A method for discriminating towed jamming based on transmit-receive spatial frequency spectrum

Abstract

This invention discloses a method for identifying dragged interference based on the transmit-receive spatial frequency spectrum, belonging to the field of radar signal processing technology. The invention includes: constructing an FDA-MIMO radar transmitter array and receiver array; calculating the transmitted signal of the transmitter array elements according to a preset frequency increment; receiving the target and interference echo signals of the receiver array, obtaining the target and interference signal vectors within a CPI; calculating the echo covariance matrix based on the target and interference signal vectors, and estimating its Capon spectrum; accumulating the Capon spectrum of multiple coherent processing intervals, then performing peak search to obtain the trajectory of the target and interference, and segmenting the trajectory; calculating the Euclidean distance between peaks within each trajectory, and calculating its variance to identify dragged interference. This invention utilizes the difference in trajectory characteristics between dragged interference and real targets in the transmit-receive spatial frequency spectrum domain of the FDA-MIMO radar, and distinguishes dragged interference from real targets by the magnitude of the trajectory variance without needing to estimate the target velocity.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and specifically to a method for identifying dragged interference using the transmit and receive spatial frequency spectrum of an FDA-MIMO (Frequency Diverse Array-Multiple Input Multiple Output) radar. Background Technology

[0002] FDA-MIMO radar combines the range dependence of FDA (Frequency Diverse Array) with the spatial degrees of freedom of Multiple-Input Multiple-Output (MIMO), enabling it to form angle-range dependent beams. It is precisely this characteristic that gives FDA-MIMO radar great potential for applications in areas such as mainlobe interference suppression, range ambiguity clutter suppression, radio frequency stealth, and positioning deception.

[0003] Towed radar active jamming (also known as towed jamming) is a new type of deception jamming method. It simulates the target's flight characteristics and radar scattering characteristics by towing a device that can emit counter-signals to the tail of the aircraft. It forms a deception jamming signal within the radar main lobe beam, which tricks the electric axis of the traditional monopulse angle measurement radar antenna from the target and points to the towed jamming. Summary of the Invention

[0004] To address the problem that traditional monopulse angle measurement radars struggle to effectively distinguish between targets and towed jamming, this invention utilizes the transmit and receive spatial frequency spectrum of FDA-MIMO radar to identify towed jamming.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0006] A dragged interference identification method based on transmit and receive spatial frequency spectra includes the following steps:

[0007] Step S1: Construct the transmitter array and receiver array of the FDA-MIMO radar, and calculate the transmitted signal of the transmitter array element according to the preset frequency increment.

[0008] Step S2: Calculate the target echo and (towed) interference signal of the receiver array based on the transmitted signal of the transmitter array element calculated in step S1, and obtain the signal vector of the target echo and interference signal within a coherent processing interval (CPI) (referred to as the target and interference signal vector).

[0009] Step S3: Calculate the covariance matrix based on the signal vectors of the target echo and interference signal obtained in step S2, and estimate its Capon spectrum;

[0010] Step S4: Accumulate the Capon spectrum of multiple CPIs, then perform peak search to obtain the trajectory of the target and interference (i.e., the trajectory including the target and interference), and segment the trajectory.

[0011] Step S5: Based on the obtained trajectory segmentation results, calculate the Euclidean distance between the target trajectory and the dragging interference trajectory, and calculate the variance of the two; identify the dragging interference based on the magnitude of the variance, and the one with the smaller variance is the dragging interference.

[0012] Further, step S1 includes the following steps:

[0013] Step S11: Preset the number of array elements at the FDA-MIMO radar transmitter and receiver, and set the array element spacing according to the center frequency of the transmitted signal. Among them, wavelength f0 is the center frequency of the transmitted signal, and c is the speed of light;

[0014] The transmitting array is obtained based on the number of array elements and the element spacing d at the transmitting end, and the receiving array is obtained based on the number of array elements and the element spacing d at the receiving end.

[0015] The carrier frequency of the transmitter array elements is calculated based on the preset frequency increment:

[0016] f m =f0+mΔf

[0017] Among them, f m Let f be the carrier frequency of the m-th element in the transmitter array, and Δf represent the frequency offset.

[0018] S12, based on the array element carrier frequency f m Calculate the transmitted signal of the transmitter array element:

[0019] s m (t)=φ m (t)exp(j2πf m (t))

[0020] Among them, s m (t) represents the transmitted signal of the m-th array element at the transmitter, φ m (t) represents the baseband signal transmitted by the m-th array element at the transmitter, and its calculation formula is:

[0021]

[0022] Where j is an imaginary number, π is a constant, t is a time variable, and B is the signal bandwidth. Indicates pulse width as Tp The rectangular pulse, exp(·) represents the natural exponential function.

[0023] Furthermore, the transmitting array and the receiving array are co-located.

[0024] Furthermore, step S2 specifically includes the following steps:

[0025] Step S21: Calculate the target echo signal and interference signal received by the nth receiving element based on the transmitted signal of the transmitting element:

[0026]

[0027]

[0028] Among them, y tn (t) represents the target echo, y jn (t) represents the interference signal, β t β represents the target complex scattering coefficient. j τ represents the interference forwarding strength. t τ represents the target delay. j Indicates the interference delay, and M represents the number of transmit array elements;

[0029] Step S22: Perform multi-channel mixing and matched filtering on the target echo and interference signal to obtain the target echo and interference signal vector of the k-th pulse of the FDA-MIMO radar:

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] Among them, X (k) This represents the target echo and interference signal vector of the k-th pulse of the FDA-MIMO radar. Let a represent the target echo vector and the interference signal vector of the k-th pulse, respectively. r (·) indicates the receiving guide vector, a t (·) represents the transmission steering vector, θ represents the direction of arrival of the target or jamming wave, R represents the distance from the target or jamming wave to the FDA-MIMO radar, and θ t and θ j R represents the direction of arrival of the target and the interference waves, respectively. t and R jThese represent the distances from the target and the interference to the FDA-MIMO radar, respectively. The circle represents the Kronecker product, and the circle represents the Hadama product.

[0036] Step S23: After CPI processing, the target echo and interference signal vectors of all pulses can be represented as the following matrix:

[0037]

[0038] Where K represents the number of pulses in a CPI.

[0039] Furthermore, step S3 specifically includes the following steps:

[0040] Step S31: Based on the result (X(t)) obtained in step S2, calculate the covariance matrix, which is expressed as:

[0041] Q=E(X(t)X H (t))

[0042] Where Q represents the covariance matrix, E(·) represents the expectation, and (·) H Represents the transpose of a matrix;

[0043] Step S32: Use the Capon spectral estimation algorithm to obtain the spatial spectrum of the p-th CPI of the FDA-MIMO radar, expressed as:

[0044]

[0045] in, N CPI Indicates the number of accumulated spatial spectra. It represents the set consisting of all integers.

[0046] Furthermore, step S4 specifically includes the following steps:

[0047] S41. Sum the envelopes of all spatial spectra obtained in step S3 to obtain the trajectory diagrams of the target and the interference;

[0048] S42. Perform peak search on the trajectory maps of the target and the interference, and obtain the coordinates of each peak in the spatial spectrum. Each obtained coordinate is used as a trajectory point.

[0049] S43. Using the divide-and-conquer method and union-find disjoint sets, the trajectory points obtained in step S42 are split to separate the target and interference trajectories, thus obtaining the target trajectory and interference trajectory.

[0050] Further, step S5 is as follows:

[0051] Calculate the variance of the Euclidean distance between the trajectory points of the target and the interference respectively. The one with a larger variance is the true target, and the one with a smaller variance is the dragged interference.

[0052] In the trajectory graph including the target trajectory and the interference trajectory, iterate through the coordinates (x, y) of each trajectory point and calculate the Euclidean distance dt between the target trajectory point and each trajectory point. xy Euclidean distance between the interference trajectory points and dj xy :

[0053]

[0054]

[0055] Among them, Rt x ,Rt y ,Rj x ,Rj y Let θt represent the target located at (x,y) and the distance to the FDA-MIMO radar where the interference is applied, respectively. x ,θt y ,θj x ,θj y Let x and y represent the angles of the target and the interference point located at (x, y), respectively.

[0056] Calculate the variances of the target and interference trajectory points based on Euclidean distance:

[0057]

[0058]

[0059] in, and Let N represent the variances of the Euclidean distances between the target and interference trajectory points, respectively. t and N j μ represents the number of target and interference trajectory points, respectively. t and μ j These represent the mean Euclidean distances between the target and the interference trajectory points, respectively.

[0060] like Greater than The target corresponding to the current trajectory point coordinates (x, y) is the true target, and the corresponding interference is drag interference.

[0061] The technical solution provided by this invention brings at least the following beneficial effects:

[0062] This invention utilizes the difference in motion trajectory characteristics between towed jamming and real targets in the spatial frequency spectrum domain of FDA-MIMO radar transmission and reception, and distinguishes between towed jamming and real targets by measuring the magnitude of trajectory variance without estimating target velocity. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 A flowchart of a dragging interference identification method based on the transmit and receive spatial frequency spectrum provided in an embodiment of the present invention;

[0065] Figure 2 Conceptual diagram for a drag-type interference scenario;

[0066] Figure 3 This is a schematic diagram of the FDA-MIMO radar structure provided in an embodiment of the present invention;

[0067] Figure 4 The segmented trajectory diagram provided as an example of the present invention;

[0068] Figure 5 The accuracy curves for identifying drag interference in head-on and tail-chase scenarios provided in this embodiment of the invention. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0070] like Figure 1 As shown, the dragged interference identification method based on the transmit and receive spatial frequency spectrum provided by this embodiment of the invention includes the following steps:

[0071] Step S1: Construct the FDA-MIMO radar transmitter array and receiver array, and calculate the transmitted signal of the transmitter array element according to the preset frequency increment;

[0072] Step S2: Calculate the target and interference echo signals of the receiving array based on the transmitted signals of the array elements in Step S1, and obtain the target and interference signal vectors within a CPI;

[0073] Step S3: Calculate the echo covariance matrix based on the target and interference signal vectors obtained in Step S2, and estimate its Capon spectrum;

[0074] When N CPIs have been accumulated, proceed to step S4; otherwise, return to step S2. The number of accumulated CPIs N is a preset value that can be set according to the actual application scenario.

[0075] Step S4: Accumulate all Capon spectral modulus values ​​estimated in the above steps, and perform peak search and trajectory segmentation on them;

[0076] Step S5: Calculate the Euclidean distance between peaks within each trajectory and its variance to identify dragging interference.

[0077] Figure 2 This illustrates the "triangular situation" of radar, target, and jamming under head-on and tail-chase attacks. Assume the radar is at point A, the target at point C, and the jamming at point B. s The length of the trailing line between the target and the jammer is given by v, which represents the speed of the target relative to the radar in head-on and tail-chase situations.

[0078] Further, step S1 includes the following sub-steps:

[0079] S11. Preset the number of array elements at the transmitter and receiver of the FDA-MIMO radar, and set the element spacing according to the center frequency of the transmitted signal. In practice, the transmitter array and receiver array of the FDA-MIMO radar adopt a co-location structure, such as... Figure 3 As shown. The formula for calculating the element spacing is:

[0080]

[0081] Where d is the element spacing. f0 is the center frequency of the transmitted signal, and c is the speed of light.

[0082] The carrier frequency of the transmitter array elements is calculated based on the preset frequency increment, and the calculation formula is expressed as follows:

[0083] f m =f0+mΔf

[0084] Among them, f m Let denoted as the carrier frequency of the m-th element in the transmitter array, and Δf as the frequency offset.

[0085] S12. Calculate the transmitted signal of the array element at the transmitting end based on the array element carrier frequency in step S11. The calculation formula is as follows:

[0086] s m (t)=φ m (t)exp(j2πf m (t))

[0087] Among them, s m (t) represents the transmitted signal of the m-th array element at the transmitter, φ m (t) represents the baseband signal transmitted by the m-th array element at the transmitter, and its calculation formula is as follows:

[0088]

[0089] Where j is an imaginary number, π is a constant, t is a time variable, and B is the signal bandwidth. Indicates pulse width as T p The rectangular pulse, exp(·) represents the natural exponential function.

[0090] Furthermore, step S2 specifically includes the following sub-steps:

[0091] S21. Calculate the target echo signal and interference signal received by the nth receiving element based on the transmitted signal of the array element in step S1, and express them as follows.

[0092]

[0093]

[0094] Among them, y tn (t) represents the target echo, y jn (t) represents the interference echo, β t β represents the target complex scattering coefficient. j τ represents the interference forwarding strength. t τ represents the target delay. j Indicates the interference delay, and M represents the number of transmit array elements;

[0095] S22. Perform multi-channel mixing and matched filtering on the target echo and interference signal from step S21 to obtain the target echo and interference signal vectors for the k-th pulse of the FDA-MIMO radar, which are expressed as follows:

[0096]

[0097]

[0098]

[0099]

[0100] Among them, a r (·) indicates the receiving guide vector, a t (·) represents the launch steering vector, θ t and θ j R represents the direction of arrival of the target and the interference waves, respectively. t and R j These represent the distances from the target and the interference to the FDA-MIMO radar, respectively. ⊙ represents the Kronecker product, and ⊙ represents the Hadama product.

[0101] S23. After CPI processing, the target echo and interference signal vectors of all pulses can be represented as the following matrix:

[0102]

[0103] Where K represents the number of pulses in a CPI.

[0104] Furthermore, step S3 specifically includes the following sub-steps:

[0105] S31. Based on the results obtained in step S2, calculate the covariance matrix, which is expressed as:

[0106] Q=E(X(t)X H (t))

[0107] Where Q represents the covariance matrix, E(·) represents the expectation, and (·) H Represents the transpose of a matrix;

[0108] S32. The spatial spectrum of the p-th CPI of the FDA-MIMO radar is obtained using the Capon spectral estimation algorithm, and is expressed as:

[0109]

[0110] in, N CPI Indicates the number of accumulated spatial spectra. It represents the set consisting of all integers.

[0111] Furthermore, step S4 specifically includes the following sub-steps:

[0112] S41. In practice, 10 CPIs are typically accumulated, i.e., N CPI =10, sum the envelopes of all spatial spectra obtained in step S3 to obtain the trajectory diagrams of the target and the interference;

[0113] S42. Perform peak search on the results obtained in step S41 and obtain the coordinates of each peak in the spatial spectrum;

[0114] S43. Using the divide-and-conquer method and the disjoint-set data structure to separate the trajectory points obtained in step S42, separate the trajectory of the target from that of the dragging interference.

[0115] Further, step S5 is as follows:

[0116] Calculate the variance of the Euclidean distance between the trajectory points of the target and the interference separately. The target with a larger variance is the true target, and the interference with a smaller variance is the dragged interference.

[0117] The Euclidean distance between the target and the interference trajectory points is calculated and expressed as:

[0118]

[0119]

[0120] Among them, dt xy and DJ xy Let Rt represent the Euclidean distance between the target and the interference trajectory points at coordinates (x, y), respectively. x ,Rt y ,Rj x ,Rj y Let θt represent the distances between the target and the interference point at (x, y), respectively. x ,θt y ,θj x ,θj y These represent the angles of the target and the interference point at (x, y), respectively.

[0121] Then, the variances of the target and interference trajectory points are calculated using Euclidean distance, and expressed as follows:

[0122]

[0123]

[0124] in, and Let N represent the variance of the Euclidean distance between the target and the interference trajectory points. t and N j μ represents the number of target and interference trajectory points, respectively. t and μ j These represent the mean Euclidean distances between the target and interference trajectory points, respectively.

[0125] Finally, based on the variance, the true target and dragging interference at the coordinates (x, y) of each trajectory point are determined, thus completing the identification of dragging interference.

[0126] In this embodiment of the invention, the following simulation experiment is used for further explanation. The simulation parameters are shown in Table 1. The transmitting and receiving arrays are co-located and the array spacing is half a wavelength. The simulated noise is additive white Gaussian noise.

[0127] Table 1 Simulation Parameters

[0128]

[0129] In the simulation experiment, the relative velocity between the target and the radar was limited to v = 300 m / s. The results after trajectory segmentation are as follows: Figure 4 As shown in the figure, the trajectories of the target and the interference are separated by white dashed lines, and each trajectory point is marked with a black diamond. The simulation results are as follows... Figure 5 As shown, it can be seen that whether it is head-on or tail-to-tail, the accuracy of identifying dragged interference gradually increases with the increase of signal-to-noise ratio; when the signal-to-noise ratio is greater than -22dB, the accuracy of identifying dragged interference is better than 95%.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0131] The above descriptions are merely some embodiments of the present invention. Those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the scope of protection of the present invention.

Claims

1. A dragged interference identification method based on transmit and receive spatial frequency spectra, characterized in that, Includes the following steps: Step S1: Construct the transmitter array and receiver array of the FDA-MIMO radar, and calculate the transmitted signal of the transmitter array element according to the preset frequency increment. Step S2: Calculate the target echo and interference signal of the receiver array based on the transmitted signal of the transmitter array element calculated in step S1, and obtain the signal vector of the target echo and interference signal within a coherent processing interval, i.e. the signal vector of the target and interference. Step S3: Calculate the covariance matrix based on the signal vectors of the target and the interference, and estimate its Capon spectrum; Step S4: Accumulate the Capon spectrum of multiple coherent processing intervals, then perform peak search to obtain the trajectory of the target and interference, and segment the trajectory; Step S5: Based on the obtained trajectory segmentation results, calculate the Euclidean distance between the target trajectory and the dragging interference trajectory, and calculate the variance of the two; identify the interference based on the magnitude of the variance, and the one with the smaller variance is the dragging interference. Step S1 includes the following steps: Step S11: Preset the number of array elements at the FDA-MIMO radar transmitter and receiver, and set the array element spacing according to the center frequency of the transmitted signal. Among them, wavelength , The center frequency of the transmitted signal. The speed of light; Based on the number of array elements and the spacing between array elements at the transmitting end The transmitting array is obtained based on the number of array elements and the element spacing at the receiving end. Obtain the receiver array; The carrier frequency of the transmitter array elements is calculated based on the preset frequency increment: in, For the first in the transmitter array The array element carrier frequency of each array element Indicates frequency offset; S12, Based on the carrier frequency of the array elements Calculate the transmitted signal of the transmitter array element: in, For the first transmitter The transmitted signal of each array element For the first transmitter The baseband signal transmitted by each array element is calculated as follows: Where j is an imaginary number, π is a constant, t is a time variable, and B is the signal bandwidth. Indicates pulse width as rectangular pulse, Represents the natural exponential function; Step S2 specifically includes the following steps: Step S21: Calculate the first [element] based on the transmitted signal of the transmitting array element. The target echo signal and interference signal received by each receiving array element: in, Indicates the target echo. Indicates interference signal. Represents the target complex scattering coefficient. Indicates the strength of interference forwarding. Indicates target delay, Indicates interference delay. Indicates the number of elements in the transmitting array; Step S22: Perform multi-channel mixing and matched filtering on the target echo and interference signal to obtain the FDA-MIMO radar's first signal. Target echo and interference signal vectors for each pulse: in, Indicates FDA-MIMO radar number The target echo and interference signal vector of each pulse , They represent the first The target echo vector and interference signal vector of each pulse Indicates the receiving guide vector. Indicates the launch steering vector. Indicates the direction of arrival of the target or interference wave. Indicates the distance between the target or interference and the FDA-MIMO radar. and These represent the directions of arrival of the target and the interference waves, respectively. and These represent the distances from the target and the interference to the FDA-MIMO radar, respectively. Indicates the Kronecker product. It represents the Hadamardi (or Hadama) stack; Step S23: Represent the target echo and interference signal vectors of all pulses in a coherent processing interval as target and interference signal vectors. : in, This represents the number of pulses in a coherent processing interval; Step S3 specifically includes the following steps: Step S31: Based on the target and interference signal vectors obtained in step S2 Calculate its covariance matrix: in, Represents the covariance matrix. Expressing expectations, Represents the transpose of a matrix; Step S32: Use the Capon spectral estimation algorithm to obtain the FDA-MIMO radar's first... The spatial spectrum of each coherent processing interval is represented as: in, , Indicates the number of accumulated spatial spectra. It represents the set consisting of all integers.

2. The method as described in claim 1, characterized in that, The transmitting array and the receiving array are co-located.

3. The method as described in claim 1 or 2, characterized in that, Step S4 specifically includes the following steps: S41. Accumulate Capon spectra from multiple coherent processing intervals: Sum the envelopes of all spatial spectra estimated by Capon spectra to obtain the trajectory maps of the target and interference. S42. Perform peak search on the trajectory maps of the target and the interference, and obtain the coordinates of each peak in the spatial spectrum. Each obtained coordinate is used as a trajectory point. S43. Using the divide-and-conquer method and disjoint-set data structure to separate the trajectory points obtained from the target and interference trajectories, we obtain the target trajectory and the interference trajectory.

4. The method as described in claim 3, characterized in that, in step... S5 includes: The coordinates of each trajectory point are traversed sequentially in the trajectory map, which includes both the target trajectory and the interference trajectory. Calculate the Euclidean distance between the target trajectory point and the coordinates of each trajectory point. Euclidean distance to interference trajectory points : in, They respectively represent the locations The distance between the target and the interference to the FDA-MIMO radar. They respectively represent the locations The angles of the target and interference points; Calculate the variances of the target and interference trajectory points based on Euclidean distance: ； in, and Let represent the variances of the Euclidean distances between the target and interference trajectory points, respectively. and These represent the number of target and interference trajectory points, respectively. and These represent the mean Euclidean distances between the target and the interference trajectory points, respectively. like Greater than Then the coordinates of the current trajectory point The corresponding target is the real target, and the corresponding interference is drag-type interference.

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