A method for suppressing strong clutter range sidelobe in MIMO staring radar

Abstract

The present invention discloses a method for suppressing strong clutter range sidelobes of a MIMO staring radar, and belongs to the field of radar. The present invention realizes adaptive suppression of strong clutter range sidelobes through two iterations of adaptive weight calculation and range image estimation, and is suitable for the situation where the range sidelobes of strong clutter in a complex environment submerge small targets. Compared with the existing conventional matched filtering, the present invention has better suppression capability for strong clutter range sidelobes, can make small targets submerged by strong clutter range sidelobes appear, and improves the detection performance of MIMO staring radar for small targets in a complex environment.

Description

Technical Field

[0001] The invention relates to the field of radars, and in particular to a method for suppressing strong clutter range sidelobe of a MIMO staring radar. Background Art

[0002] In recent years, various small aircraft and airborne objects (small rotorcraft drones, paragliders, etc.) have been flying indiscriminately, posing a serious threat to the safety of important facilities, airway safety, and the safety of people's lives and property. Radar is an important means of achieving low-altitude target detection, but in traditional mechanical scanning radars or phased array radars, due to the limited beam dwell time, ground clutter interference has a serious impact.

[0003] MIMO staring radar is very suitable for detecting low, slow and small targets in complex environments due to its long beam dwell time and high MTI filter order. MIMO staring radar often uses waveforms such as orthogonal frequency coding and orthogonal phase coding, which have the advantages of high data rate and strong anti-clutter and interference capabilities.

[0004] In MIMO staring radar detection, pulse compression processing of received signals through matched filtering is a common means to improve the signal-to-noise ratio. However, since the radar cross-sectional area of ​​such targets is usually small (much smaller than ground buildings, etc.), it is easy for strong clutter range sidelobes to submerge the target, greatly reducing the radar's detection performance. Therefore, improving the strong clutter range sidelobe suppression capability of MIMO staring radar is of great significance for the detection of low, slow and small targets in complex environments. Summary of the invention

[0005] In view of this, the present invention proposes a method for suppressing strong clutter range sidelobes of a MIMO staring radar. The method utilizes two iterations of adaptive weight calculation and range profile estimation to achieve adaptive suppression of strong clutter range sidelobes and improve the detection capability of the MIMO staring radar for small targets in complex environments.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for suppressing strong clutter range sidelobe of a MIMO staring radar comprises the following steps:

[0008] Step 1: Turn off the radar transmitter, intercept the received signal vector n0(k) of length N, where N is the length of a transmission signal cycle, and calculate the noise correlation matrix:

[0009]

[0010] Wherein, M is a positive integer greater than 10 times N, and the superscript H represents the conjugate transpose;

[0011] Step 2: Turn on the radar transmitter and at time n, transmit the signal S r (n), intercept the vector of length N:

[0012] s(n)=[S r (n),S r (n+1),...,S r (n+N-1)] T

[0013] Step 3: Extract a vector of length N from the received signal x(n):

[0014]

[0015] Step 4: Set i = 1, 2, ..., K, where K is the total number of range units that the radar needs to measure, and estimate the i-th initial range image a i0 , get K initial distance images a 10 ,a 20 ,…,a K0 ;

[0016] Step 5, let i=1;

[0017] Step 6: According to the initial distance image a i0 , transmitted signal vector s(n), received signal vector And the noise correlation matrix R n , estimate the optimal filter coefficient w(i);

[0018] Step 7, estimate the final i-th distance image:

[0019]

[0020] Step 8, let i=i+1, and repeat steps 5 to 8 until i=K.

[0021] Furthermore, in step 4, for the i-th initial distance image a i0 The estimation method is:

[0022] Step 401, calculate the initial filter coefficients of the i-th range image:

[0023]

[0024] Step 402, multiply the initial filter coefficients by the received signal vector to obtain an initial range profile estimate:

[0025]

[0026] Furthermore, in step 6, the estimation method of the optimal filter coefficient w(i) is:

[0027] Step 601, calculate the signal vector covariance matrix:

[0028]

[0029] Step 602, calculate the optimal filter coefficients:

[0030] w(i)=|a i0 | 2 (C0+R n ) -1 s(ni).

[0031] Compared with the background technology, the present invention has the following advantages:

[0032] (1) The invention has a better ability to suppress strong clutter range sidelobes than conventional matched filtering, and can make small targets submerged by strong clutter range sidelobes appear, thereby improving the detection performance of MIMO staring radar for small targets in complex environments.

[0033] (2) For orthogonal frequency coded signals and orthogonal phase coded signals commonly used in MIMO staring radar, conventional matched filtering is difficult to reduce range side lobes, but the present invention is applicable to both.

[0034] (3) The present invention is applicable to situations where the range sidelobes of strong clutter in complex environments affect the detection of weak small targets. Through two iterations of adaptive weight calculation and range profile estimation, the adaptive suppression of the range sidelobes of strong clutter is achieved, which can improve the detection performance of MIMO staring radar for small targets in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a comparison chart between the effects of this method and conventional matched filtering. DETAILED DESCRIPTION

[0036] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0037] A method for suppressing strong clutter range sidelobe of a MIMO staring radar comprises the following steps:

[0038] Step 1: Turn off the radar transmitter, intercept the received signal vector n0(k) of length N, where N is the length of a transmitted signal cycle, and calculate the noise correlation matrix

[0039]

[0040] Where M>>N;

[0041] Step 2: Turn on the radar transmitter and at time n, transmit the signal S r(n), intercept the vector of length N

[0042] s(n)=[S r (n),S r (n+1),...,S r (n+N-1)] T

[0043] Step 3: Extract a vector of length N from the received signal x(n):

[0044]

[0045] In the embodiment, the received signal includes echoes of two targets, located at the 20th and 70th range units, respectively, with signal-to-noise ratios of 25 dB and 5 dB, respectively, representing strong clutter and a small target;

[0046] Step 4: Set i = 1, 2, ..., K, where K is the total number of range units that the radar needs to measure, and estimate the i-th initial range image a i0 , get K initial distance images a 10 ,a 20 ,…,a K0 ;

[0047] In step 4, for the i-th initial distance image a i0 The estimation method is:

[0048] 401, calculate the initial filter coefficients of the i-th range image:

[0049]

[0050] 402, the initial filter coefficients are multiplied and summed with the received signal vector to obtain an initial range profile estimate:

[0051]

[0052] Step 5, let i=1;

[0053] Step 6: According to the initial distance image a i0 , transmitted signal vector s(n), received signal vector And the noise correlation matrix R n , estimate the optimal filter coefficient w(i);

[0054] In step 6, the estimation method of the optimal filter coefficient w(i) is:

[0055] 601, calculate the signal vector covariance matrix:

[0056]

[0057] 602, calculate the optimal filter coefficients:

[0058] w(i)=|a i0 | 2 (C0+R n ) -1 s(ni)

[0059] Step 7, estimate the final i-th distance image:

[0060]

[0061] Step 8, let i=i+1, and repeat steps 5 to 8 until i=K.

[0062] The final distance image obtained by this method is Figure 1 shown. Figure 1 In the figure, the conventional matched filtering result only shows a peak representing a strong target at the 20-distance unit, and is submerged by the side lobes of the strong target at the 70-distance unit; the present invention obtains two peaks at the 20 and 70-distance units at the same time, representing two targets respectively, indicating that the present invention has a better ability to suppress the strong clutter range side lobes than the conventional matched filtering, and can make the small targets submerged by the strong clutter range side lobes appear.

[0063] In summary, compared with the existing conventional matched filtering, the invention has better suppression capability for strong clutter range sidelobes, can make small targets submerged by strong clutter range sidelobes appear, and improve the detection performance of MIMO staring radar for small targets in complex environments.

Claims

1. A method for suppressing strong clutter range sidelobe of MIMO staring radar, characterized in that: The following steps are involved: Step 1: Turn off the radar transmitter, intercept the received signal vector n0(k) of length N, where N is the length of a transmission signal cycle, and calculate the noise correlation matrix: Wherein, M is a positive integer greater than 10 times N, and the superscript H represents the conjugate transpose; Step 2: Turn on the radar transmitter and at time n, transmit the signal S r (n), intercept the vector of length N: s(n)=[S r (n),S r (n+1),...,S r (n+N-1)] T Step 3: Extract a vector of length N from the received signal x(n): Step 4: Set i = 1, 2, ..., K, where K is the total number of range units that the radar needs to measure, and estimate the i-th initial range image a i0 , get K initial distance images a 10 ,a 20 ,…,a K0 ; Among them, for the i-th initial distance image a i0 The estimation method is: Step 401, calculate the initial filter coefficients of the i-th range image: Step 402, multiply the initial filter coefficients by the received signal vector to obtain an initial range profile estimate: Step 5, let i=1; Step 6: According to the initial distance image a i0 , transmitted signal vector s(n), received signal vector And the noise correlation matrix R n , estimate the optimal filter coefficient w(i); where the estimation method of the optimal filter coefficient w(i) is: Step 601, calculate the signal vector covariance matrix: Step 602, calculate the optimal filter coefficients: w(i)=|a i0 | 2 (C0+R n ) -1 s(n-i); Step 7, estimate the final i-th distance image: Step 8, let i=i+1, and repeat steps 5 to 8 until i=K.

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