An Adaptive Cancellation Method for Sidelobe Sweeping Interference

Abstract

The present invention relates to an adaptive cancellation method for sidelobe swept-frequency interference, belonging to the field of anti-interference in radar signal processing. The present invention determines the interference area by comparing the main channel and the blanking channel, and calculates the cancellation weight coefficients using the snapshot data of each channel corresponding to the interference position, effectively utilizing the interference data. Compared with the prior art, the features of the present invention are as follows: adaptively determining the interference area and obtaining effective interference data, using this data to calculate the weight coefficients to improve the cancellation effect; performing cancellation processing on the blanking channel to reduce the remaining interference in the blanking channel and avoid excessive blanking of the main channel by the blanking channel.

Description

Technical Field

[0001] The present invention belongs to the field of anti - interference in radar signal processing, and relates to a method for realizing adaptive cancellation of sidelobe swept - frequency interference. Swept - frequency interference has the characteristics of intermittence, pseudo - pulse, and randomness. Traditional sidelobe cancellation methods often cannot collect effective snapshot data of interference, which affects the calculation of weights, and further affects the cancellation effect and convergence speed, resulting in excessive shadowing of the main channel by the remaining strong interference in the shadowing channel. To solve this problem, the present invention adopts joint processing of the main channel and the shadowing channel, adaptively collects interference sample data, can effectively combat swept - frequency interference, and can be widely applied to the field of radar anti - interference. Background Art

[0002] Radar anti - interference has become an important part of modern electronic warfare. Sidelobe cancellation and sidelobe blanking are essential anti - interference measures for modern radar systems. Sidelobe cancellation is a technical means to suppress jamming by active jammers. However, for smart intermittent interference, the convergence speed of the cancellation system is slow and it cannot cope with the continuous changes of external interference. Sidelobe blanking is mainly used to suppress low - duty - cycle pulse interference or stored - and - forwarded interference entering from the sidelobes of the radar antenna. However, if the sidelobe cancellation in the shadowing channel is not well - handled, the remaining interference in the shadowing channel will obscure the targets in the main channel, thereby affecting target detection. How to effectively suppress smart intermittent interference is the main problem that needs to be solved urgently in current radar anti - interference.

[0003] The swept - frequency bandwidth of the swept - frequency interference signal is greater than the receiving bandwidth of the radar receiver. The energy of the interference signal cannot all enter the receiver. Therefore, after being detected by the receiver, its time - domain waveform changes from a continuous wave to an intermittent pseudo - pulse waveform that appears intermittently, and the appearance frequency is inconsistent with the radar working rhythm. The time it appears in the radar receiver is not fixed and shows great randomness. Traditional sidelobe cancellation uses fixed gates to collect interference, often unable to collect effective snapshot data of interference, which affects the calculation of weights, and further affects the cancellation effect and convergence speed. The remaining interference in the shadowing channel after cancellation is relatively large, causing excessive shadowing of the main channel. Therefore, an adaptive cancellation method for sidelobe anti - swept - frequency interference is proposed. This method combines the main channel and the shadowing channel, adaptively determines the interference area, and selects interference snapshot data for weight calculation, realizing real - time tracking of interference and effectively combating sidelobe swept - frequency interference. Summary of the Invention

[0004] Technical Problems to be Solved

[0005] Aiming at the sidelobe swept - frequency interference with intermittence, pseudo - pulse, and strong randomness, the sidelobe cancellation process cannot collect effective snapshot data of interference in real time, resulting in poor cancellation effect and excessive shadowing of the main channel by the shadowing channel. The present invention proposes an adaptive cancellation method for sidelobe swept - frequency interference.

[0006] Technical solution

[0007] An adaptive cancellation method for sidelobe sweep interference, characterized by the following steps:

[0008] Step 1: In the rest area, perform modulo operations on the data of the main channel and the blanking channel respectively. Multiply the modulus value of the main channel by the threshold factor K to obtain the main channel modulus threshold;

[0009] Step 2: Compare the modulus value of the blanking channel with the main channel modulus threshold. If the modulus value of the blanking channel is greater than the main channel modulus threshold, mark the distance unit as the interference position, and cache the data of the main channel, the blanking channel, and all auxiliary channels corresponding to this distance unit to obtain an effective snapshot of the interference;

[0010] Step 3: When sidelobe interference is detected and the time slice for calculating the weight coefficient arrives, read out the interference snapshot data and send it to the weight coefficient module for weight coefficient calculation at the same time;

[0011] Step 4: During the time slice of sidelobe cancellation processing, perform sidelobe cancellation processing on the main channel and the blanking channel using the weight coefficient calculated in the rest area.

[0012] A further technical solution of the present invention: K described in Step 1 = 3.0.

[0013] A further technical solution of the present invention: The weight coefficient calculation in Step 3 is as follows:

[0014] Assume that the interference snapshot of the main channel is d, and the interference snapshot data of N auxiliary channels is X. Calculate the cross-correlation matrix r of the main channel and the auxiliary channels respectively Xd , the autocorrelation matrix R of the auxiliary channels XX . For system robustness, perform diagonal loading on the autocorrelation matrix, and the loading amount is σ n ; perform an inverse operation on the autocorrelation matrix (R XX +σ n I) to obtain the final cancellation weight coefficient W opt =(R XX +σ n I) -1 ·r Xd , and store this weight coefficient.

[0015] A further technical solution of the present invention: The cancellation processing formula in Step 4 is d - W opt *X.

[0016] Beneficial effects

[0017] The present invention provides an adaptive cancellation method for sidelobe swept interference. For intermittent, pulse-like, and highly random swept interference, traditional methods cannot collect effective interference data, which affects the calculation of weight coefficients and results in poor cancellation effects. As a result, the shadow channel causes excessive shadowing of the main channel, affecting target detection. By comparing the main channel and the shadow channel, the present invention determines the interference area and calculates the cancellation weight coefficients using the snapshot data corresponding to the interference position, effectively utilizing the interference data. Compared with the prior art, the present invention has the following beneficial effects:

[0018] 1. Adaptively determine the interference area and obtain effective interference data, and use this data to calculate the weight coefficients to improve the cancellation effect;

[0019] 2. Perform cancellation processing on the shadow channel to reduce the remaining interference in the shadow channel and avoid excessive shadowing of the main channel by the shadow channel. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings are only for the purpose of showing specific embodiments and are not considered to be a limitation of the present invention. Throughout the drawings, the same reference signs represent the same components.

[0021] Figure 1 To show the flow chart of the method of the present invention;

[0022] Figure 2 To show the radiation patterns of the main channel and the shadow channel used in the present invention;

[0023] Figure 3 To show the data processing timing diagram of the present invention;

[0024] Figure 4 To show the flow chart of the weight coefficient calculation of the present invention;

[0025] Figure 5 To show the flow chart of the sidelobe cancellation processing of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0027] The present invention combines the main channel and the shadow channel, adaptively determines the position of the interference area by comparing the modulus values of the two channels, selects snapshot data in the interference area, and uses this data to obtain the weight coefficients for sidelobe cancellation. The present invention includes the following steps:

[0028] Step 1: Perform modulo operations on the main channel and the shadow channel data respectively in the rest area. Multiply the modulo value of the main channel by the threshold factor to obtain the main channel modulo value threshold.

[0029] Step 2: Compare the modulo value of the shadow channel and the main channel modulo value threshold. If the modulo value of the shadow channel is greater than the main channel modulo value threshold, mark the range cell as an interference position, and cache the data of the main channel, shadow channel, and all auxiliary channels corresponding to this range cell to obtain an effective snapshot of the interference.

[0030] Step 3: When sidelobe interference is detected and the time slice for calculating the weight coefficients arrives, read out the interference snapshot data and send it to the weight coefficient module for weight coefficient calculation at the same time.

[0031] Step 4: During the time slice of sidelobe cancellation processing, perform sidelobe cancellation processing on the main channel and the shadow channel using the weight coefficients calculated in the rest area.

[0032] The interference samples of the present invention need to be collected in the radar rest area. The present invention takes one main channel and one shadow channel as an example for illustration. The main channel and the shadow channel are the synthesized beams. Configure the number of rest area pulses within the coherent processing interval (CPI) to be 1, the actual number of working pulses to be M, the rest area time length to be T, the pulse repetition period (PRT) to be T1, and the number of auxiliary channels of the sidelobe cancellation system to be N. The specific implementation method is as follows:

[0033] 1. To avoid collecting clutter data, the interference snapshot needs to be collected in the radar rest area. During the radar rest area, the radiation is turned off and only reception is performed. At the same time, in order to cope with the randomness and variability of interference, it is necessary to calculate the cancellation weight coefficients of the current CPI in real time. Therefore, it is necessary to calculate the cancellation weight coefficients of the current CPI using the collected snapshot data in the rest area. The length T of the rest area needs to include the pulse repetition period (PRT) T1 and the weight coefficient calculation time T2, that is, T>T1+T2, to ensure that the collected interference data can guarantee full coverage of the radar working power. The data timing diagram is as Figure 3 shown. The actual working pulses are in the time slice of sidelobe cancellation processing. During this time slice, sidelobe cancellation processing is performed using the weight coefficients calculated in the rest area.

[0034] 2. The radiation patterns of the main channel and the shadow channel are as Figure 2As shown, the sidelobe of the shadow channel is about 3 dB higher than that of the main channel. For the swept-frequency interference entering from the sidelobe, the shadow channel will be larger than the main channel. Therefore, the comparison between the shadow channel and the main channel can be used to detect the sidelobe interference and determine the location of the interference. First, perform modulus processing on the main channel data and the shadow channel data. Multiply the modulus value of the main channel data by the threshold coefficient K to obtain a threshold for the main channel modulus value. In the present invention, K is configured to be 3.0. Compare the modulus value of the shadow channel with the threshold of the main channel modulus value. If the modulus value of the shadow channel is larger, it indicates that there is interference in this range cell. Latch the range cell number corresponding to the interference, and cache the original data of the main channel, the shadow channel, and N auxiliary channels corresponding to this range cell number. At the same time, send the interference detection result to the read control module to control the reading of the cached interference snapshot data. As Figure 1 shown in step S1 of

[0035] 3. In Figure 1 step S2, the read control module counts the number of interference snapshots for each main channel. When the number of interference snapshots is greater than N*4, new interference data will no longer be received. If the number of samples collected within time T1 is less than N*4, the actual number of samples is used for weight coefficient counting. When the time slice of the weight coefficient arrives, judge the interference detection result in step S1 to detect whether there is sidelobe interference currently. If there is interference, start reading the interference snapshot data, read out the stored interference snapshot data in sequence, and send it to the weight coefficient calculation module for weight coefficient calculation.

[0036] 4. The weight coefficient calculation is as Figure 4 shown. Taking the weight coefficient calculation of the main channel as an example for illustration. The interference snapshot of the main channel is d, and the interference snapshot data of N auxiliary channels are X. Calculate the cross-correlation matrix r Xd of the main channel and the auxiliary channels respectively, and the autocorrelation matrix R XX of the auxiliary channels. For system robustness, perform diagonal loading on the autocorrelation matrix, and the loading amount is σ n . Perform inverse processing on the autocorrelation matrix (R XX +σ n I) to obtain the final cancellation weight coefficient W opt =(R XX +σ n I) -1 ·r Xd , and store this weight coefficient. The weight coefficient calculation of the shadow channel is the same as that of the main channel. Replace the main channel data with the shadow channel data for calculation to obtain the cancellation weight coefficient of the shadow channel.

[0037] 5. During the sidelobe cancellation processing time slice, use the main channel weight coefficients calculated in step 4 and the auxiliary channel to perform weighted summation processing, and then subtract the weighted sum of the auxiliary channel from the main channel to obtain the cancellation processing result of the main channel, that is, d - W opt *X. The same processing is applied to the blanking channel to obtain the cancellation processing result of the blanking channel. The cancellation processing is as Figure 5 shown.

[0038] As mentioned above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or replacements, and these modifications or replacements should be covered within the protection scope of the present invention.

Claims

1. An adaptive cancellation method for sidelobe swept interference, characterized in that the steps are as follows: Step 1: In the rest area, perform modulo operations on the data of the main channel and the blanking channel respectively. Multiply the modulus value of the main channel by the threshold factor K to obtain the threshold of the modulus value of the main channel; Step 2: Compare the modulus value of the blanking channel corresponding to the range cell with the threshold of the modulus value of the main channel. If the modulus value of the blanking channel is greater than the threshold of the modulus value of the main channel, mark this range cell as the interference position, and cache the data of the main channel, the blanking channel, and all auxiliary channels corresponding to this range cell to obtain the effective snapshots of the interference; Step 3: When sidelobe interference is detected and the time slice for calculating the weight coefficient arrives, read out the interference snapshot data and send it to the weight coefficient module for calculating the weight coefficient at the same time; the calculation of the weight coefficient is as follows: The interference snapshots of the main channel are , The interference snapshot data of the auxiliary channels are respectively used to calculate the cross-correlation matrix of the main channel and the auxiliary channels and the autocorrelation matrix of the auxiliary channels For system robustness, diagonal loading is performed on the autocorrelation matrix with a loading amount of ; The inverse of the autocorrelation matrix ( ) is calculated to obtain the final cancellation weight coefficients , and the weight coefficients are stored; Step 4: During the sidelobe cancellation processing time slice, perform sidelobe cancellation processing on the main channel and the blanking channel by using the weight coefficients calculated from the rest area; the cancellation processing formula .

2. An adaptive cancellation method for sidelobe swept interference according to claim 1, characterized in that K in Step 1 is 3.0.

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