array and the spatial domain sparse characteristic of the target radiation source as constraint conditions to obtain an overcomplete dictionary;
Step S3, expanding the model in the step S1 into a DIFFERENCE COARRAY-based sparse reconstruction model of the target radiation source based on the overcomplete dictionary;
And S4, solving the model by adopting a re-weighting l 1 norm algorithm to obtain an image of the reconstructed target radiation source.
Further, in the weighted l 1 norm algorithm, the calculation weight of the m element of the k+1st solution vector is:
Wherein, Representing the partitioned network, k is the number of iterations and e represents a positive parameter of algorithm robustness.
Further, after the positioning result of the pseudo-target radiation source point is obtained, the method further comprises complete attenuation or partial attenuation of the pseudo-target radiation source point.
According to another aspect of the present invention, there is provided a pseudo-target radiation source discrimination system based on array factor characteristics, comprising:
An image reconstruction module of the target radiation source, which is used for reconstructing an image of the target radiation source;
The distance matrix construction module is used for traversing each pixel point in the reconstructed image, determining the maximum value of each source point in the reconstructed image and the position of the maximum value, taking the position of the maximum value as the position of the source point, and constructing a distance matrix D from each source point to other source points;
the multi-antenna array factor model building module is used for building a multi-antenna array factor model diagram;
The position confirmation module of the suspicious source point on the model diagram is used for determining a side lobe point with the maximum relative strength in a certain error range on the array factor model diagram, taking the side lobe point as the suspicious source point and determining the position information and the distribution characteristics of the suspicious source point in the model; calculating the distance d t from the suspicious source point to the main valve according to the position information and the position information of the main valve in the model, wherein t is a positive integer;
The position confirmation module of the suspicious source point on the reconstructed image is used for traversing each column in the distance matrix D aiming at each D t in the reconstructed image, and marking the source point with the distance within the error range of D t as the suspicious source point of the source point;
and the pseudo-target radiation source point determining module is used for counting the distribution characteristics of the suspicious source points corresponding to each source point, and determining the suspicious source points as the pseudo-target radiation source points corresponding to the source points when the distribution characteristics are consistent with the distribution characteristics in the array factor model, so as to obtain the positioning result of the pseudo-target radiation source points.
Further, the multi-antenna array factor model building module builds a multi-antenna array factor model graph according to the distribution of the difference correlation array DIFFERENCE COARRAY by obtaining the difference correlation array DIFFERENCE COARRAY of the original sparse array of the multi-antennas.
Further, the image reconstruction module of the target radiation source acquires the reconstructed image of the target radiation source by adopting a convex optimization algorithm.
Further, the device also comprises an image processing module for completely attenuating or partially attenuating the pseudo target radiation source points.
In general, through the above technical solutions conceived by the present invention, the following beneficial effects can be obtained:
(1) The method comprises the steps of establishing a multi-antenna array factor model diagram, finding out the highest side lobe point on the array factor model diagram, taking the side lobe point as a suspicious source point, determining the position information and characteristic distribution of the side lobe point on the array factor model diagram, comparing the actual position information with the position information and the distribution characteristic obtained in the model diagram on a reconstructed target radiation source image, and finally determining the specific position of a pseudo target radiation source point on the reconstructed target radiation source image. Compared with the existing detection mode, the method solves the detection and processing problems of false source points generated by large fluctuation range of the intensity of the interference source, reduces the false alarm probability of the detection of the radiation interference source in the discrete multi-target image, improves the observability of the inversion image, and has high application value.
(2) Furthermore, when reconstructing the image of the target radiation source, the invention adopts a heavy weighting l 1 norm algorithm, can enhance the signal intensity of the radiation source, simultaneously inhibit background noise, further improve the detection and positioning accuracy, can be applied to the field of signal detection, and solves the problems that the resolution of the adjacent interference source is insufficient and the position aggregation source cannot be detected and positioned.
In summary, the method for detecting the target radiation source improves the robustness of the method, screens out the pseudo target radiation source under the condition of mixing strong and weak sources, improves the detection precision, and has high application value.
Drawings
FIG. 1 is a schematic diagram of a method for discriminating a pseudo target radiation source based on array factor characteristics.
FIG. 2 is a schematic diagram of an original sparse array in an embodiment of the present invention.
FIG. 3 is a schematic diagram of an original sparse array DIFFERENCE COARRAY in an embodiment of the present invention.
Fig. 4 is a schematic diagram of element value distribution of a redundant mark matrix according to an embodiment of the present invention.
Fig. 5 is an image of a target radiation source reconstructed in an embodiment of the invention.
FIG. 6 is a diagram of an array factor model in an embodiment of the invention.
Fig. 7 is a schematic diagram of the position of the highest sidelobe in the embodiment of the present invention.
Fig. 8 is a diagram of the detection and positioning result of the actually measured target radiation source after the image feature matching process in the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
As shown in fig. 1, the method for discriminating the pseudo target radiation source based on the array factor features provided by the invention comprises the following steps:
reconstructing an image of the target radiation source;
Traversing each pixel point in the reconstructed target radiation source image, finding the maximum value and the position of each source point in the image, regarding the position of the maximum value as the position (x i,yi) of the source point, and constructing a distance matrix D from each source point to other source points:
Wherein D ij represents the coordinate distance between the i-th and j-th source points, i=1, 2,3.
Establishing an antenna array factor model of the multi-sensor system: acquiring a difference correlation array DIFFERENCE COARRAY of a multi-antenna original sparse array, and constructing an antenna array factor model diagram according to the distribution of the difference correlation array DIFFERENCE COARRAY;
Determining suspicious source point position information on an array factor model map: finding out the highest side lobe point on the array factor model diagram, wherein the highest side lobe point is also the side lobe point with the greatest relative intensity of the radiation source on the array factor model diagram, taking the side lobe point as a suspicious source point, and determining the position information and the distribution characteristics of the suspicious source point in the model; calculating the distance from the suspicious source point to the main valve according to the position information of the suspicious source point and the position information of the main valve; depending on the required accuracy, there may be multiple sidelobe points with maximum relative intensities within a certain error range, and the distance from the suspicious source point to the main lobe is denoted as d t, and t is a positive integer.
Determining suspicious source points on an image of a target radiation source: traversing each column in the distance matrix D, namely the distance from each source point to other source points, aiming at each D t and t being a positive integer, and marking the source points with the distances within the error range of D t as suspicious source points of the source points until each column in the distance matrix D is completely compared with t reference distances respectively, so as to obtain a positioning result of the suspicious source points corresponding to each source point; wherein d t takes on values within a certain error range, depending on the required accuracy.
Determining a pseudo-target radiation source point: and counting the distribution characteristics of suspicious source points corresponding to each source point, and determining the suspicious source points meeting the distribution characteristics as pseudo-target radiation source points when the distribution characteristics are consistent with the distribution characteristics in the array factor model, so as to obtain a positioning result of the pseudo-target radiation source points. In this embodiment, the distribution of suspicious source points is characterized by a 6-point hexagonal distribution.
Processing the pseudo target radiation source points: the number n of pseudo-target radiation source points of each source point is calculated separately, and according to specific precision, the pseudo-target radiation source points with n being larger than a certain value are completely attenuated, and the pseudo-target radiation source points with n being smaller than the certain value are partially attenuated. In this embodiment, when n is greater than or equal to 3, the pseudo source point of the source point is completely attenuated, and when n is less than 3, the pseudo source point of the source point is partially attenuated.
Specifically, in the present invention, the step of reconstructing an image of a target radiation source includes:
Step S1: the covariance matrix of the original sparse array of the multiple antennas is obtained by utilizing the multi-sensor system, and the covariance matrix is subjected to redundant averaging and vectorization to construct a differential correlation array DIFFERENCE COARRAY signal receiving model of the original sparse array, which is specifically as follows: ,
z′＝D′(C)q+E
Wherein D' (C) represents DIFFERENCE COARRAY flow patterns, the dimension N u×K,Nu represents the number of array antennas, K represents the number of target radiation sources, C represents a cosine function set of the incoming wave directions of the K target radiation sources, E represents a measured noise matrix,Wherein,And e' 1 denote the autocorrelation output power and the corresponding unit vector after the covariance matrix redundancy average, respectively, q denotes the position vector of the target radiation source.
Step S2: constructing an overcomplete dictionary according to the difference correlation array DIFFERENCE COARRAY of the original sparse array and the spatial sparse characteristic of the target radiation source, dividing the incoming wave direction of the target radiation source into a network M g×Mg, wherein M g > K, the overcomplete dictionary is D degrees (C degrees),Representing the divided grids, and expanding the signal receiving model in the step S1 into a DIFFERENCE COARRAY-based target radiation source sparse reconstruction model:
z′＝D°(C°)q+E
Step S3: and carrying out arrival angle estimation by adopting a re-weighting l 1 norm algorithm to solve the model so as to obtain an image of the reconstructed target radiation source.
Specifically, the sparse reconstruction problem is relaxed convexly, and the following can be obtained:
in the equation, δ represents a regularization parameter.
The substitution is performed by adopting a heavy weighting l 1 norm model:
In the method, in the process of the invention, The dimension is M g×Mg, which is a weight coefficient vector.
The calculation weight of the (k+1) th solution vector (m) th element is as follows:
In the method, in the process of the invention, The m element representing the kth re-weighted solution vector, k being the number of iterations, e representing a positive parameter of algorithm robustness.
Solving the replaced model by adopting a rapid convergence threshold convergence algorithm and a neighborhood weighting strategy; specific:
transforming the replaced model into an unconstrained condition solving model:
Wherein, And W (k) represents the solution and weight matrix for the kth solution process, μ represents the regularization parameter.
Solving the unconstrained condition solving model by utilizing soft threshold convergence:
Wherein, Representing the p+1st iteration solution vector in the kth re-weighting process. w (k)＝diag(W(k)) represents a diagonalized weight matrix, the parameter a being determined by the Lipschitz constant L, i.e. a=1/L,Representing an element-wise soft thresholded vector contraction operator.
And reconstructing an image of the target radiation source through the solved solution, namely the position vector q of the target radiation source.
In the invention, the weighting coefficient W is adopted to weight the position vector q, so that the intensity of the signal source can be enhanced, and the background noise can be restrained.
In the invention, an antenna array factor model is determined according to the antenna array arrangement of a multi-sensor system:
where C (u, v) is a portion of a two-dimensional comb function in a rectangular window, (l, m) is cosine coordinates, AndIs the position vector for the r and s antennas. AF represents the array factor and λ represents the wavelength of the radiation source.
In this embodiment, 400 points are uniformly taken on (-0.2,0.2) for l and m respectively, substituted into the above formula for calculation, and the result is obtained by drawing, so as to obtain the antenna array factor model diagram.
Firstly, utilizing DIFFERENCE COARRAY of an original sparse array of a multi-sensor system to obtain a virtual array with a larger scale, constructing an original sparse array covariance matrix, and constructing a DIFFERENCE COARRAY signal receiving model by redundant averaging and vectorizing the current covariance matrix; constructing an overcomplete dictionary according to DIFFERENCE COARRAY of an original sparse array and the airspace sparse characteristic of the target radiation source, and establishing a target radiation source sparse reconstruction model based on a virtual exhibition array; and estimating an arrival angle of the target radiation source sparse reconstruction model based on the overcomplete dictionary by adopting a heavy weighting l 1 norm algorithm, and detecting and positioning the target radiation source to obtain an image of the reconstructed target radiation source.
When the real source point has larger intensity and the weak source point exists in the image at the same time, the decision threshold needs to be reduced, and false source points which do not exist in practice can appear at the side lobe position of the strong source. According to the principle that the measured bright temperature can be regarded as the convolution of the bright temperature of a real target and an array factor, an antenna array factor model of the multi-sensor system is established.
In this embodiment, after obtaining an image of a reconstructed target radiation source, according to the array factor characteristics of the microwave radiometer antenna, a false source point which is generated at a side lobe position due to excessive real source point intensity in an inversion image (i.e., an image of the reconstructed target radiation source) is found, and then the false source point is processed on the image of the reconstructed target radiation source.
In this embodiment, a Y-type array carried by the satellite of the ou-space SMOS (Soil Moisture and Ocean Salinity) is selected as the original sparse array, the number of array elements is n' =69, and the array structure and DIFFERENCE COARRAY (virtual array) are shown in fig. 2 and 3.
And determining a redundancy marking matrix according to the structural redundancy characteristic of DIFFERENCE COARRAY to mark redundant sampling data in the current covariance matrix, wherein the matrix dimension is 931×931, and the element distribution is shown in fig. 4.
And vectorizing the covariance matrix after redundancy average to obtain a virtual array signal receiving model, namely DIFFERENCE COARRAY signal receiving model.
And constructing an overcomplete dictionary according to the virtual signal receiving model and the target radiation source airspace sparse characteristic, and obtaining a target radiation source sparse reconstruction model by using the overcomplete dictionary.
The neighborhood re-weighting concept is utilized to construct a target radiation source sparse reconstruction model with re-weighting l 1 norm, a rapid iteration threshold convergence algorithm is adopted to solve, and detection and positioning results are shown in a figure 5, wherein zeta and eta are cosine coordinates.
An array factor model diagram established by utilizing a SMOS satellite-mounted Y-shaped array is shown in fig. 6, wherein the highest side lobe positions are shown in fig. 7.
And processing suspicious false positive source points in the positioning result diagram according to the position information, wherein the processed detection and positioning results of the actually measured target radiation source are shown in fig. 8. The method can be used for obviously removing false source points generated at the side valve position due to overlarge strength of the real source points after the suspicious source points are processed, has good detection and positioning effects, and improves the observability of images.
In other embodiments, other convex optimization algorithms may be employed to acquire images of the reconstructed target radiation source.
In the embodiment provided by the invention, the obtained detection result shows that the method for distinguishing and processing the pseudo target radiation source based on the array factor characteristics has better spatial resolution and detection precision performance, can solve the problem of detecting and positioning the target radiation source with weak strength under the condition of high strength dynamic state (namely that a strong source and a weak source exist at the same time), and has higher robustness in the application of detecting and positioning the actual target radiation source.
The invention also provides a pseudo-target radiation source discrimination system based on array factor characteristics, which comprises:
An image reconstruction module of the target radiation source, which is used for reconstructing an image of the target radiation source;
The distance matrix construction module is used for traversing each pixel point in the reconstructed image, determining the maximum value of each source point in the reconstructed image and the position of the maximum value, taking the position of the maximum value as the position of the source point, and constructing a distance matrix D from each source point to other source points;
the multi-antenna array factor model building module is used for building a multi-antenna array factor model diagram;
The position confirmation module of the suspicious source point on the model diagram is used for determining a side lobe point with the maximum relative strength in a certain error range on the array factor model diagram, taking the side lobe point as the suspicious source point and determining the position information and the distribution characteristics of the suspicious source point in the model; calculating the distance d t from the suspicious source point to the main valve according to the position information and the position information of the main valve in the model, wherein t is a positive integer;
The position confirmation module of the suspicious source point on the reconstructed image is used for traversing each column in the distance matrix D aiming at each D t in the reconstructed image, and marking all the source points with the distance within the error range of D t as the suspicious source points of the source point;
And the pseudo-target radiation source point determining module is used for counting the distribution characteristics of the suspicious source points corresponding to each source point, and determining the suspicious source point as the pseudo-target radiation source point corresponding to the source point when the distribution characteristics are consistent with the distribution characteristics in the array factor model, so as to obtain the positioning result of the pseudo-target radiation source point.
Specifically, the multi-antenna array factor model building module builds a multi-antenna array factor model graph according to the distribution of the difference correlation array DIFFERENCE COARRAY by acquiring the difference correlation array DIFFERENCE COARRAY of the original sparse array of the multi-antenna.
Specifically, an image reconstruction module of the target radiation source adopts a convex optimization algorithm to acquire the reconstructed image of the target radiation source.
The step of reconstructing an image of the target radiation source by image reconstruction of the target radiation source comprises:
Step S1, acquiring a covariance matrix of a multi-antenna original sparse array, and carrying out redundant averaging and vectorization on the covariance matrix to construct a differential correlation array DIFFERENCE COARRAY signal receiving model of the original sparse array;
S2, dividing the incoming wave direction of the target radiation source into a network by taking the difference correlation array DIFFERENCE COARRAY of the original sparse array and the spatial domain sparse characteristic of the target radiation source as constraint conditions to obtain an overcomplete dictionary;
Step S3, expanding the model in the step S1 into a DIFFERENCE COARRAY-based sparse reconstruction model of the target radiation source based on the overcomplete dictionary;
And S4, solving the model by adopting a re-weighting l 1 norm algorithm to obtain an image of the reconstructed target radiation source.
Specifically, the device also comprises an image processing module which is used for completely attenuating or partially attenuating the pseudo target radiation source points.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A false target radiation source discriminating method based on array factor features is characterized by comprising the following steps:
reconstructing an image of the target radiation source;
traversing each pixel point in a reconstructed image, determining a maximum value of each source point in the reconstructed image and the position of the maximum value, taking the position of the maximum value as the position of the source point, and constructing a distance matrix D from each source point to other source points;
establishing a multi-antenna array factor model diagram;
Determining a side lobe point with the maximum relative intensity in a certain error range on an array factor model diagram, taking the side lobe point as a suspicious source point, and determining the position information and the distribution characteristics of the suspicious source point in a model; calculating the distance d t from the suspicious source point to the main valve according to the position information and the position information of the main valve in the model, wherein t is a positive integer;
Traversing each column in the distance matrix D for each D t on the reconstructed image, and marking a source point with a distance within the error range of D t as a suspicious source point of the source point;
counting the distribution characteristics of suspicious source points corresponding to each source point, and determining the suspicious source points as pseudo-target radiation source points corresponding to the source points when the distribution characteristics are consistent with the distribution characteristics in the array factor model, so as to obtain a positioning result of the pseudo-target radiation source points.
2. The method of claim 1, wherein the multi-antenna array factor model map is constructed from a distribution of the difference correlation array DIFFERENCE COARRAY by obtaining a difference correlation array DIFFERENCE COARRAY of a multi-antenna original sparse array.
3. The method of claim 2, wherein a convex optimization algorithm is employed to obtain an image of the reconstructed target radiation source.
4. A method according to claim 3, wherein the step of reconstructing an image of the target radiation source comprises:
step S1, acquiring a covariance matrix of a multi-antenna original sparse array, and carrying out redundant averaging and vectorization on the covariance matrix to construct a differential correlation array DIFFERENCE COARRAY signal receiving model of the original sparse array;
S2, dividing the incoming wave direction of the target radiation source into a network by taking the difference correlation array DIFFERENCE COARRAY of the original sparse array and the spatial domain sparse characteristic of the target radiation source as constraint conditions to obtain an overcomplete dictionary;
Step S3, expanding the model in the step S1 into a DIFFERENCE COARRAY-based sparse reconstruction model of the target radiation source based on the overcomplete dictionary;
And S4, solving the model by adopting a re-weighting l 1 norm algorithm to obtain an image of the reconstructed target radiation source.
5. The method of claim 4, wherein in the re-weighting l 1 -norm algorithm, the calculation weight of the m element of the (k+1) -th solution vector is:
Wherein, Representing the partitioned network, k is the number of iterations and e represents a positive parameter of algorithm robustness.
6. The method of any one of claims 1-5, further comprising completely or partially attenuating the pseudo-target radiation source point after obtaining the positioning result of the pseudo-target radiation source point.
7. A pseudo-target radiation source discrimination system based on array factor characteristics, comprising:
An image reconstruction module of the target radiation source, which is used for reconstructing an image of the target radiation source;
The distance matrix construction module is used for traversing each pixel point in the reconstructed image, determining the maximum value of each source point in the reconstructed image and the position of the maximum value, taking the position of the maximum value as the position of the source point, and constructing a distance matrix D from each source point to other source points;
the multi-antenna array factor model building module is used for building a multi-antenna array factor model diagram;
The position confirmation module of the suspicious source point on the model diagram is used for determining a side lobe point with the maximum relative strength in a certain error range on the array factor model diagram, taking the side lobe point as the suspicious source point and determining the position information and the distribution characteristics of the suspicious source point in the model; calculating the distance d t from the suspicious source point to the main valve according to the position information and the position information of the main valve in the model, wherein t is a positive integer;
The position confirmation module of the suspicious source point on the reconstructed image is used for traversing each column in the distance matrix D aiming at each D t in the reconstructed image, and marking the source point with the distance within the error range of D t as the suspicious source point of the source point;
and the pseudo-target radiation source point determining module is used for counting the distribution characteristics of the suspicious source points corresponding to each source point, and determining the suspicious source points as the pseudo-target radiation source points corresponding to the source points when the distribution characteristics are consistent with the distribution characteristics in the array factor model, so as to obtain the positioning result of the pseudo-target radiation source points.
8. The system of claim 7, wherein the multi-antenna array factor model building module builds a multi-antenna array factor model map from a distribution of the difference correlation arrays DIFFERENCE COARRAY by obtaining the difference correlation arrays DIFFERENCE COARRAY of the original sparse array of the multi-antennas.
9. The system of claim 8, wherein the image reconstruction module of the target radiation source employs a convex optimization algorithm to obtain the reconstructed image of the target radiation source.
10. The system of any of claims 7-9, further comprising an image processing module for completely or partially attenuating the pseudo-target radiation source point.
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| CN114201728A (en) * | 2021-11-30 | 2022-03-18 | 华中科技大学 | Radio frequency interference source positioning method and system based on multi-beat combined sparse reconstruction |
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