Flexible interference suppression method based on clustering rejection

Abstract

The invention discloses a smart interference suppression method based on cluster rejection, and relates to the technical field of guidance tracking radar signal processing, comprising the following steps: preliminarily suppressing a noise interference part through an interference cancellation method, and improving the detectability of a false target interference part; aiming at the characteristics that smart interference false targets are dense and the constant false alarm detection performance is poor, to-be-clustered points are selected in a step-by-step detection mode; according to the characteristic of space-time inconsistency of the interference false target, the existing detection points are clustered through angle deviation and position information; carrying out secondary clustering on the detection points which do not meet the clustering condition; and carrying out elimination and noise replacement on all the clustering points. According to the method, the interference can be eliminated in a clustering mode according to the characteristics of the smart noise interference, and the signal to interference ratio is effectively improved, so that the target signal is separated from the interference, and the problem that the target signal cannot be effectively separated from the smart noise interference by the existing interference suppression method is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of guided tracking radar signal processing, and in particular relates to a smart interference suppression method based on cluster elimination. Background Art

[0002] Missile-borne radars often need to work in complex electromagnetic environments. Enemy active jammers detect electromagnetic signals from missile-borne radars and release jammers after modulation to interfere with the normal operation of radar systems. Since active jammers are often very strong, radar systems often fail to detect target signals or only detect false targets generated by jammers. Therefore, suppressing active jammers is one of the main functions of radar systems.

[0003] Smart noise jamming is mainly used to jam pulse Doppler radar (PD) and pulse compression radar (PC). Smart noise jamming is to modulate the intercepted radar transmission signal with noise, which has both suppression and deception effects. For radars of different systems, smart noise jamming has different modulation methods: product modulation and convolution modulation.

[0004] Product-modulated smart noise jammer is mainly used to jam pulse Doppler radar. The mathematical model of product-modulated smart noise jammer is as follows:

[0005] J NBRN (t) = s(tt j )×n(t)+n(t)

[0006] In the formula, t j is the jammer forwarding delay; n(t) is the narrowband Gaussian noise signal; s(t) is the radar transmit signal.

[0007] The smart noise of product modulation will generate a narrow-band random Doppler frequency deviation at the position of each comb tooth of the PD radar signal. When the interference signal enters the pulse Doppler radar receiver, it will form a narrow-band Doppler noise in a narrow frequency range near the center frequency of its Doppler narrowband filter, blocking the filter passband, reducing the Doppler radar's spectral filtering ability for the target signal, and seriously affecting the radar's ability to detect the relative speed of the target, thereby achieving effective interference to the pulse Doppler radar.

[0008] Convolution-modulated smart noise jammer is mainly used to jam pulse compression radar. The mathematical model of convolution-modulated smart noise jammer is as follows:

[0009] J NBRN (t) = s(tt j )*n(t)+n(t)

[0010] The convolution-modulated smart noise jammer can obtain pulse compression processing gain after pulse compression processing, and generate dense false target signals behind the target to cover the target signal, taking into account the effects of deception and suppression jamming. In addition, since time domain convolution is equivalent to frequency domain product, the white noise spectrum is evenly distributed in the frequency domain, and the smart noise spectrum generated by multiplying it with the radar signal in-band spectrum can automatically cover the radar bandwidth and implement noise aiming frequency jamming on the radar in the frequency domain.

[0011] The convolution-modulated smart noise jammer has the characteristics of both dense false target jammer and suppressive jammer effect. Dense false targets have small intervals and wide coverage in the time-frequency domain, which makes them difficult to distinguish through traditional constant false alarm detection. However, they still have the characteristic of poor temporal and spatial consistency of active jammer.

[0012] Therefore, the current interference suppression method cannot effectively separate the target signal from the smart noise interference. Summary of the invention

[0013] The purpose of the present invention is to provide a smart interference suppression method based on clustering elimination to solve the problem that the current interference suppression method cannot effectively separate the target signal for smart noise interference.

[0014] To achieve the above object, the present invention adopts the following technical solution:

[0015] On the one hand, the present specification provides a smart interference suppression method based on cluster elimination, comprising:

[0016] Step 102, eliminating interference signals in the radar received signal by using an interference cancellation method to obtain a signal after interference elimination;

[0017] Step 104, selecting a number of points to be clustered from the signal after interference removal by a step-by-step detection method;

[0018] Step 106, clustering the points to be clustered based on the angular deviation and the position information to obtain a number of cluster points;

[0019] Step 108: remove and replace noise on a number of cluster points to obtain interference suppressed signals.

[0020] On the other hand, the present specification provides a smart interference suppression device based on cluster elimination, comprising:

[0021] The interference cancellation module is used to eliminate the interference signal in the radar receiving signal through the interference cancellation method to obtain the signal after the interference is eliminated;

[0022] A step-by-step detection module is used to select a number of points to be clustered from the signal after interference removal through a step-by-step detection method;

[0023] A spatiotemporal clustering module is used to cluster a number of points to be clustered based on angular deviation and position information to obtain a number of cluster points;

[0024] The interference removal module is used to remove and replace noise from several cluster points to obtain interference-suppressed signals.

[0025] Based on the above technical solution, this specification can achieve the following technical effects:

[0026] This method aims at the characteristics of convolutionally modulated smart noise interference, that is, it has both dense false target interference and suppressive interference effects, and dense false targets have small intervals and wide coverage in the time and frequency domains. Smart noise interference is eliminated by interference cancellation, step-by-step detection, time and space clustering, etc., which can effectively improve the signal-to-interference ratio, thereby separating the target signal from the interference, thereby solving the problem that the current interference suppression method cannot effectively separate the target signal for smart noise interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The figure is a flow chart of a smart interference suppression method based on cluster elimination in one embodiment of the present invention.

[0028] Figure 2 It is a time-frequency domain diagram of the original echo and the echo after channel coherent accumulation in one embodiment of the present invention.

[0029] Figure 3 This is a time-frequency domain diagram of the echo after preliminary suppression of noise interference and channel coherent accumulation in one embodiment of the present invention.

[0030] Figure 4 This is a time-frequency domain diagram of the echo after interference removal and channel coherent accumulation in one embodiment of the present invention.

[0031] Figure 5 It is a structural schematic diagram of a smart interference suppression device based on cluster elimination in one embodiment of the present invention.

[0032] Figure 6 The figure is a schematic diagram of an electronic device according to the present invention. DETAILED DESCRIPTION

[0033] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present invention will become more apparent from the following description and claims. It should be noted that the drawings are all in very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0034] It should be noted that, in order to clearly explain the content of the present invention, the present invention specifically cites multiple embodiments to further illustrate different implementations of the present invention, wherein the multiple embodiments are enumerated rather than exhaustive. In addition, for the sake of brevity of explanation, the contents mentioned in the previous embodiments are often omitted in the subsequent embodiments. Therefore, the contents not mentioned in the subsequent embodiments can refer to the previous embodiments accordingly.

[0035] Example 1

[0036] Please refer to Figure 1 , Figure 1 The figure shows a smart interference suppression method based on cluster elimination provided by this embodiment. In this embodiment, the method includes:

[0037] Step 102, eliminating interference signals in the radar received signal by using an interference cancellation method to obtain a signal after interference elimination;

[0038] In this embodiment, one implementation of step 102 is:

[0039] Step 202, obtaining a noise signal estimation value based on the radar received signal;

[0040] Step 204: Eliminate the noise signal estimation value from the radar received signal to obtain a signal after interference elimination.

[0041] Step 104, selecting a number of points to be clustered from the signal after interference removal by a step-by-step detection method;

[0042] In this embodiment, one implementation of step 104 is:

[0043] Step 302, performing fast Fourier transform on the signal after interference removal to obtain a range Doppler two-dimensional signal;

[0044] Step 304, quantizing the range Doppler two-dimensional signal to obtain a quantized echo signal;

[0045] Step 306, based on the maximum intensity value in the quantized echo signal and the preset noise quantization value, a number of thresholds and corresponding intensity values ​​are obtained in sequence, and the number of points in the quantized echo signal under each threshold that are greater than or equal to the threshold is recorded;

[0046] In this embodiment, one implementation of step 306 is:

[0047] Step 3061, determining the maximum intensity value of the quantized echo signal;

[0048] Step 3062, subtracting a plurality of multiples of the preset noise quantization value from the maximum intensity value in turn to obtain a threshold corresponding to each multiple;

[0049] Step 3063, recording in sequence the number of points in the quantized echo signal that are greater than or equal to the corresponding threshold at each multiple.

[0050] Step 308: Determine a number of points to be clustered under the current threshold based on the ratio of the number of points under two adjacent thresholds and a preset ratio threshold.

[0051] In this embodiment, one implementation of step 308 is:

[0052] Step 3081, compare the points of the previous threshold with the points of the current threshold to obtain a point ratio;

[0053] Step 3082, determine whether the point ratio is less than or equal to the preset ratio threshold. If so, take all points corresponding to the points under the previous threshold as the points to be clustered under the current threshold.

[0054] Step 106, clustering the points to be clustered based on the angular deviation and the position information to obtain a number of cluster points;

[0055] In this embodiment, one implementation of step 106 is:

[0056] Step 402, performing a first clustering of a number of points to be clustered based on the angle deviation, the position information and the angle threshold, to obtain clustering points that meet the clustering conditions and points to be clustered that do not meet the clustering conditions;

[0057] In this embodiment, one implementation of step 402 is:

[0058] Step 4021, calculating the angular deviation and the missile-target distance of all the points to be clustered and obtaining the coordinates of each point to be clustered in the search coordinate system based on the servo frame azimuth angle and the servo frame pitch angle;

[0059] Step 4021, record the to-be-clustered points whose ordinate values ​​are greater than the height threshold as clustering reference points;

[0060] Step 4021, subtract the angular deviation of all the points to be clustered from the angular deviation of the clustering reference point to obtain an angular difference;

[0061] In step 4021, the points to be clustered whose absolute values ​​of angle differences are less than the angle threshold are the clustering points that meet the clustering condition, and the remaining points to be clustered are the points to be clustered that do not meet the clustering condition.

[0062] Step 404: perform a second clustering on the points to be clustered that do not meet the clustering conditions to obtain a number of clustering points.

[0063] In this embodiment, one implementation of step 404 is:

[0064] Step 4041, taking a number of adjacent points of each cluster point that meets the clustering condition and removing the cluster points that meet the clustering condition, to obtain a number of remaining points to be clustered;

[0065] Step 4041: If the intensity value of the remaining points to be clustered is greater than the intensity value corresponding to the current threshold and less than or equal to the intensity value corresponding to the previous threshold, and the absolute value of the angle difference is less than the angle threshold or the ordinate value is greater than the height threshold, then the remaining points to be clustered are determined to be cluster points.

[0066] Step 108: remove and replace noise on a number of cluster points to obtain interference suppressed signals.

[0067] In this embodiment, one implementation of step 108 is:

[0068] Step 502, calculating and obtaining the phase values ​​of all cluster points;

[0069] Step 504, restoring the intensity value corresponding to the current threshold to the original intensity value corresponding to the radar received signal;

[0070] Step 506: Replace all cluster points based on the restored strength value of the current threshold and the phase value corresponding to each cluster point to obtain an interference suppressed signal.

[0071] Specifically, step 1: preliminarily suppress the noise interference part through the interference cancellation method to improve the detectability of the false target interference part. The specific steps are as follows:

[0072] The sum signal received by the radar is x(t), and its instantaneous amplitude and instantaneous phase are represented by A(t) and φ(t), respectively. The logarithm of x(t) is taken as ln(x(t)), and its real part and imaginary part are represented as R x (t) = Re[ln(x(t))] and I x (t) = Im[ln(x(t)].

[0073] The estimated value of the noise signal is recorded as It can be expressed as:

[0074]

[0075] To eliminate the interference signal from the radar receiving signal, we have:

[0076]

[0077] Step 2: In view of the characteristics of dense smart jamming false targets and poor constant false alarm detection, select the points to be clustered by step-by-step detection. The specific steps are as follows:

[0078] First, the signal after noise interference is removed Perform fast Fourier transform to obtain the range Doppler two-dimensional signal, denoted as X(ω), and the typical Fourier transform echo number is 64 or 128; then quantize X(ω), and the quantized echo is denoted as X qt (ω), the typical quantization bit is 8 bits; then select X qt (ω) and record the maximum value as q max and the maximum value points; subtract a fixed value δ from the maximum value qt After (typical δ qt is the quantized value of the local noise), that is, q max -δ qt , record X qt (ω) is greater than or equal to q max -δ qt Then subtract δ qt , that is, q max -2×δ qt , record X qt (ω) is greater than or equal to q max -2×δ qt The number of points, and so on, to get a series of points N i , and the corresponding intensity value q max -i×δ qt ; Then calculate N i-1 / N i , if N i-1 / N i ≤0.8, then it is considered that N i The number of points included is already a noise value and cannot be detected. Take N i-1 All corresponding points are points to be clustered.

[0079] Step 3: According to the characteristics of spatial and temporal inconsistency of false interference targets, cluster the existing detection points through angular deviation and position information. The specific steps are as follows:

[0080] First, calculate the pitch angle deviation for all points to be clustered Azimuth deviation Δψ R 、Range between projectile and target R R , the servo frame azimuth angle ψ is known R , Servo frame pitch angle Calculate the coordinates of all points to be clustered in the search coordinate system:

[0081] R op =[x op y op z op ] T =R om +A om R mp

[0082] Where:

[0083]

[0084] R om is the coordinate value of the projectile in the search coordinate system; θ is the pitch angle of the projectile in the search system; ψ is the yaw angle of the projectile in the search system; γ is the roll angle of the projectile in the search system, all of which are known quantities.

[0085] Then, mark all |y op The points to be clustered whose values ​​are greater than the height threshold are recorded as clustering reference points. The typical height threshold is 400m.

[0086] Taking the angular deviation of the clustering reference point as the reference, subtract the angular deviation of all the points to be clustered from the reference to obtain the angle difference. Mark all the points to be clustered whose absolute values ​​of the angle differences are less than the angle threshold as clustering points. The typical angle threshold is 0.2°.

[0087] Step 4: Perform secondary clustering on the detection points that do not meet the clustering conditions. The specific steps are as follows:

[0088] Take all the 8 points adjacent to the cluster point. If any of the 8 points is also a cluster point, it will not be included in the selection. The remaining points are recorded as q neigber If q neigber The strength is greater than N i The corresponding intensity, but less than or equal to N i-1 The corresponding intensity, and the absolute value of its angle difference is less than the angle threshold or |y op | value is greater than the height threshold, the point is also recorded as a cluster point.

[0089] Step 5: Eliminate all cluster points and replace noise. The specific steps are as follows:

[0090] Calculate the phase value of all cluster points, denoted as φ jl , take N i The corresponding intensity is restored to the original intensity corresponding to X(ω), denoted as A i , replace all cluster points in X(ω) with A i exp[jφ jl (t)].

[0091] Simulation case description:

[0092] The method proposed in the present invention is verified by simulation as follows.

[0093] In the simulation, a main lobe smart noise jammer with a signal-to-noise ratio of 20dB is set at 2° in the pitch direction (angular deviation is -2°). The target is located at the center of the beam (angular deviation is 0°), the signal-to-noise ratio is set to 20dB, and it is assumed that the target's echo signal is a linear frequency modulation signal with a bandwidth of 40MHz and a pulse width of 180us, the projectile pitch angle is -45°, the yaw and roll angles are 0°, and the servo angle is 0°.

[0094] Figure 2 The time-frequency domain diagram of the original echo and the echo after coherent accumulation of the channel, in which 64 echoes are used for coherent accumulation and the echo sampling number is 1900 points. The coordinate (950,1) in the figure is the location of the target. It can be seen from the figure that due to the influence of interference, it is difficult to distinguish the target from the interference; Figure 3 The time-frequency domain diagram of the echo after preliminary suppression of noise interference and channel coherent accumulation, where Figure a) is the range-Doppler perspective, the color in the figure represents the intensity, and Figure b) is the range-Doppler-amplitude perspective. After noise suppression, false target interference appears, showing the characteristics of many points, high density, and poor detectability. At the same time, the target can also be seen, but it cannot be separated because the false target interference is too dense; Figure 4 It is the time-frequency domain diagram of the echo after interference removal and channel coherent accumulation, where Figure a) is the distance-Doppler perspective, and the color in the figure represents the intensity, and Figure b) is the distance-Doppler-amplitude perspective. After interference removal and noise replacement, most of the false target interference is eliminated, especially the interference with relatively large interference-signal ratio is eliminated relatively thoroughly, and some points with relatively weak signal-to-interference are retained due to poor angle measurement accuracy, but the target is already the strongest point in the echo, and the signal-to-interference ratio is effectively improved; therefore, the smart interference suppression method based on clustering removal proposed in the present invention eliminates interference by clustering, which can effectively improve the signal-to-interference ratio and separate the target from the interference signal.

[0095] In summary, this method targets the characteristics of convolutionally modulated smart noise interference, that is, it has both dense false target interference and suppressive interference effects, and dense false targets have small intervals and wide coverage in the time-frequency domain. Smart noise interference is eliminated through interference cancellation, step-by-step detection, and spatiotemporal clustering, which can effectively improve the signal-to-interference ratio, thereby separating the target signal from the interference, thereby solving the problem that the current interference suppression method cannot effectively separate the target signal for smart noise interference.

[0096] Example 2

[0097] Please refer to Figure 5 , Figure 5 The figure shows a smart interference suppression device based on cluster elimination provided by this embodiment. In this embodiment, the device includes:

[0098] The interference cancellation module is used to eliminate the interference signal in the radar receiving signal through the interference cancellation method to obtain the signal after the interference is eliminated;

[0099] A step-by-step detection module is used to select a number of points to be clustered from the signal after interference removal through a step-by-step detection method;

[0100] A spatiotemporal clustering module is used to cluster a number of points to be clustered based on angular deviation and position information to obtain a number of cluster points;

[0101] The interference removal module is used to remove and replace noise from several cluster points to obtain interference-suppressed signals.

[0102] Optionally, the interference cancellation module includes:

[0103] A noise signal acquisition unit, used to obtain a noise signal estimation value based on a radar received signal;

[0104] The interference cancellation unit is used to eliminate the noise signal estimation value from the radar receiving signal to obtain the signal after interference elimination.

[0105] Optionally, the step-by-step detection module includes:

[0106] A Fourier transform unit is used to perform fast Fourier transform on the signal after interference removal to obtain a range Doppler two-dimensional signal;

[0107] A signal quantization unit, used for quantizing the range Doppler two-dimensional signal to obtain a quantized echo signal;

[0108] A step-by-step detection unit, used to sequentially obtain a number of thresholds and corresponding intensity values ​​based on the maximum intensity value in the quantized echo signal and a preset noise quantization value, and record the number of points in the quantized echo signal under each threshold that are greater than or equal to the threshold;

[0109] The to-be-clustered point determination unit is used to determine a number of to-be-clustered points under the current threshold based on the ratio of the number of points under two adjacent thresholds and a preset ratio threshold.

[0110] Optionally, the step-by-step detection unit includes:

[0111] A maximum intensity value determination subunit, used to determine the maximum intensity value in the quantized echo signal;

[0112] A threshold determination subunit is used to sequentially subtract a plurality of multiples of the preset noise quantization value from the maximum intensity value to obtain a threshold corresponding to each multiple;

[0113] The point number acquisition subunit is used to sequentially record the number of points in the quantized echo signal that are greater than or equal to the corresponding threshold at each multiple.

[0114] Optionally, the unit for determining points to be clustered includes:

[0115] The point ratio acquisition subunit is used to compare the points of the previous threshold with the points of the current threshold to obtain the point ratio;

[0116] The subunit for determining points to be clustered is used to determine whether the point ratio is less than or equal to a preset ratio threshold. If so, all points corresponding to the points under the previous threshold are taken as points to be clustered under the current threshold.

[0117] Optional spatiotemporal clustering modules include:

[0118] A first clustering unit, used to perform a first clustering of a number of to-be-clustered points based on the angular deviation, the position information and the angular threshold, to obtain clustering points that meet the clustering conditions and to-be-clustered points that do not meet the clustering conditions;

[0119] The second clustering unit is used to perform a second clustering on the to-be-clustered points that do not meet the clustering conditions to obtain a number of clustering points.

[0120] Optionally, the first clustering unit includes:

[0121] The angular deviation calculation subunit is used to calculate the angular deviation and the missile-target distance of all the points to be clustered and obtain the coordinates of each point to be clustered in the search coordinate system based on the servo frame azimuth angle and the servo frame pitch angle;

[0122] A clustering reference point determination subunit is used to record the to-be-clustered points whose ordinate values ​​are greater than the height threshold as clustering reference points;

[0123] An angle difference calculation subunit, used for subtracting the angle deviation of all the points to be clustered from the angle deviation of the clustering reference point to obtain the angle difference;

[0124] The first clustering subunit is used to classify the to-be-clustered points whose absolute values ​​of the angle differences are less than the angle threshold as the clustering points that meet the clustering condition, and the remaining to-be-clustered points as the to-be-clustered points that do not meet the clustering condition.

[0125] Optionally, the second clustering unit includes:

[0126] The remaining points to be clustered are obtained by the subunit, which is used to obtain a number of adjacent points of each cluster point that meets the clustering condition and remove the cluster points that meet the clustering condition, so as to obtain a number of remaining points to be clustered;

[0127] The second clustering subunit is used to determine that the remaining points to be clustered are clustering points if the intensity value of the remaining points to be clustered is greater than the intensity value corresponding to the current threshold and less than or equal to the intensity value corresponding to the previous threshold, and the absolute value of the angle difference is less than the angle threshold or the ordinate value is greater than the height threshold.

[0128] Optional interference rejection module includes:

[0129] A phase value acquisition unit, used to calculate and obtain the phase values ​​of all clustering points;

[0130] The strength value restoration unit is used to restore the strength value corresponding to the current threshold to the original strength value corresponding to the radar received signal;

[0131] The noise replacement unit is used to replace all cluster points based on the restored strength value of the current threshold and the phase value corresponding to each cluster point to obtain a signal after interference suppression.

[0132] Based on this, the device targets the characteristics of convolutionally modulated smart noise interference, that is, it has both dense false target interference and suppressive interference effects, and the dense false targets have small intervals and wide coverage in the time and frequency domains. The smart noise interference is eliminated through interference cancellation, step-by-step detection, and time-space clustering. It can effectively improve the signal-to-interference ratio, thereby separating the target signal from the interference, thereby solving the problem that the current interference suppression method cannot effectively separate the target signal for smart noise interference.

[0133] Example 3

[0134] Please refer to Figure 6 , this embodiment provides an electronic device, which includes a processor, an internal bus, a network interface, a memory and a non-volatile memory, and of course may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a smart interference suppression method based on clustering elimination at the logical level. Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0135] The network interface, processor and memory can be connected to each other through a bus system. The above bus can be divided into an address bus, a data bus, a control bus, etc.

[0136] The memory is used to store programs. Specifically, the program may include program code, and the program code includes computer operation instructions. The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor.

[0137] The processor is used to execute the program stored in the above memory, and specifically perform:

[0138] Step 102, eliminating interference signals in the radar received signal by using an interference cancellation method to obtain a signal after interference elimination;

[0139] Step 104, selecting a number of points to be clustered from the signal after interference removal by a step-by-step detection method;

[0140] Step 106, clustering the points to be clustered based on the angular deviation and the position information to obtain a number of cluster points;

[0141] Step 108: remove and replace noise on a number of cluster points to obtain interference suppressed signals.

[0142] The processor may be an integrated circuit chip with signal processing capability. In the implementation process, each step of the above method may be completed by an integrated logic circuit of the processor hardware or by instructions in the form of software.

[0143] Based on the same invention, the embodiment of this specification also provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple application programs, the electronic device executes Figure 1-Figure 4 The corresponding embodiment provides a smart interference suppression method based on cluster elimination.

[0144] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-readable storage media containing computer-usable program code.

[0145] In addition, for the above-mentioned specific implementation of the system, since it is basically similar to the method implementation, the description is relatively simple, and the relevant parts can be referred to the partial description of the method implementation. Moreover, it should be noted that in each module of the system of the present application, the components therein are logically divided according to the functions to be implemented, but the present application is not limited thereto, and the components can be re-divided or combined as needed.

[0146] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between it and other embodiments.

[0147] The above is a description of a specific embodiment of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the specific order or sequential order shown in the process depicted in the drawings is not necessarily required to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0148] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A smart interference suppression method based on cluster elimination, characterized in that: include: Eliminate interference signals in radar receiving signals by using interference cancellation methods to obtain interference-removed signals; Selecting a number of points to be clustered from the signal after interference removal by means of a step-by-step detection method; Clustering a number of points to be clustered based on the angular deviation and the position information to obtain a number of cluster points; Several cluster points are eliminated and noise replaced to obtain interference suppressed signals.

2. The method according to claim 1, characterized in that Eliminating the interference signal in the radar received signal by using the interference cancellation method to obtain the signal after the interference is eliminated includes: Based on the radar received signal, obtaining a noise signal estimation value; The noise signal estimation value is eliminated from the radar received signal to obtain the interference-removed signal.

3. The method according to claim 1, characterized in that The step of selecting a number of points to be clustered from the signal after interference removal by means of a step-by-step detection method comprises: Perform fast Fourier transform on the signal after interference removal to obtain a two-dimensional range Doppler signal; Quantifying the range Doppler two-dimensional signal to obtain a quantized echo signal; Based on the maximum intensity value in the quantized echo signal and the preset noise quantization value, a number of thresholds and corresponding intensity values ​​are obtained in sequence, and the number of points in the quantized echo signal under each threshold that are greater than or equal to the threshold is recorded; Based on the ratio of the number of points under two adjacent thresholds and a preset ratio threshold, a number of points to be clustered under the current threshold are determined.

4. The method according to claim 3, characterized in that The method of sequentially obtaining a plurality of thresholds and corresponding intensity values ​​based on the maximum intensity value in the quantized echo signal and the preset noise quantization value, and recording the number of points in the quantized echo signal under each threshold that are greater than or equal to the threshold, includes: Determining the maximum intensity value in the quantized echo signal; Subtract several multiples of the preset noise quantization value from the maximum intensity value in turn to obtain the threshold corresponding to each multiple; The number of points in the quantized echo signal that are greater than or equal to the corresponding threshold at each multiple is recorded in sequence.

5. The method according to claim 4, characterized in that The step of determining a number of points to be clustered under the current threshold based on the ratio of the number of points under two adjacent thresholds and a preset ratio threshold comprises: Compare the points of the previous threshold with the points of the current threshold to obtain the point ratio; Determine whether the point ratio is less than or equal to the preset ratio threshold. If so, take all points corresponding to the points under the previous threshold as the points to be clustered under the current threshold.

6. The method according to claim 5, characterized in that The step of clustering a plurality of points to be clustered based on the angular deviation and the position information to obtain a plurality of cluster points comprises: Performing the first clustering of several to-be-clustered points based on the angular deviation, position information and angle threshold, obtaining clustering points that meet the clustering conditions and to-be-clustered points that do not meet the clustering conditions; Perform a second clustering on the points to be clustered that do not meet the clustering conditions to obtain several clustering points.

7. The method according to claim 6, characterized in that The first clustering of a plurality of to-be-clustered points based on the angle deviation, the position information and the angle threshold to obtain clustered points that meet the clustering conditions and to-be-clustered points that do not meet the clustering conditions includes: Calculate the angular deviation and the missile-target distance of all the points to be clustered, and obtain the coordinates of each point to be clustered in the search coordinate system based on the servo frame azimuth angle and the servo frame pitch angle; The points to be clustered whose ordinate values ​​are greater than the height threshold are recorded as clustering reference points; Subtract the angular deviation of all the points to be clustered from the angular deviation of the clustering reference point to obtain the angular difference; The points to be clustered whose absolute values ​​of the angle differences are less than the angle threshold are the clustering points that meet the clustering conditions, and the remaining points to be clustered are the points to be clustered that do not meet the clustering conditions.

8. The method according to claim 7, characterized in that The second clustering of the to-be-clustered points that do not meet the clustering conditions to obtain a number of clustering points includes: Take several adjacent points of each cluster point that meets the clustering conditions and remove the cluster points that meet the clustering conditions to obtain several remaining points to be clustered; If the intensity value of the remaining points to be clustered is greater than the intensity value corresponding to the current threshold and less than or equal to the intensity value corresponding to the previous threshold, and the absolute value of its angle difference is less than the angle threshold or the ordinate value is greater than the height threshold, then the remaining points to be clustered are determined to be cluster points.

9. The method according to claim 1, characterized in that: The removing and noise replacing of a plurality of cluster points to obtain interference suppressed signals comprises: Calculate and obtain the phase values ​​of all cluster points; The intensity value corresponding to the current threshold is restored to the original intensity value corresponding to the radar receiving signal; All clustering points are replaced based on the restored strength value of the current threshold and the phase value corresponding to each clustering point to obtain a signal after interference suppression.

10. A smart interference suppression device based on cluster elimination, characterized in that: include: The interference cancellation module is used to eliminate the interference signal in the radar receiving signal through the interference cancellation method to obtain the signal after the interference is eliminated; A step-by-step detection module is used to select a number of points to be clustered from the signal after interference removal through a step-by-step detection method; A spatiotemporal clustering module is used to cluster a number of points to be clustered based on angular deviation and position information to obtain a number of cluster points; The interference removal module is used to remove and replace noises from several cluster points to obtain interference-suppressed signals.

Images