A method for joint processing of airborne radar clutter suppression and high-speed target coherent accumulation based on Radon-STAP

Abstract

This invention discloses a Radon-STAP-based method for joint processing of airborne radar clutter suppression and high-speed target coherent accumulation. Applied to the field of radar technology, it addresses the problem that direct spatio-temporal adaptive processing of high-speed targets exhibits range movement in the fast time dimension, leading to decreased clutter suppression performance and ineffective target energy accumulation. This invention transforms the echo signal to the frequency domain, converting target range movement into phase changes, and designs a corresponding move-Doppler joint compensation function based on the time-frequency characteristics of the echo signal within the fast time dimension. An improved spatio-temporal two-dimensional filter is applied to non-stationary clutter suppression and target coherent accumulation detection. This method achieves good clutter suppression and high-speed target coherent accumulation even when clutter correlation is poor in different range cells.

Description

Technical Field

[0001] This invention belongs to the field of radar technology, and specifically relates to a radar clutter suppression and high-speed target coherent accumulation technology. Background Technology

[0002] Airborne radar plays a crucial role in scenarios such as aerial detection and long-range early warning. Due to the altitude and speed of the aircraft platform, the clutter area illuminated by the airborne radar beam expands. Simultaneously, different clutter scattering points located on the ground or sea surface have significant differences in azimuth and velocity relative to the airborne radar, causing a shift in the center frequency and broadening of the clutter Doppler spectrum. If the target's Doppler frequency falls within the clutter Doppler frequency range, it will pose difficulties for effective detection by the airborne radar. Traditional clutter suppression methods only process in the time and frequency domains, making it difficult to effectively address these problems, resulting in difficulties in detecting weak targets within strong clutter. Space-Time Adaptive Processing (STAP) technology utilizes the space-time coupling characteristics of airborne radar clutter, employing two-dimensional adaptive filtering in both the spatial and temporal domains to effectively suppress strong clutter.

[0003] However, range migration may exist in high-speed target echo signals, leading to time-lapse steering vector mismatch in array signal processing and degrading the clutter suppression performance of STAP. Coherent accumulation of the target signal can effectively improve the signal-to-clutter-to-noise ratio (SNR) of the target echo signal. Traditional coherent accumulation mainly improves the SNR of the echo signal through envelope correction and phase compensation using parametric or non-parametric search methods. Typical parametric search coherent accumulation methods include Keystone Transform (KT) and Radon Fourier Transform (RFT). However, these methods focus more on achieving target accumulation detection and fail to combine clutter suppression processing.

[0004] To date, the Adaptive Radon Fourier Transform (ARFT), based on the maximum signal-to-clutter ratio criterion and the RFT algorithm, can simultaneously achieve clutter suppression and target signal accumulation, enabling effective detection of "low-observable" targets in cluttered environments. However, this method only utilizes single-channel echo information and does not consider the fusion of airborne multi-channel radar echoes. Furthermore, the method's sub-aperture division of coherent pulse samples limits its ability to suppress non-stationary clutter. The Time-Division STAP target refocusing algorithm in strong clutter environments achieves robust clutter suppression and target energy accumulation based on a semi-search mechanism. However, this method requires prior information or estimates of the target's spatial frequency and Doppler frequency, and the target may still exhibit range movement within the divided sliding window, leading to a degraded algorithm performance. Therefore, there is an urgent need to study a joint processing method for airborne radar clutter suppression and high-speed target coherent accumulation when clutter correlation is poor in different range cells. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a joint processing method for airborne radar clutter suppression and high-speed target coherent accumulation based on Radon-STAP. This method can achieve good clutter suppression and high-speed target coherent accumulation even when clutter correlation is poor in different range cells.

[0006] The technical solution adopted in this invention is: a joint processing method for airborne radar clutter suppression and high-speed target coherent accumulation based on Radon-STAP, comprising:

[0007] S1. Assume the airborne radar antenna is an N-element equidistant linear array with an element spacing of d. The elevation and azimuth angles of the high-speed target relative to the radar are θ and θ, respectively. In the absence of interference signals, the space-time steering vector of the airborne radar echo signal is:

[0008] X = aS + X c +n

[0009] Where a is the complex amplitude of the target signal, X c and n represent the space-time steering vectors of clutter and noise, respectively, and S is the space-time steering vector of the target signal;

[0010] S2. The discretized expression of the airborne radar echo signal space-time steering vector transformed to the frequency domain in step S1 is as follows:

[0011] SF r (f)=F PC (f)+X c (f)+n

[0012] Among them, F PC (f) represents the S-frequency domain representation, X c (f) represents X c Frequency domain representation;

[0013] S3. Simultaneously extract multiple consecutive distance cells as the cells to be detected, and then use the data of the reference cells as training samples to estimate the clutter covariance matrix.

[0014] S4. For each search velocity and search space frequency, design a corresponding walking-Doppler joint compensation function to rewrite the target space-time guidance vector;

[0015] S5. Using the clutter covariance matrix from step S3 and the target space-time steering vector rewritten in step S4, construct an improved space-time two-dimensional filter. The optimal solution for the weights of the improved space-time two-dimensional filter is expressed as follows:

[0016]

[0017] S6. Perform space-time adaptive filtering on the discretized signal of the unit to be detected in step S2 to obtain:

[0018] SF r (f) = W(f) H SF r (f)

[0019] S7. Performing an inverse Fourier transform on the filtered signal yields the result after clutter suppression, when the radial velocity and spatial frequency are respectively v t and Echo signal distance dimension accumulation results at time:

[0020] S r (t)=IFT(SF r (f))

[0021] The beneficial effects of this invention are as follows: This invention addresses the problem of weakened clutter suppression and target coherent accumulation performance caused by high-speed target range movement and poor clutter correlation between different range cells. By transforming the echo signal to the frequency domain, the target range movement is converted into a phase change. Based on the time-frequency characteristics of the echo signal within a fast time interval, a corresponding range-Doppler joint compensation function is designed. An improved space-time two-dimensional filter is used to achieve good clutter suppression and high-speed target coherent accumulation. The process included in this invention can be implemented using the Radon-STAP algorithm, which is beneficial for engineering implementation. Attached Figure Description

[0022] Figure 1 This is a flowchart of an embodiment of the present invention.

[0023] Figure 2 This refers to the low signal-to-noise ratio echo data received in this embodiment of the invention.

[0024] Figure 3 This is a schematic diagram of a distance unit, a detection unit, and a reference unit provided in an embodiment of the present invention.

[0025] Figure 4 This represents the clutter suppression and target coherent accumulation results of the method proposed in this embodiment of the invention.

[0026] Figure 5 This represents the clutter suppression and target coherent accumulation results of the STAP algorithm.

[0027] Figure 6 This represents the clutter suppression and target coherent accumulation results of the RFT algorithm. Detailed Implementation

[0028] To facilitate understanding of the technical content of this invention by those skilled in the art, the following description, in conjunction with the accompanying drawings, further illustrates the invention.

[0029] This invention primarily utilizes the scientific computing software Matlab R2022b for simulation experiments to verify its correctness. The embodiments of this invention are further described below with reference to the accompanying drawings.

[0030] Please see Figure 1 The present invention proposes a joint processing method for airborne radar clutter suppression and high-speed target coherent accumulation based on Radon-STAP, which is implemented through the following steps:

[0031] Step 1: Perform Fourier transform on the fast time dimension of the echo signal to obtain the target echo in the fast time frequency domain, and convert the target distance movement into phase change.

[0032] In this embodiment, it is assumed that the airborne radar antenna is an N-element equidistant linear array with an element spacing of d, and the elevation and azimuth angles of the high-speed target relative to the radar are θ and θ, respectively. In the absence of interference signals, the space-time steering vector of the airborne radar echo signal can be written as:

[0033] X = aS + X c +n

[0034] Where a is the complex amplitude of the target signal, X c Let and n represent the space-time steering vectors of clutter and noise, respectively, and S be the space-time steering vector of the target signal, whose expression is:

[0035]

[0036] in It represents the Kronecker product, and has and These represent the target's temporal steering vector and spatial steering vector, respectively:

[0037]

[0038] in, This represents the radial Doppler frequency shift of the target relative to the radar. The spatial frequency of the target, v t T represents the radial velocity of the target relative to the radar. r Let λ represent the pulse repetition period, λ be the wavelength of the transmitted signal, and M and N represent the number of array elements and the number of pulses, respectively. When the target moves across multiple array elements in the slower time dimension due to its high speed, the target echo signal can be written as:

[0039]

[0040] Where t represents the fast time variable, m and n represent the m-th array element and the n-th pulse, respectively, B represents the signal bandwidth, R0 is the initial distance to the target, c is the speed of light, and At This represents the amplitude of the signal envelope.

[0041] Due to the linear nature of the Fourier Transform (FT), performing FT processing on the fast time dimension of the echo signal does not affect the phase change characteristics of the clutter signal in the slow time and spatial domains. Therefore, the expression for the target echo in the frequency domain can be obtained as follows:

[0042]

[0043] Where rect is a rectangular function that merges phase terms containing n:

[0044]

[0045] Thus, the different envelope offsets of the target echo signal in the range dimension are converted into different phase change rates in the frequency domain, which can be compensated by the weight vector in STAP without affecting the clutter space-time steering vector properties.

[0046] In this embodiment, the system parameters used are: the number of airborne radar array elements M is 8, the transmitted signal is a linear frequency modulated signal, the number of coherent pulses N is 40, and the pulse repetition period T is... r The duration is 0.5 ms, the signal bandwidth B is 15 MHz, the wavelength λ is 0.15 m, and the element spacing d is half the wavelength. The target's radial velocity v... t The speed is 1200 m / s, the initial range cell R0 is 2000, and the cone angle of the target's airspace is 60°. In a strong clutter environment, the input signal-to-clutter ratio (SNR) is -32.21 dB. In a low SNR environment, the target trajectory is obscured by clutter.

[0047] The low signal-to-clutter ratio echo data received in this embodiment of the invention is as follows: Figure 2 As shown.

[0048] Step 2: Simultaneously extract multiple consecutive distance cells as the cells to be detected, and then use the data X of adjacent reference cells as training samples to estimate the clutter covariance matrix.

[0049] like Figure 3 As shown, a range cell is a cell that is divided according to the radar resolution in terms of radar detection range; the cell to be detected is the range cell where the target is located. Since the target moves quickly and causes distance movement, the cell to be detected in this embodiment is several consecutive range cells where the target is located; the reference cell is several range cells near the cell to be detected, and the data in the reference cell is the training sample.

[0050] Unlike traditional STAP, Radon-STAP requires simultaneously sampling K neighboring distance cells as the target cell. Then, data from the reference cell is used as training samples to estimate the clutter covariance matrix. Assuming L training samples, the clutter covariance matrix can be obtained using the maximum likelihood estimation criterion:

[0051]

[0052] Where X l Let H represent the spacetime steering vector of the l-th training sample, and let H represent the conjugate transpose operation in matrix operations.

[0053] In this embodiment, K is 200, the unit to be detected is a distance unit from 1900 to 2099, and L is 800.

[0054] Searching for speed and spatial frequency is a necessary operation for STAP. The search for the target's speed and spatial frequency will produce peaks at the speed and spatial frequency of the target to indicate the target's speed and angle.

[0055] Step 3: Based on the time-frequency characteristics of the target echo signal within a fast time interval, design a corresponding walking-Doppler joint compensation function for each search velocity and search space frequency, and rewrite the target space-time steering vector.

[0056] In this embodiment, after the signal of the unit to be detected is transformed to the fast time frequency domain, for each frequency unit f, when the search speed is v t The search space frequency is At that time, the spacetime weight vector of the target can be written as:

[0057]

[0058] in The expressions for the Kronecker product and the spatiotemporal compensation function are as follows:

[0059]

[0060] Here, T represents the "transpose" of the matrix.

[0061] Step 4: Use an improved space-time two-dimensional filter to perform space-time adaptive filtering on the echo signal in the detection unit.

[0062] The received spatiotemporal two-dimensional data are subjected to the linearly constrained minimum variance criterion (LCMV).

[0063] Solving for the weight vector using the minimum-variance method is equivalent to solving the following convex quadratic optimization problem:

[0064]

[0065] W(f) is the weight vector of the STAP space-time filter;

[0066] In this embodiment, the optimal solution for the STAP space-time filter weights can be expressed as:

[0067]

[0068] Among them, non-zero complex constants for The inverse matrix.

[0069] The discretized expression of the signal of the unit to be detected after transformation to the frequency domain is:

[0070] SF r (f)=F PC (f)+X c (f)+n

[0071] By performing space-time adaptive filtering on the signal of the unit to be detected, we can obtain:

[0072] SF r (f) = W(f) H SF r (f)

[0073] Step 5: Perform an inverse Fourier transform on the filtered signal. The maximum value is the target coherent accumulation result after clutter suppression, where the radial velocity and spatial frequency are the corresponding search values.

[0074] In this embodiment, performing an inverse Fourier transform on the filtered signal yields the result after clutter suppression, when the search speed and search space frequency are respectively v t and Echo signal distance dimension accumulation results at time:

[0075] S r (t)=IFT(SF r (f))

[0076] To demonstrate the effectiveness of this method, the same target and clutter background echoes under the same signal-to-clutter ratio (-32.21dB) were processed. Figure 4 The clutter suppression and target coherent accumulation results of the proposed method are presented. Figure 5 and Figure 6 The results of clutter suppression and target coherent accumulation using the STAP and RFT algorithms in conventional methods are presented. Due to the low signal-to-clutter ratio of the received signal, the clutter suppression and target coherent accumulation performance of existing methods are far inferior to those of the method obtained in this invention.

[0077] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Various modifications and variations can be made to the invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the scope of the claims of the invention.

Claims

1. A method for joint processing of airborne radar clutter suppression and high-speed target coherent accumulation based on Radon-STAP, characterized in that, include: S1. Assume the airborne radar antenna is an N-element equidistant linear array with an element spacing of d. The elevation and azimuth angles of the high-speed target relative to the radar are θ and θ, respectively. The space-time steering vector of the airborne radar echo signal is obtained as follows, in the absence of interference signals: X=aS+X c +n Where a is the complex amplitude of the target signal, X c and n represent the space-time steering vectors of clutter and noise, respectively, and S is the space-time steering vector of the target signal; S2. The discretized expression of the airborne radar echo signal space-time steering vector after transformation to the frequency domain in step S1 is as follows: SF r (f)=F PC (f)+X c (f)+n Among them, F PC (f) represents the S-frequency domain representation, X c (f) represents X c Frequency domain representation; S3. Simultaneously extract multiple consecutive distance cells as the cell to be detected, and then use several adjacent distance cells before and after the cell to be detected as reference cells, and use the data of the reference cells as training samples to estimate the clutter covariance matrix. S4. For each search velocity and search space frequency, design a corresponding walking-Doppler joint compensation function to rewrite the target space-time guidance vector; S5. Using the clutter covariance matrix from step S3 and the target space-time steering vector rewritten in step S4, construct an improved space-time two-dimensional filter, and obtain the optimal solution for the weights of the improved space-time two-dimensional filter as follows: Where W(f) represents the improved spatiotemporal two-dimensional filter weights, and h(f) represents the spatiotemporal weight vector of the target. Let μ denote the clutter covariance matrix, μ denote a non-zero complex constant, and f be the frequency element; S6. Perform space-time adaptive filtering on the discretized signal of the unit to be detected in step S2 to obtain: SF r (f)=W(f) H SF r (f) Where H represents the conjugate transpose operation; S7. Perform an inverse Fourier transform on the filtered signal to obtain the radial velocity and spatial frequency v after clutter suppression. t and The result of distance dimension accumulation of the echo signal at that time.

2. The method for joint processing of airborne radar clutter suppression and high-speed target coherent accumulation based on Radon-STAP as described in claim 1, characterized in that, For each frequency unit f, when the search speed is v t The search space frequency is At that time, the spacetime weight vector of the target is represented as: in, H represents the Kronecker product. t (f), h s (f) are the spatiotemporal compensation functions.

3. The method for joint processing of airborne radar clutter suppression and high-speed target coherent accumulation based on Radon-STAP according to claim 2, characterized in that, h t (f), h s (f) The expressions are as follows: Among them, v t T represents the radial velocity of the target relative to the radar. r λ represents the pulse repetition period, λ is the wavelength of the emitted signal, and c is the speed of light.

4. The method for joint processing of airborne radar clutter suppression and high-speed target coherent accumulation based on Radon-STAP according to claim 3, characterized in that, The weight vector is solved by applying the linear constraint minimum variance criterion to the received spatiotemporal two-dimensional data, i.e., by solving an optimization problem. The optimal solution for the improved space-time two-dimensional filter weights is obtained.

5. The method for joint processing of airborne radar clutter suppression and high-speed target coherent accumulation based on Radon-STAP according to claim 4, characterized in that, It is obtained based on the maximum likelihood estimation criterion.

6. The method for joint processing of airborne radar clutter suppression and high-speed target coherent accumulation based on Radon-STAP according to claim 4, characterized in that,

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