CN-122017787-A - Exogenous radar target detection method and system based on sparse model

CN122017787ACN 122017787 ACN122017787 ACN 122017787ACN-122017787-A

Abstract

The invention provides an exogenous radar target detection method and system based on a sparse model, and relates to the technical field of radar signal processing, wherein the method comprises the steps of receiving a time domain monitoring signal of an orthogonal frequency division multiplexing waveform, performing discrete Fourier transform to obtain a carrier domain monitoring signal, and extracting data at a pilot frequency position; the method comprises the steps of constructing a carrier domain reference signal based on known transmitting end pilot frequency information, carrying out clutter suppression processing on a carrier domain monitoring signal, carrying out inverse discrete Fourier transform on the carrier domain monitoring signal and the carrier domain reference signal, reconstructing a new time domain reference signal and a new time domain monitoring signal, carrying out distance processing to obtain distance domain data, establishing a sparse model and forming an optimization problem to solve a target detection result, constructing the reference signal by using the pilot frequency information only, not needing to accurately estimate a direct wave, greatly reducing the complexity of a system, and effectively solving the problem of covering a weak target by strong clutter by introducing clutter suppression before sparse modeling.

Inventors

ZHAO ZHIXIN
HUANG HAO
ZHENG YIQUN
Liang Baiming
HUANG XIN

Assignees

南昌大学

Dates

Publication Date: 20260512
Application Date: 20260413

Claims (10)

1. An exogenous radar target detection method based on a sparse model, which is characterized by comprising the following steps: receiving a time domain monitoring signal of an exogenous radar monitoring channel based on an orthogonal frequency division multiplexing waveform, performing discrete Fourier transform on the time domain monitoring signal to obtain a carrier wave domain monitoring signal, and extracting data at a pilot frequency position of the carrier wave domain monitoring signal; Constructing a carrier domain reference signal containing pilot frequency information based on the known pilot frequency information of the transmitting end and the data at the pilot frequency position; In a carrier domain, carrying out zero frequency clutter suppression processing on the carrier domain monitoring signal by utilizing the carrier domain reference signal to obtain a carrier domain monitoring signal after clutter suppression; Respectively carrying out inverse discrete Fourier transform on the carrier wave domain monitoring signal after clutter suppression and the carrier wave domain reference signal, reconstructing a new time domain reference signal and a new time domain monitoring signal, and carrying out segmentation distance correlation processing on the two signals to obtain distance domain data; And establishing a sparse model of a distance-Doppler spectrum based on the distance domain data, forming a corresponding optimization problem, and solving the optimization problem to obtain a target detection result.
2. The method for detecting an exogenous radar target based on a sparse model according to claim 1, wherein the steps of receiving a time domain monitoring signal of an exogenous radar monitoring channel based on an orthogonal frequency division multiplexing waveform, performing discrete fourier transform on the time domain monitoring signal to obtain a carrier domain monitoring signal, and extracting data at a pilot frequency position of the carrier domain monitoring signal, specifically comprise: The time domain monitoring signal comprises direct wave, zero frequency multipath clutter, target echo and receiver thermal noise, and is expressed as: , Wherein, the In order to monitor the time domain monitoring signal received by the channel, Is a discrete time delay The direct wave after the wave is transmitted to the receiving device, Is the total number of zero-frequency multipath spurs, For the number of targets to be detected, 、 The complex envelope amplitude and the discrete time delay of the ith zero-frequency multipath clutter are respectively represented, 、 And Representing the complex envelope amplitude, discrete delay and normalized doppler shift of the qth target echo, Indicating that the direct wave has a time delay of Doppler shift of Is a copy of the (c), In order to monitor the discrete noise vector in the channel, N is a discrete time index, and N is the total discrete time of the time domain monitoring signal; Dividing the time domain monitoring signal into a plurality of groups of orthogonal frequency division multiplexing symbols according to a symbol period, wherein each group of orthogonal frequency division multiplexing symbols comprises an effective data segment and a cyclic prefix, and the symbols are expressed as follows: , , Wherein, the Representing the first group of OFDM symbols Complex data at the subcarrier locations, The number of samples for the valid data in a set of orthogonal frequency division multiplexing symbols, For the total number of samples in a set of orthogonal frequency division multiplexing symbols, As a function of the sampling function, For the total number of groups of orthogonal frequency division multiplexing symbols, First, the The set of orthogonal frequency division multiplexing symbols, M is a sum index, j is an imaginary unit; Removing cyclic prefix from each group of orthogonal frequency division multiplexing symbols, performing discrete Fourier transform, and converting the time domain monitoring signals into carrier domains to obtain carrier domain monitoring signals, wherein the carrier domain monitoring signals are expressed as follows: , , Wherein, the The signal vector is monitored for the carrier domain, Monitor signal subvectors for carrier domains corresponding to the first set of orthogonal frequency division multiplexing symbols, For the first group of OFDM symbols Monitoring data of subcarrier position, if When the subcarrier position is the pilot position, the monitored data is the value of the extracted data at the pilot position after the channel response, if the data at the pilot position is the first When the subcarrier position is a non-pilot position, the monitored data is zero.
3. The method for detecting an exogenous radar target based on a sparse model according to claim 2, wherein the step of constructing a carrier domain reference signal containing pilot information based on the known transmitting end pilot information and the data at the pilot position specifically comprises: Based on the known transmitting end pilot information, assigning the pilot position as the pilot value of the known transmitting end pilot information, setting the non-pilot position to zero, and constructing a carrier domain reference signal containing the pilot information, wherein the carrier domain reference signal is expressed as: , , Wherein, the For the carrier domain reference signal vector, The carrier domain reference signal sub-vector corresponding to the first set of orthogonal frequency division multiplexing symbols, For the first group of OFDM symbols Reference data of subcarrier locations, if When the subcarrier position is pilot position, the reference data is pilot value, if the first When the subcarrier location is a non-pilot location, the reference data is zero.
4. The method for detecting an exogenous radar target based on a sparse model according to claim 3, wherein in a carrier domain, the carrier domain monitoring signal is subjected to zero frequency clutter suppression processing by using the carrier domain reference signal, and a step of obtaining a carrier domain monitoring signal after clutter suppression specifically comprises the following steps: Extracting the first of several groups of OFDM symbols The monitoring data of the sub-carriers form a monitoring data vector; extracting the first of the corresponding set of OFDM symbols Reference data of the sub-carriers form a reference data vector; based on the monitoring data vector and the reference data vector, the self-adaptive cancellation coefficient is calculated by taking the energy of the signal after clutter suppression minimization as a target, and the calculation formula is as follows: , Wherein, the In order to adapt the cancellation coefficient to the actual value, 、 Respectively monitoring data vectors and reference data vectors; Method for monitoring signal in carrier wave domain by adopting extended cancellation algorithm The subcarriers are independently subjected to clutter suppression processing to obtain carrier domain monitoring signals after clutter suppression, and the calculation formula is as follows: , Wherein, the Is the first The carrier domain after subcarrier clutter suppression monitors the signal vector.
5. The method for detecting an exogenous radar target based on a sparse model according to claim 4, wherein the steps of performing inverse discrete fourier transform on the carrier domain monitoring signal and the carrier domain reference signal after clutter suppression, reconstructing a new time domain reference signal and a new time domain monitoring signal, and performing piecewise distance correlation on the two signals to obtain distance domain data specifically comprise: Performing inverse discrete Fourier transform on the carrier domain monitoring signal and the carrier domain reference signal after clutter suppression according to symbols, and adding cyclic prefix to reconstruct a new time domain reference signal and a new time domain monitoring signal respectively; dividing the new time domain reference signal and the new time domain monitoring signal into a plurality of segments according to preset segment lengths respectively, wherein the segments are expressed as follows: , , Wherein t is a continuous time variable, For a time domain reference signal at a continuous time variable t, For a time domain monitoring signal at a continuous time variable t, For the reference signal segment corresponding to the b-th segment, For the monitor signal segment corresponding to the b-th segment, As the total number of segments to be segmented, Is a preset segment length that is set to be equal to the segment length, As a rectangular pulse function when When the rectangular pulse function is 1, the rest is 0; Performing distance compression on the time domain reference signal and the time domain monitoring signal corresponding to each segment to obtain distance domain data, wherein the distance domain data is expressed as: , Wherein, the Delay for the b-th segment The distance field data of the back is used, For a preset maximum detectable delay, For the time domain monitoring signal corresponding to the b-th segment, Time delay is carried out on the time domain reference signal corresponding to the b-th segment And taking the signal component after complex conjugation, which is complex conjugation operation.
6. The method for detecting an exogenous radar target based on a sparse model according to claim 5, wherein the step of establishing a sparse model of a range-doppler spectrum based on the range domain data specifically comprises: according to the speed range and the working parameters of the target to be detected, a Doppler frequency search interval is determined, and for each segmented distance domain data, a segmented mutual ambiguity function is constructed by taking Doppler frequency as a search variable, wherein the segmentation mutual ambiguity function is expressed as follows: , Wherein, the As a mutual ambiguity function of the b-th segment, Is Doppler frequency; And weighting and summing all segmented mutual fuzzy functions according to slow time indexes to obtain a global mutual fuzzy function, wherein the global mutual fuzzy function is expressed as: , Wherein, the , Slow time index for segment b; when the product of the segment length and the maximum Doppler frequency in the Doppler frequency search interval meets the preset small quantity condition, the phase of the sampling point in the segment is approximate to the phase of the middle time of the segment, namely An approximate piecewise mutual blur function is obtained, expressed as: , Introducing the approximate piecewise mutual blur function into the global mutual blur function to obtain the global approximate mutual blur function, wherein the global approximate mutual blur function is expressed as: ; discretizing the delay and Doppler frequency based on the global approximate mutual ambiguity function, and constructing a Doppler shift Fourier transform matrix and a range domain echo matrix; And establishing a sparse model of a range-Doppler spectrum based on the Doppler shift Fourier transform matrix and the range-domain echo matrix.
7. The method of claim 6, wherein the step of discretizing the delay and doppler frequencies based on the global approximate mutual blur function to construct a doppler shift fourier transform matrix and a range-echo matrix, specifically comprises: Splitting the global approximate mutual ambiguity function into a delay discrete part and a Doppler frequency discrete part; Based on the time delay discrete part, a time delay interval [0, Discretizing into a first preset number of grids, and constructing a distance domain echo matrix, wherein the distance domain echo matrix is expressed as follows: , Wherein, the For a first predetermined number of the first set, In order to be a distance domain echo matrix, Representing the matrix as Row of lines A complex matrix of columns; dispersing Doppler frequency search intervals into grids with a second preset number, and constructing a Doppler shift Fourier transform matrix based on a slow time index and the Doppler frequency dispersing part, wherein the Doppler shift Fourier transform matrix is expressed as follows: , Wherein the Doppler frequency interval is , For the doppler shift fourier transform matrix, For a second predetermined number of times, Representing the matrix as A row(s), A matrix of a plurality of columns, Is the first Slow time index of each segment.
8. The method for detecting an exogenous radar target based on a sparse model according to claim 7, wherein the step of establishing a sparse model of a range-doppler spectrum based on the doppler shift fourier transform matrix and the range-domain echo matrix specifically comprises: based on the Doppler shift Fourier transform matrix and the distance domain echo matrix, a discretized mutual ambiguity function matrix is established, expressed as: , Wherein, the Representing a matrix of discretized mutual ambiguity functions, Representing the matrix as A row(s), A complex matrix of columns; Performing transposition operation on the mutual ambiguity function matrix, and combining the unitary nature of the Doppler shift Fourier transform matrix to obtain a sparse model, wherein the sparse model is expressed as: , Wherein, the Is the conjugate transpose of the doppler shift fourier transform matrix.
9. The method for detecting the exogenous radar target based on the sparse model according to claim 8, wherein the method is characterized by comprising the steps of The sparsity of (2) transforming the sparse model into an optimization problem expressed as: , Wherein, the The F-norms of the matrix are represented, The L1 norm of the matrix is represented, Is a preset error tolerance threshold; and solving the optimization problem through an orthogonal matching pursuit algorithm to obtain a target detection result.
10. A sparse model-based exogenous radar target detection method system for implementing the sparse model-based exogenous radar target detection method of any one of claims 1 to 9, the system comprising: the monitoring signal acquisition module is used for receiving the time domain monitoring signal of the external source radar monitoring channel based on the orthogonal frequency division multiplexing waveform and performing discrete Fourier transform on the time domain monitoring signal to obtain a carrier wave domain monitoring signal; the reference signal acquisition module is used for extracting data at a pilot frequency position from the carrier domain monitoring signal and constructing a carrier domain reference signal containing pilot frequency information based on the known pilot frequency information of the transmitting end; The clutter suppression module is used for performing zero frequency clutter suppression processing on the carrier domain monitoring signal by utilizing the carrier domain reference signal in a carrier domain to obtain a carrier domain monitoring signal after clutter suppression; the distance correlation processing module is used for respectively carrying out inverse discrete Fourier transform on the carrier domain monitoring signal after clutter suppression and the carrier domain reference signal, reconstructing a new time domain reference signal and a new time domain monitoring signal, and carrying out segmentation distance correlation processing on the two signals to obtain distance domain data; and the sparse model solving module is used for establishing a sparse model of the distance-Doppler spectrum based on the distance domain data and forming a corresponding optimization problem, and solving the optimization problem to obtain a target detection result.

Description

Exogenous radar target detection method and system based on sparse model Technical Field The invention relates to the technical field of radar signal processing, in particular to an exogenous radar target detection method and system based on a sparse model. Background Exogenous radar, also called passive radar, does not emit signals by itself, but uses commercial or broadcast signals (such as digital television signals, frequency modulation broadcast signals, cellular network signals and the like) emitted by a third party as an irradiation source, and detects, positions and tracks a target by receiving the signals reflected by the target. Compared with the traditional active radar, the external radar has the remarkable advantages of no need of spectrum allocation, strong anti-interference capability, flexible system deployment, low maintenance cost, no electromagnetic pollution and the like, and therefore, the external radar is widely paid attention to both military and civil fields. The method has the advantages that the method is mainly used for constructing a model around the sparsity of a received signal and solving the model, but the technical defects still exist in practical engineering application, namely firstly, the method is highly dependent on accurate estimation of a direct wave signal, generally, when the sparse model is constructed and clutter suppression is carried out, the accurate direct wave signal received by a reference channel is taken as a reference, the estimation of the direct wave signal is influenced by factors such as multipath propagation, channel fading, noise interference and the like, a complex estimation algorithm is needed, the calculation burden and hardware complexity of the system are greatly increased, the subsequent target detection performance is reduced due to estimation errors, secondly, the weak target detection performance is insufficient due to the fact that clutter suppression processing is not considered by the sparse model, the clutter suppression step is added before sparse modeling in the existing part method, but only simple average subtraction, basic cancellation algorithm and the like are adopted, the clutter suppression effect is poor in the environment of strong clutter noise ratio (the signal to noise ratio is 60dB or more), the target signal to-30 dB lower than the target signal is detected, the accuracy of the method cannot be easily balanced, and the accuracy of the target detection cannot be improved, and the accuracy of the method cannot be easily calculated, and the real-time precision cannot be easily calculated by the method is reduced. Disclosure of Invention Aiming at the defects of the prior art, the invention aims to provide an exogenous radar target detection method and system based on a sparse model, and aims to solve at least one problem in the background art. The first aspect of the invention provides an exogenous radar target detection method based on a sparse model, which comprises the following steps: receiving a time domain monitoring signal of an exogenous radar monitoring channel based on an orthogonal frequency division multiplexing waveform, performing discrete Fourier transform on the time domain monitoring signal to obtain a carrier wave domain monitoring signal, and extracting data at a pilot frequency position of the carrier wave domain monitoring signal; Constructing a carrier domain reference signal containing pilot frequency information based on the known pilot frequency information of the transmitting end and the data at the pilot frequency position; In a carrier domain, carrying out zero frequency clutter suppression processing on the carrier domain monitoring signal by utilizing the carrier domain reference signal to obtain a carrier domain monitoring signal after clutter suppression; Respectively carrying out inverse discrete Fourier transform on the carrier wave domain monitoring signal after clutter suppression and the carrier wave domain reference signal, reconstructing a new time domain reference signal and a new time domain monitoring signal, and carrying out segmentation distance correlation processing on the two signals to obtain distance domain data; And establishing a sparse model of a distance-Doppler spectrum based on the distance domain data, forming a corresponding optimization problem, and solving the optimization problem to obtain a target detection result. According to one aspect of the above technical solution, the steps of receiving a time domain monitoring signal of an exogenous radar monitoring channel based on an orthogonal frequency division multiplexing waveform, performing discrete fourier transform on the time domain monitoring signal to obtain a carrier domain monitoring signal, and extracting data at a pilot frequency position of the carrier domain monitoring signal specifically include: The time domain monitoring signal comprises direct wave, zero frequency multipath clutter, target echo and