A Detection Method for Small and Weak Targets in a Strong Moving Clutter Environment

Abstract

The present invention belongs to the technical field of target detection methods, and particularly relates to a method for detecting small and weak targets in a strong motion clutter environment, including the following steps: suppressing the signal sidelobes after radar echo pulse compression within the coherent processing interval by using the method of adding a Hamming window in the frequency domain, and then performing constant false alarm rate detection after moving target detection processing; determining whether the targets detected by the CFAR detector are strong motion clutter; accurately estimating the spectral center of the strong motion clutter by using a spectral center compensation method based on a rearranged spectrogram, and then compensating the spectral center of the strong motion clutter to the zero-frequency position; suppressing the strong motion clutter by using the method of an adaptive high- and low-order pulse pair cancellation moving target indication filter; and restoring the spectral position of the data after suppressing the strong motion clutter. The present invention avoids the mutual influence caused by noise and when the spectral center energies of multiple targets are relatively close, and can accurately estimate the spectral center position of the strong motion clutter.

Description

Technical Field

[0001] The present invention belongs to the technical field of target detection methods, and particularly relates to a method for detecting small and weak targets in a strong motion clutter environment. Background Art

[0002] While the new business format of "low, slow, and small" unmanned aerial vehicles (UAVs) has developed rapidly, incidents of "unauthorized flight" and "flight interference" have occurred from time to time. Once a UAV "invades" an airport, the consequences can range from affecting flight takeoffs and landings and causing airport operations to be interrupted, to potentially dangerous accidents such as collisions with civil aviation aircraft. Small and weak UAVs pose a huge threat to airport order and civil aviation flight safety. Therefore, it is necessary to accelerate the research of technologies such as UAV target detection to ensure order and safety in areas such as airports.

[0003] In response to the frequent incidents of drones flying illegally and disrupting flights, typical drone detection technologies currently include radar detection, photoelectric detection, radio spectrum detection, acoustic wave detection, and broadcast automatic dependent surveillance technology. The radio spectrum method is to detect drones by capturing radio signals such as drone control, navigation, and digital transmission. The technical cost of this method is relatively low and it can work around the clock. When there is no information exchange between the drone in flight and the outside world, this type of technology cannot work effectively; the detection performance of drones in multi-device scenarios working in the common signal transmission frequency band of drones also needs to be studied. Photoelectric detection technologies such as optical imaging and infrared detection can meet the system requirements of real-time and visual detection over a long period of time, and can simultaneously detect fixed-wing, rotary-wing, and bionic drones and other small targets, but the detection range of this type of method is limited and is easily affected by weather interference. For example, this type of system cannot work effectively at night, in rain, snow, fog, sandstorms, and other weather environments. Some drone detection equipment uses modulated sound waves generated by the rotation of drone motors and rotors to detect drones. This method not only requires the establishment of a sound database containing various types of drones in advance, but also the noise generated by civil aircraft in the airport environment is relatively large, which greatly limits the detection distance of this type of method and is not suitable for specific occasions such as airports. The combined use of broadcast automatic dependent surveillance and geo-fencing technology can effectively prevent cooperative drones from invading important places such as airports, but it cannot solve the phenomenon of "illegal flying" invading airports. Radar technology with the characteristics of fast response speed and accurate positioning has matured. It is not restricted by the type of drone and can be detected in real time around the clock. It is currently the main technical means for detecting drone targets, but it also has disadvantages such as high cost, close-range blind spots, and difficulty in detection under obstructed environments. Existing detection systems often use multiple detection methods in combination to achieve the maximum detection effect. In the airport environment, there are many electronic devices and a complex electromagnetic environment. While quickly and effectively detecting the possible drone intrusion phenomenon in this scene, it cannot bring new safety hazards. Radar is often the core equipment for multi-sensor joint detection of drones. Research on radar detection of "low, slow, and small" drones has become an important scientific research direction.

[0004] The RCS of civil aircraft is much larger than that of consumer drones, so the use of radar equipment to detect drones in airport environments may be affected by civil aircraft. Existing drone monitoring radars often use a wider beam in azimuth. When the drone and civil aircraft are far apart in azimuth but close in slant range, the drone may not be detected because it is submerged in the clutter of the civil aircraft. Summary of the invention

[0005] In view of the technical problem that the existing UAV surveillance radar often uses a relatively wide beam in the azimuth direction, and when the UAV and the civil aviation aircraft are far apart in the azimuth direction but close in the slant range direction, the UAV may not be detected because it is submerged in the civil aviation aircraft clutter, the present invention provides a method for detecting weak targets in a strong moving clutter environment, which utilizes the characteristic that the target surveillance radar can obtain a long coherent pulse interval to suppress the strong moving clutter to detect weak targets.

[0006] To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0007] A method for detecting weak targets in a strong moving clutter environment, comprising the following steps:

[0008] S1. Use the method of adding a Hamming window in the frequency domain to suppress the sidelobes of the signal after pulse compression of the radar echo within the coherent processing interval, and then perform constant false alarm rate detection after moving target detection processing;

[0009] S2. Set a threshold to determine whether the target detected by the CFAR detector is strong moving clutter;

[0010] S3. Use the spectral center compensation method based on the rearranged spectrogram to accurately estimate the spectral center of the strong moving clutter, and then compensate the spectral center of the strong moving clutter to the zero-frequency position;

[0011] S4. Use the method of an adaptive high- and low-order pulse pair cancellation moving target indication filter to suppress the strong moving clutter;

[0012] S5. Restore the spectral position of the data after suppressing the strong moving clutter, and then return to S2 for processing until the CFAR detection result is output.

[0013] The S1 further includes: performing pulse compression processing on the radar echo signal to improve the range resolution while ensuring the detection range of the radar; secondly, performing frequency domain Hamming window processing on the range direction of the signal after pulse compression of the radar echo within the CPI to suppress the range direction echo sidelobes; then, using fixed-order MTI cancellation processing to remove static ground clutter; finally, performing MTD processing on the signal to improve the SNR of the target and using a CFAR detector to detect the MTD signal.

[0014] The method for determining whether the target detected by the CFAR detector in S2 is strong moving clutter is as follows: Since there is a huge difference in the radar cross section area between the weak target and the strong moving clutter, when the RCS value of the target detected by the CFAR detector is lower than the threshold, it is determined that a weak target not interfered by strong moving clutter is detected; if the RCS value of the target exceeds the threshold, it is considered that the target detected by the CFAR detector is strong moving clutter and the next step of processing is performed.

[0015] The method for estimating the spectral center of strong moving clutter in S3 is as follows: Process the data before MTD processing, after pulse compression and sidelobe suppression in the range cell where the target position is located through the RSP time-frequency analysis method, and perform maximum value detection on the data near the approximate Doppler spectral center position in different time sliding windows of RSP respectively. Then, take the mean of the Doppler spectral center data in each sliding window to obtain its accurate spectral center position.

[0016] The RSP time-frequency analysis method is as follows:

[0017] The RSP method rearranges the time-frequency spectrogram to enhance the aggregation of signal spectral energy in traditional time-frequency analysis. It concentrates the distribution of the signal in a certain time-frequency domain towards the points with stronger energy, thereby changing the concentration of spectral lines. The rearrangement is to redistribute the energy of any point (t′, f′) to the energy centroid position.

[0018]

[0019] The is the offset of the time-frequency spectrum at (t′, f′), and the W x is the time-frequency analysis result of the Wigner-Ville distribution of the signal x, and the δ(t) is the Dirac impulse function;

[0020] Based on the spectral center compensation method of RSP, first obtain the slant range position and approximate Doppler spectral center position of the target through range dimension and velocity dimension CFAR detection on the data after pulse compression, sidelobe suppression, and MTD processing respectively. Then, process the data before MTD processing, after pulse compression and sidelobe suppression in the range cell where the target position is located through the RSP time-frequency analysis method, and perform maximum value detection on the data near the approximate Doppler spectral center position in different time sliding windows of RSP respectively. Then, take the mean of the Doppler spectral center data in each sliding window to obtain its accurate spectral center position:

[0021]

[0022] The f c0 is the spectral center position, the m is the time window sorting value of RSP, m = 0, 1,..., M, and the arg max(·) obtains the Doppler frequency f m at a specific t i when RSP takes the maximum value at the moment; then the data obtained after compensating the spectral center to the Doppler zero-frequency position is:

[0023]

[0024] The is the signal after pulse compression processing of the radar echo signal, and the It is the signal after spectrum compensation processing for the signal after pulse pressure.

[0025] The method of using an adaptive high- and low-order pulse cancellation moving target indication (MTI) filter to suppress strong moving clutter in S4 is as follows: when the radial velocity difference between a weak target and strong moving clutter is large, the characteristic of the higher-order MTI filter with a deeper null and wider notch is used to remove strong moving clutter to a greater extent; when the radial velocity difference between a weak target and strong moving clutter is small, the characteristic of the lower-order MTI filter with a narrower notch is used to better retain the signal energy of the weak target while removing strong moving clutter.

[0026] The method of the higher-order MTI filter and the lower-order MTI filter for suppressing strong moving clutter includes the following steps:

[0027] S4.1: Calculate the Doppler frequency value corresponding to the amplitude of the frequency response of the higher-order pulse canceller being -20 dB according to the radar parameters, and then obtain the radial velocity difference Δv between the weak target and strong moving clutter at this time;

[0028] S4.2: Use a lower-order pulse cancellation MTI filter to filter out strong moving clutter from the data in the coherent processing interval (CPI), perform spectrum restoration on the processed data, and then perform constant false alarm rate (CFAR) detection;

[0029] S4.3: If a target is detected, make a decision on the CFAR detection result. When the radial velocity difference between the detected weak target and strong moving clutter is less than or equal to Δv, output the CFAR detection result; if the radial velocity difference between the two is greater than Δv, process the data whose spectrum has been shifted to the zero-frequency position before again using a higher-order pulse cancellation MTI filter, perform CFAR detection after restoring its spectrum position, and output the result to remove strong moving clutter to a greater extent and reduce the false detection probability caused by the residue of strong moving clutter.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] 1. The method of the present invention adopts the idea of suppressing strong moving clutter to detect weak UAV targets, and uses the characteristic that the new UAV surveillance radar can obtain a long coherent pulse interval to propose a spectrum center compensation method based on the rearranged spectrogram. This method avoids the mutual influence caused by noise and when the spectrum center energies of multiple targets are relatively close, can accurately estimate the spectrum center position of strong moving clutter, and avoids the problem of strong moving clutter residue and UAV energy loss caused by the null of the subsequent MTI canceller not being completely aligned with the clutter spectrum position.

[0032] 2. The method of the present invention can suppress clutter using an adaptive MTI filter according to the magnitude of the radial velocity difference between strong moving clutter and weak targets. When the radial velocity difference between the weak target and the strong moving clutter is large, the higher-order MTI filter with deeper nulls and wider "notches" is utilized to remove the strong moving clutter to a greater extent. When the radial velocity difference between the weak target and the strong moving clutter is small, the lower-order MTI filter with a narrower "notch" is used to better retain the signal energy of the weak target while removing the strong moving clutter. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can be obtained by extending the provided drawings.

[0034] The structures, ratios, sizes, etc. depicted in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention. Therefore, they do not have technical essence. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0035] Figure 1 is the flowchart of the steps of the present invention;

[0036] Figure 2 is the amplitude-frequency response curve of the MTI canceller of the present invention;

[0037] Figure 3 is the detection performance curve when SCR = -40dB in the environment of Experimental Group 1 of the present invention;

[0038] Figure 4 is the detection performance curve when SNR = 30dB in the environment of Experimental Group 1 of the present invention;

[0039] Figure 5 is the detection performance curve when SCR = -40dB in the environment of Experimental Group 2 of the present invention;

[0040] Figure 6 is the detection performance curve when SNR = 30dB in the environment of Experimental Group 2 of the present invention;

[0041] Figure 7 is the detection performance curve when SCR = -40dB in the environment of Experimental Group 3 of the present invention;

[0042] Figure 8 This is the detection performance curve when SNR = 30dB in the experimental group 3 environment of the present invention. Specific embodiments

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Apparently, the described embodiments are only a part rather than all of the embodiments of the present application. These descriptions are only for further illustrating the features and advantages of the present invention rather than limiting the claims of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without making creative efforts belong to the scope protected by the present application.

[0044] The following will further describe in detail the specific embodiments of the present invention in conjunction with the accompanying drawings and embodiments. The following embodiments are used to illustrate the present invention but are not used to limit the scope of the present invention.

[0045] As Figure 1 shown, a method for detecting small targets in a strong motion clutter environment provided in this embodiment includes the following steps carried out in sequence:

[0046] Step 1: Perform pulse compression processing on the radar echo signal to improve the range resolution while ensuring the detection range of the radar. Secondly, perform frequency-domain Hamming window processing on the range dimension of the signal after pulse compression of the radar echo within the CPI to suppress the range-side lobes of the echo. Then, perform fixed-order MTI cancellation processing to remove static ground clutter. Finally, perform MTD processing on the signal to improve the SNR of the target and use a CFAR detector to detect the signal of MTD;

[0047] Step 2: Set an appropriate threshold to determine whether the target detected by the CFAR algorithm is a strong motion clutter such as a civil aviation aircraft. Since the RCS of a consumer drone is significantly different from that of a civil aviation aircraft, when the RCS value of the target detected by the CFAR algorithm is lower than the set threshold, it is determined that a drone target not interfered by strong motion clutter is detected; if the RCS value of the target exceeds the threshold, it is considered that the target detected by the CFAR algorithm is a strong motion clutter of a civil aviation aircraft and proceed to the next step;

[0048] Step 3: Use the spectral center compensation method based on RSP to accurately estimate the spectral center of the strong motion clutter, and then compensate it to the Doppler zero-frequency position;

[0049] The RSP method rearranges the time-frequency spectrogram to enhance the concentration of signal spectral energy in traditional time-frequency analysis. It concentrates the distribution of the signal in a certain time-frequency domain towards the points with stronger energy, thereby changing the concentration of spectral lines. The key to its rearrangement is to redistribute the energy of any point (t′, f′) to the energy centroid position, as shown in Equation (1):

[0050]

[0051] In the formula, is the offset of the time-frequency spectrum at (t′, f′), and W x is the time-frequency analysis result of the Wigner-Ville distribution (WVD) of signal x, and δ(t) is the Dirac impulse function. The RSP method improves the time resolution and frequency resolution.

[0052] Based on the spectral center compensation method of RSP, first, the data after pulse compression, sidelobe suppression, and MTD processing are respectively used to obtain the slant range position of the target and the approximate Doppler spectral center position through range dimension and velocity dimension CFAR detection. Then, the data before MTD processing, after pulse compression and sidelobe suppression in the range cell where the target position is located are processed by the RSP time-frequency analysis method, and the maximum value detection is respectively performed on the data near the approximate Doppler spectral center position in different time sliding windows of RSP. Then, the Doppler spectral center data in each sliding window are averaged to obtain its accurate spectral center position. As shown in Equation (2):

[0053]

[0054] Among them, f c0 is the spectral center position, m is the sorting value of the time window of RSP, m = 0, 1,..., M. arg max(·) obtains the Doppler frequency f m at a specific time t i when RSP takes the maximum value. Then, the data obtained after compensating the spectral center to the Doppler zero-frequency position is shown in Equation (3).

[0055]

[0056] Among them, is the signal after pulse compression processing of the radar echo signal, is the signal after spectral compensation processing of the pulse-compressed signal.

[0057] Step 4: Use the method of an adaptive high- and low-order pulse cancellation MTI filter to suppress strong moving clutter;

[0058] The MTI canceller is a linear phase filter, which is at f = nfr The frequency characteristic responses of all points are 0, and the "notch" characteristic can be used to suppress strong clutter. The amplitude-frequency response curves obtained by cancellation with different numbers of pulses (i.e., different orders of cancellers) are as shown in Figure 2 Figure []. The lower the order of the canceller, the narrower its "notch"; the higher the order, the deeper its null.

[0059] The method of using a fixed-order pulse cancellation MTI filter to filter clutter cannot be applied simultaneously to scenarios where the radial velocity difference between a weak target and strong moving clutter is relatively large or relatively small. When the radial velocity difference between a weak target and strong moving clutter is relatively large, a high-order pulse cancellation MTI filter can better remove strong moving clutter. However, when the radial velocity difference between the two is relatively small, the high-order MTI filter will also cause loss of the signal energy of the weak target while effectively removing strong moving clutter, thereby reducing the detection probability. The clutter suppression performance of a low-order pulse cancellation MTI filter is lower than that of a high-order MTI filter.

[0060] To address the above problems, an adaptive high- and low-order pulse cancellation MTI filter is used to remove strong moving clutter of civil aviation aircraft. When the radial velocity difference between a weak UAV target and strong moving clutter is relatively large, the deeper null and wider "notch" characteristics of the high-order MTI filter are used to remove strong moving clutter to a greater extent. When the radial velocity difference between a weak UAV target and strong moving clutter is relatively small, the narrower "notch" characteristic of the low-order MTI filter is used to better retain the signal energy of the UAV target while removing strong moving clutter. For the CPI data with the spectrum center of strong moving clutter shifted to the Doppler zero frequency, an adaptive MTI cancellation method is used for processing, and the specific implementation steps are as follows:

[0061] 1) Calculate the Doppler frequency value corresponding to the amplitude of the frequency response of the high-order pulse canceller being -20 dB based on the radar parameters, and then obtain the radial velocity difference Δv between the weak target and strong moving clutter at this time.

[0062] 2) Use a low-order pulse cancellation MTI filter to filter strong moving clutter from the data in the CPI, and perform CFAR detection after spectrum restoration of the processed data.

[0063] 3) If a target is detected, make a decision on the CFAR detection result. When the radial velocity difference between the detected weak target and strong moving clutter is less than or equal to Δv, output the CFAR detection result; if the radial velocity difference between the two is greater than Δv, process the data with the spectrum shifted to the zero frequency position again using a high-order pulse cancellation MTI filter, perform CFAR detection after spectrum position restoration and output the result to remove strong moving clutter to a greater extent and reduce the false detection probability caused by the residue of strong moving clutter.

[0064] Step 5: Restore the spectral position of the data after strong moving clutter suppression, and then return to Step 2 for processing until the CFAR detection result is output;

[0065] During the radar signal processing, only one accurate estimation of the spectral center position of the strong moving clutter is required to suppress the strong moving clutter in the data of different range cells within a CPI.

[0066] In this embodiment, based on accurately estimating the spectral center position of the strong moving clutter using the RSP-based spectral center compensation method and placing it at the Doppler zero frequency, the method of an adaptive MTI canceller is adopted to effectively remove the strong moving clutter. Finally, weak targets are effectively detected and a low false detection probability is ensured. The method of the embodiment is used to detect weak targets at different positions in the strong moving clutter and to detect weak targets that are simultaneously located at the main lobe position of the strong moving clutter but have different velocity differences. The experimental parameters are shown in Table 1 (in the table, civil aircraft are strong moving clutter and unmanned aerial vehicles are weak targets). The results of 100 Monte-Carlo experiments are as Figures 3 - 8 shown. In the experimental scenario, the clutter power of the unmanned aerial vehicle target and the civil aircraft both follow a negative exponential distribution, and the added noise is complex Gaussian white noise. Figure 4 、 Figure 5 are the detection probabilities at different SNRs when SCR = -40dB and the detection probabilities at different SCRs when SNR = 30dB under the parameters of Experiment Group 1 in Table 1, respectively; Figures 4 - 8 are the experimental results of Experiment Groups 2 - 3 in Table 1, respectively. During the experiment, according to the decision on the suppression result of the civil aircraft clutter by the adaptive MTI canceller, Figures 3 - 6 a high-order MTI filter is used in the scenario of Figure 7 、 Figure 8 a low-order MTI filter is used in the scenario of

[0067]

[0068] Table 1 Radar Target Simulation Parameter Table

[0069] Only the preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those of ordinary skill in the art, various changes can be made without departing from the spirit of the present invention, and all such changes should be included within the protection scope of the present invention.

Claims

1. A method for detecting small targets in a strong moving clutter environment, characterized in that: It includes the following steps: S1. Use the method of adding a Hamming window in the frequency domain to suppress the signal sidelobes after radar echo pulse compression within the coherent processing interval, and then perform constant false alarm rate detection after moving target detection processing; S2. Set a threshold to determine whether the targets detected by the CFAR detector are strong moving clutter; S3. Use the spectral center compensation method based on the rearranged spectrogram to accurately estimate the spectral center of the strong moving clutter, and then compensate the spectral center of the strong moving clutter to the zero-frequency position; the method for estimating the spectral center of the strong moving clutter in S3 is: Process the data before MTD processing, after pulse compression and sidelobe suppression in the range cell where the target is located through the RSP time-frequency analysis method, and perform maximum value detection on the data near the approximate Doppler spectral center position in different time sliding windows of the RSP, and then take the average of the Doppler spectral center data in each sliding window to obtain its accurate spectral center position; The RSP time-frequency analysis method is: The RSP method rearranges the time-frequency spectrogram to enhance the aggregation of signal spectral energy in traditional time-frequency analysis. It concentrates the distribution of the signal in a certain time-frequency domain towards the points with stronger energy, thereby changing the concentration of spectral lines. The rearrangement is to redistribute the energy of any point (t′, f′) to the energy centroid position The is the offset of the time-frequency spectrum at (t′, f′), and the W x is the time-frequency analysis result of the Wigner-Ville distribution of the signal x, and the δ(t) is the Dirac impulse function; Based on the spectral center compensation method of RSP, first perform range and velocity dimension CFAR detection on the data after pulse compression, sidelobe suppression, and MTD processing respectively to obtain the slant range position and approximate Doppler spectral center position of the target, and then process the data before MTD processing, after pulse compression and sidelobe suppression in the range cell where the target is located through the RSP time-frequency analysis method, and perform maximum value detection on the data near the approximate Doppler spectral center position in different time sliding windows of the RSP, and then take the average of the Doppler spectral center data in each sliding window to obtain its accurate spectral center position: The said f c0 is the center position of the spectrum. The said m is the time window sorting value of the RSP, where m = 0, 1,..., M, and the argmax(·) obtains the Doppler frequency f m at a specific time t i when the RSP takes the maximum value; then the data obtained after compensating the spectrum center to the Doppler zero frequency position is: The said is the signal after pulse compression processing of the radar echo signal, and the said is the signal after spectrum compensation processing of the signal after pulse compression; S4. Use the method of an adaptive high- and low-order pulse pair cancellation moving target indication filter to suppress the strong moving clutter; S5. Restore the spectral position of the data after suppressing the strong moving clutter, and then return to S2 for processing until the CFAR detection result is output.

2. A method for detecting small targets in a strong motion clutter environment according to claim 1, characterized in that: S1 also includes: Perform pulse compression processing on the radar echo signal to improve the range resolution while ensuring the detection range of the radar; Secondly, perform frequency domain Hamming window processing on the range direction of the signal after radar echo pulse compression within the CPI to suppress the range direction echo sidelobes; Then, use fixed-order MTI cancellation processing to remove static ground clutter; Finally, perform MTD processing on the signal to improve the SNR of the target and use a CFAR detector to detect the MTD signal.

3. A method for detecting small targets in a strong moving clutter environment according to claim 1, characterized in that: The method for determining whether the target detected by the CFAR detector in S2 is strong moving clutter is as follows: Since there is a huge difference in the radar cross section between the weak target and the strong moving clutter, when the RCS value of the target detected by the CFAR detector is lower than the threshold, it is determined that a weak target not interfered by strong moving clutter is detected; if the RCS value of the target exceeds the threshold, it is considered that the target detected by the CFAR detector is strong moving clutter, and the next step is processed.

4. A method for detecting small targets in a strong moving clutter environment according to claim 1, characterized in that: The method for suppressing strong moving clutter by using the adaptive high- and low-order pulse pair cancellation moving target indication filter in S4 is as follows: When the radial velocity difference between the weak target and the strong moving clutter is large, the deeper null and wider notch of the high-order MTI filter are used to remove the strong moving clutter to a greater extent; when the radial velocity difference between the weak target and the strong moving clutter is small, the narrower notch of the low-order MTI filter is used to better retain the signal energy of the weak target while removing the strong moving clutter.

5. A method for detecting small targets in a strong motion clutter environment according to claim 4, characterized in that: The method for suppressing strong moving clutter by the high-order MTI filter and the low-order MTI filter includes the following steps: S4.

1. Calculate the Doppler frequency value corresponding to the amplitude of the frequency response of the high-order pulse canceller being -20 dB according to the radar parameters, and then obtain the radial velocity difference Δv between the weak target and the strong moving clutter at this time; S4.

2. Use the low-order pulse cancellation MTI filter to filter the strong moving clutter from the data in the CPI, and perform CFAR detection after spectrum restoration of the processed data; S4.

3. If a target is detected, the CFAR detection result is judged. When the radial velocity difference between the detected weak target and the strong moving clutter is less than or equal to Δv, the CFAR detection result is output; if the radial velocity difference between the two is greater than Δv, the data whose spectrum has been shifted to the zero-frequency position before is processed again by the high-order pulse cancellation MTI filter, and CFAR detection is performed after restoring its spectrum position and the result is output to remove the strong moving clutter to a greater extent and reduce the false detection probability caused by the residue of the strong moving clutter.

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