A method for discriminating range-drag jamming in wideband radar tracking

Abstract

The application discloses a kind of methods for discriminating range decoy jamming in wideband radar tracking, by the three-stage mathematical modeling of radar wideband signal, obtain wideband signal model set;Based on wideband signal model set, according to the full data vector or accurate data vector of the wideband signal to be processed is generated;At the beginning of target and jamming overlap stage, the received wideband signal is discriminated based on the full data vector, and the reference signal template is obtained;At the beginning of target and jamming separation stage, the accurate data vector of the filtering result of target tracking is discriminated according to the reference signal template, the overall steps of target and jamming are determined, real-time and accurate range decoy jamming discrimination can be carried out in the triangular situation where target maneuvering and range decoy jamming exist simultaneously, and the discrimination accuracy of jamming is greatly improved, which is beneficial to target tracking of radar.The application can be widely applied in the field of radar tracking technology.

Description

Technical Field

[0001] This invention relates to the field of radar tracking technology, and in particular to a method for identifying range drag interference in broadband radar tracking. Background Technology

[0002] Currently, radar tracking is increasingly susceptible to interference. Among the various types of interference, the most difficult to handle is range gate pull-off (RGPO). This type of interference is generated by enemy jammers modulating friendly radar signals. Once inside the radar system, RGPO appears as a false target in the pulse-compressed signal, differing from the real target only in distance. It gradually pulls the radar range gate, causing the target to lose lock on. Especially in triangular situations, the target may maneuver while emitting RGPO (such as chaff), greatly increasing the probability that the radar will lose target access.

[0003] Existing technologies can be divided into decision-based jamming resistance tracking methods and jamming identification methods based on broadband radar signal characteristics.

[0004] Decision-based anti-jamming tracking methods primarily rely on comparing the target's tracking status with the rate of change of the radar range gate to formulate the correct tracking strategy. T. KIRUBARAJAN et al. proposed the Probabilistic Data Association (PDA) tracking method, which improves the likelihood of subsequent correct target tracking. Furthermore, some anti-jamming tracking strategies based on radar range gates with memory have been proposed. These methods assume that the jamming signal will move out of the gate earlier than the real signal compared to the original range gate velocity, thus distinguishing between the jamming signal and the target and ensuring stable target tracking.

[0005] Interference identification methods based on broadband radar signal characteristics work by extracting and analyzing certain features of both the target and interference signals to distinguish between them. One proposed method is a decomposition and fusion-based interference identification approach, a theoretical, systematic anti-interference method that utilizes the angular echo characteristics of the signal for interference identification. Greco et al. statistically modeled the spectral characteristics of interference and target signals, forming a cone-shaped signal, and proposed two interference identification methods: adaptive coherence estimation and generalized likelihood ratio test. Zhao et al. proposed an interference identification algorithm based on signal fusion, identifying active false targets by performing correlation tests between the complex envelopes of any two targets in different receivers. Furthermore, there is a feature fusion method based on Bayesian decision theory, which uses bispectral transform to extract radar signal features from several aspects to identify interference.

[0006] The decision-based jamming resistance tracking methods mentioned above are too simplistic. While applicable to various radar systems, they can only track steady-state targets under jamming. Their performance degrades or even fails when tracking maneuvering targets, and they are not suitable for triangular situational environments. Jamming identification methods based on broadband radar signal characteristics often utilize low-order feature information, which contains insufficient target and jamming information. When the jamming and target features are similar, the identification effect drops sharply. Summary of the Invention

[0007] In view of this, embodiments of the present invention provide a method for identifying range drag interference in broadband radar tracking, aiming to identify range drag interference in broadband radar tracking in real time, quickly and accurately.

[0008] This invention provides a method for identifying range-induced interference in broadband radar tracking, comprising: performing mathematical modeling on the broadband signal of the radar in three stages to obtain a broadband signal model set; wherein the three stages include an interference-free stage, a target-interference overlap stage, and a target-interference separation stage; the broadband signal model set includes a first broadband signal model, a second broadband signal model, and a third broadband signal model; generating a full data vector or a precise data vector based on the broadband signal to be processed according to the broadband signal based on the broadband signal to be processed; at the beginning of the target-interference overlap stage, performing likelihood ratio discrimination on the received broadband signal based on the full data vector to obtain a reference signal template; at the beginning of the target-interference separation stage, performing likelihood ratio discrimination on the precise data vector of the target tracking filtering result based on the reference signal template to determine the target and the interference.

[0009] Optionally, the three-stage mathematical modeling of the radar's broadband signal to obtain a broadband signal model set includes: in the interference-free stage, mathematical modeling of the broadband signal based on the target signal and noise to obtain a first signal model; in the target and interference overlap stage and the separation stage, modeling of the broadband signal based on the target signal, interference signal and noise to obtain a second signal model and a third signal model.

[0010] Optionally, generating a full data vector or a precise data vector based on the broadband signal to be processed according to the broadband signal set includes: generating a full data vector from the broadband signal to be processed in the interference-free stage based on the first broadband signal model; generating a full data vector from the broadband signal to be processed in the target-interference overlap stage based on the second broadband signal model; and generating a precise data vector from the broadband signal to be processed in the target-interference separation stage based on the third broadband signal model.

[0011] Optionally, the step of generating the full data vector is as follows: dividing the complex vector of the broadband signal to be processed into a real part and an imaginary part; combining the real parts in sequence to form a real part vector; combining the imaginary parts in sequence to form an imaginary part vector; and combining the real part vector and the imaginary part vector to form the full data vector.

[0012] Optionally, the step of generating an accurate data vector is as follows: determining the target signal amplitude and noise amplitude in the broadband signal to be processed during the target-interference separation stage; and determining an accurate data vector based on the target signal amplitude and the noise amplitude.

[0013] Optionally, at the beginning of the target-interference overlap phase, the step of performing likelihood ratio discrimination on the received broadband signal based on the full data vector to obtain a reference signal template includes: performing constant false alarm rate detection on the received broadband signal and extracting complete broadband features from the noise; dividing the extracted complete broadband features into undetermined target feature segments and normalizing the undetermined target feature segments; inputting each undetermined target feature segment into a broadband tracking algorithm for tracking calculation to obtain feature segment filtering results; generating a temporary complex signal template based on the feature segment filtering results; and performing likelihood ratio discrimination on the full data vector and the temporary complex signal template to determine the temporary complex signal template of the correct target feature segment as the reference signal template.

[0014] Optionally, the expression for the full data vector likelihood ratio criterion is:

[0015]

[0016] The expression for the accurate data vector likelihood ratio discrimination is:

[0017]

[0018] Where T(z) is the probability of the test statistic; T(z) / σ J 2 It is a statistic; β is the false alarm rate threshold corresponding to the entire data vector; It is the variance of the target signal; It is the variance of the interference signal; The variance of the target signal amplitude; denoted as Variance of the interference signal amplitude; LR is the likelihood ratio; γ is the full data vector likelihood ratio discrimination threshold; ζ is the false alarm rate threshold of the precise data vector; ξ is the precise data vector likelihood ratio discrimination threshold; Jamming indicates that the discrimination result is interference; Target indicates that the discrimination result is target.

[0019] This invention also provides a system for identifying range-induced interference in broadband radar tracking, comprising: a first module for performing three-stage mathematical modeling on the broadband signal of the radar to obtain a broadband signal model set; wherein the three stages include an interference-free stage, a target-interference overlap stage, and a target-interference separation stage; the broadband signal model set includes a first broadband signal model, a second broadband signal model, and a third broadband signal model; a second module for generating a full data vector or a precise data vector based on the broadband signal to be processed according to the broadband signal model set; a third module for performing likelihood ratio discrimination on the received broadband signal based on the full data vector at the beginning of the target-interference overlap stage to obtain a reference signal template; and a fourth module for performing likelihood ratio discrimination on the precise data vector of the target tracking filtering result based on the reference signal template at the beginning of the target-interference separation stage to determine the target and interference.

[0020] This invention also provides an electronic device, including a processor and a memory; the memory is used to store a program; the processor executes the program to implement the method described above.

[0021] This invention also provides a computer-readable storage medium storing a program that is executed by a processor to implement the method described above.

[0022] The embodiments of the present invention have the following beneficial effects: The embodiments of the present invention obtain a broadband signal model set by performing three-stage mathematical modeling on the broadband signal of the radar; based on the broadband signal model set, a full data vector or an accurate data vector is generated according to the broadband signal to be processed; at the beginning of the target-interference overlap stage, the received broadband signal is subjected to likelihood ratio discrimination based on the full data vector to obtain a reference signal template; at the beginning of the target-interference separation stage, the accurate data vector of the target tracking filtering result is subjected to likelihood ratio discrimination based on the reference signal template to determine the overall steps of the target and interference. This enables real-time and accurate range-tow interference identification in a triangular situation where target maneuvering and range-tow interference coexist, and greatly improves the interference discrimination accuracy, which is beneficial for radar target tracking. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of the method steps provided in the embodiments of the present invention;

[0025] Figure 2 This is a schematic diagram of the overall process of the triangular situation provided in the embodiment of the present invention;

[0026] Figure 3 This is provided by the embodiments of the present invention. Figure 2 Enlarged view of the relationship between HRRP amplitude and distance cell when there is no interference;

[0027] Figure 4 This is provided by the embodiments of the present invention. Figure 2 An enlarged view of the relationship between HRRP amplitude and distance cell when the target and interference overlap;

[0028] Figure 5 This is provided by the embodiments of the present invention. Figure 2 An enlarged view of the relationship between HRRP amplitude and range cell during target and interference separation;

[0029] Figure 6 This is a schematic diagram illustrating the HRRP difference between the interference signal and the target signal provided in an embodiment of the present invention;

[0030] Figure 7 This is a schematic diagram of full data vector generation provided in an embodiment of the present invention;

[0031] Figure 8 This is a schematic diagram illustrating the generation of precise data vectors provided in an embodiment of the present invention;

[0032] Figure 9 This is a diagram illustrating the interference identification steps based on the full data vector at the beginning of the target and interference overlap phase, as provided in an embodiment of the present invention.

[0033] Figure 10 This is a schematic diagram of full data vector identification provided in an embodiment of the present invention;

[0034] Figure 11 This is a diagram illustrating the interference identification steps based on precise data vectors at the beginning of the target-interference separation stage, as provided in an embodiment of the present invention.

[0035] Figure 12 This is a graph showing the acceptance probability of identifying a target covered by interference as a pure target at the beginning of the target-interference overlap phase, as provided by the method of this invention.

[0036] Figure 13 This is an acceptance probability curve of the conventional method provided in this embodiment of the invention for identifying a target covered by interference as a pure target at the beginning of the target and interference overlap phase.

[0037] Figure 14 This is a comparison curve of the acceptance probability of the method provided in this embodiment of the invention and the traditional method in a noisy environment during the interference-free stage.

[0038] Figure 15 This is a graph showing the acceptance probability of identifying a target covered by interference during the overlapping phase of the target and interference phase, as a target, according to the method provided in this embodiment of the invention.

[0039] Figure 16 This is a graph showing the acceptance probability of interference being identified as a target during the target-interference separation stage, provided by the embodiments of the present invention. Figure 17 This is a graph showing the acceptance probability of interference being identified as a target during the target-interference separation stage of the conventional method provided in this embodiment of the invention.

[0040] Figure 18 This is a comparison curve of the acceptance probability curves of the method provided in this embodiment of the invention and the traditional method in a noisy environment during the target and interference separation stage, showing the correct identification of the target. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0042] First, let's introduce the terms used in the embodiments of this invention:

[0043] HRRP (High Resolution Range Profile) is the vector sum of the projections of the complex echoes of target scattering points onto the radar beam obtained from broadband radar signals. It provides information on the distribution of target scattering points along the range direction. Its characteristic is that by emitting a high-frequency signal of a certain wavelength, the time and position of the reflection imaging are used to obtain a high-resolution range profile, which has important structural features of the target.

[0044] To address the problems of poor target tracking performance and range drag interference identification in existing technologies, this invention provides a method for identifying range drag interference in broadband radar tracking. This method can maintain optimal target tracking in a triangular situation where target maneuvering and RGPO coexist. (Refer to...) Figure 1 , Figure 1This is a flowchart of the method steps provided in an embodiment of the present invention. The method of the present invention includes: performing mathematical modeling on the broadband signal of the radar in three stages to obtain a broadband signal model set; wherein, the three stages include an interference-free stage, a target-interference overlap stage, and a target-interference separation stage; the broadband signal model set includes a first broadband signal model, a second broadband signal model, and a third broadband signal model; based on the broadband signal model set, generating a full data vector or an accurate data vector according to the broadband signal to be processed; at the beginning of the target-interference overlap stage, performing likelihood ratio discrimination on the received broadband signal based on the full data vector to obtain a reference signal template; at the beginning of the target-interference separation stage, performing likelihood ratio discrimination on the accurate data vector of the target tracking filtering result according to the reference signal template to determine the target and interference.

[0045] Specifically, the embodiments of the present invention divide the triangular situational scenario of radar tracking into three stages, referring to... Figure 2 , Figure 2 This is a schematic diagram of the overall process of the triangular situation provided in the embodiment of the present invention. Figure 2 210 in the diagram represents the overall process of the triangular situation. The external form of the triangular situation is the process of the target (shown as a small aircraft in the diagram) and the interference (shown as an "×" sign in the diagram) overlapping and then separating. In signal processing, this is represented by the radar receiving multiple frames of echoes and performing range pulse compression to create a frame-range two-dimensional image, such as... Figure 2 As shown in 220, the horizontal axis is the frame number and the vertical axis is the range cell, where the range cell represents the target distance detected by the radar. Figure 2 In the diagram, 221 is the interference signal and 222 is the target signal. Figure 2 As can be seen from 220 in the data, in the first stage there is only the target and no interference; in the second stage the interference overlaps with the target, at which point the target maneuvers and turns, causing the distance between the radar and the target to decrease, and the distance between the radar and the interference to increase; in the third stage, the target and the interference completely separate, indicating that the target maneuvers and turns to escape, while the interference continues to move along the original trajectory of the target to deceive the radar's tracking, causing the radar to lose lock on the target. Figure 2 The number 230 in the text reflects this process from the signal level perspective of HRRP. Figure 3 , Figure 4 and Figure 5 All Figure 2 A magnified view of part 230 in the image.

[0046] Reference Figure 1 An embodiment of the present invention provides a method for identifying wideband radar tracking range drag interference, comprising the following steps S100 to S400:

[0047] S100. Perform mathematical modeling on the wideband radar signal in three stages to obtain a wideband signal model set. The three stages include the interference-free stage, the target-interference overlap stage, and the target-interference separation stage. The wideband signal model set includes a first wideband signal model, a second wideband signal model, and a third wideband signal model.

[0048] Specifically, mathematical models are performed on the received wideband radar signal echoes in the three stages of the triangular situation scenario for subsequent calculations. Furthermore, wideband radar tracking is based on target echo pulse signals; therefore, unless otherwise stated, the wideband signal, target signal, and interference signal mentioned in this embodiment are all echo pulse signals received by the wideband radar. Step S100 includes the following steps S110 to S130:

[0049] S110. In the interference-free stage, the broadband signal is mathematically modeled based on the target signal and noise to obtain the first signal model.

[0050] Specifically, in the interference-free phase, the radar steady state depends on the target, therefore the received broadband signals are all target signals, which contain noise. For the noise in this phase, the first broadband signal model S1 is constructed as follows:

[0051] S1=S m +n

[0052] Where S1 represents the first wideband signal model, S m Let n represent the target signal and n represent noise.

[0053] S120. During the target and interference overlap and separation stages, the broadband signal is modeled based on the target signal, interference signal and noise to obtain the second signal model and the third signal model.

[0054] Specifically, during the target-interference overlap phase, the broadband signal received by the radar is a superposition of the target signal and the interference signal, which also includes noise. Therefore, for the broadband signal in this phase, the second broadband signal model S2 is constructed as follows:

[0055] S2 = S m +J+n

[0056] Where S2 is the second wideband signal model, S m J is the target signal, J is the interference signal, and n is the noise.

[0057] During the target-jamming separation phase, the broadband signal received by the radar includes the separated target signal and jamming signal, as well as noise. Therefore, for the broadband signal in this phase, a third broadband signal model is constructed as follows:

[0058]

[0059] Among them, S 31 and S 32 Together they constitute the third broadband signal model.

[0060] Based on the fact that the target contains D scattering centers, the radar transmits a broadband linear frequency modulated signal, which is scattered by the target and then echoes back to the radar. The target signal S m It can be represented as:

[0061]

[0062] Among them, S m (t,t a ) represents the target signal, and t represents fast time (i.e., distance in unit dimension). a Representing slow time (i.e., frame count dimension), a d The amplitude of the d-th scattering center is given by τ(d, t). T is the pulse duration, f0 is the center carrier frequency, μ is the frequency modulation slope, and τ(d, t) is the amplitude of the d-th scattering center. a ) is the time delay of d scattering centers, and j is the imaginary unit.

[0063] After pulse compression, S m This can be further expressed as:

[0064]

[0065] Where c is the speed of light, j is the imaginary unit, and λ is the wavelength of the radar signal.

[0066] For the three broadband signal models in steps S110 to S120, the interference signal J (i.e., range drag interference RPGO) is typically generated by the jammer DRFM (Digital Radio Frequency Memory). This interference signal J can be expressed as:

[0067] J = a J S J (t)*h(t)

[0068] Among them, a J The amplitude of the interference signal constructed by DRFM is represented by h(t), and the system response is represented by S. J (t) represents the radar signal intercepted by the jammer.

[0069] For the three broadband signal models in steps S110 to S120, the noise in them follows a Gaussian distribution and can be represented by the expression for the Gaussian distribution.

[0070] S200, based on a broadband signal model set, generates a full data vector or an accurate data vector according to the broadband signal to be processed.

[0071] Specifically, step S200 includes the following steps S210 to S250:

[0072] S210. Based on the first broadband signal model, generate a full data vector from the broadband signal to be processed in the interference-free stage.

[0073] Specifically, the target echo HRRP (i.e., target signal S) received by the radar is a broadband signal. m In actual processing, a complex vector is used to quantize the wideband signal S(i) to be processed through a wideband signal model. This complex vector S(i) can be decomposed into the sum of its real and imaginary parts. According to the first wideband signal model, this complex vector can be specifically represented as:

[0074] S(i)=S m (i)+n(i)=S mR (i)+n R (i)+j[S mI (i)+n I [i], i = 1, 2, ..., K

[0075] Where S(i) represents a complex vector; i represents an element in the complex vector; S mR (i) represents a target signal S m The real part of the i-th element of the vector; S mI Represents target signal S m The imaginary part of the i-th element of the vector; similarly, nR(i) represents the real part of the i-th element of the noise vector n, n I This represents the imaginary part of the i-th element of the noise vector n; the noise n follows a Gaussian distribution with zero mean. j represents the imaginary unit.

[0076] The real and imaginary parts of the overall target signal can be represented as:

[0077]

[0078] Where, μ n This represents the mean of the noise. The variance of the noise is represented. This represents a Gaussian distribution.

[0079] Based on this, refer to Figure 7 , Figure 7 This is a schematic diagram of full data vector generation provided in an embodiment of the present invention. The steps for generating the full data vector include steps one to four:

[0080] Step 1: Divide the complex vector of the broadband signal to be processed into real and imaginary parts.

[0081] Step 2: Combine the real parts in order to form a real part vector.

[0082] Step 3: Combine the imaginary parts in sequence to form the imaginary part vector.

[0083] Step 4: Combine the real and imaginary vectors to form the complete data vector.

[0084] S220. Based on the second broadband signal model, generate a full data vector from the broadband signal to be processed during the overlap phase between the target and the interference.

[0085] Specifically, the processing method for the target signal and interference signal during the target-interference overlap phase is similar to step S210, and will not be repeated here. After processing, the real and imaginary parts of the overall target signal can be expressed as:

[0086]

[0087] Where, μ n This represents the mean of the noise. This represents the variance of the noise.

[0088] The interference signal J can be represented as:

[0089]

[0090] Wherein, the real part J of the interference signal J R And the imaginary part J I All follow a mean of μ J The variance is The Gaussian distribution.

[0091] The steps for generating the full data vector are similar to steps one to four of step S210. The broadband signal to be processed is the target signal and the interference signal.

[0092] S230. Based on the third broadband signal model, generate an accurate data vector from the broadband signal to be processed in the target and interference separation stage.

[0093] Specifically, refer to Figure 8 , Figure 8 This is a schematic diagram illustrating the generation of a precise data vector according to an embodiment of the present invention. The steps for generating the precise data vector include the following steps S231 to S232:

[0094] S231. Determine the target signal amplitude and noise amplitude in the broadband signal to be processed during the target-interference separation stage.

[0095] Specifically, since the phase information in HRRP is unpredictable due to the target's maneuvering, this embodiment of the invention uses HRRP amplitude with a gradually changing process for processing. Since the HRRP amplitude after the target maneuvering maintains a high similarity to the amplitude in the previous stage, using HRRP amplitude for processing is beneficial to accurately identify the target and interference during the target-interference separation stage, and then continue to track the target.

[0096] S232. Determine the precise data vector based on the target signal amplitude and noise amplitude.

[0097] Specifically, the expression for the precise data vector is:

[0098] A(i)=A S (i)+A n (i), i = 1, 2, ..., K

[0099] Among them, A S A represents the amplitude of the target signal. n Let A represent the amplitude of the noise signal, and the entire vector length be K. Within a short time, the amplitude fluctuations of the target signal can be considered constant. S It can be considered a constant. And A n It approximately follows a Gaussian distribution, and has Therefore, the overall accurate data vector also follows a Gaussian distribution. Similarly, for the interference signal J, its amplitude A J It approximately follows a Gaussian distribution, and has

[0100] S300. At the beginning of the target and interference overlap phase, the likelihood ratio of the received broadband signal is determined based on the full data vector to obtain the reference signal template.

[0101] Specifically, at the start of the target-interference overlap phase, the broadband signal of this phase is received, the HRRP template of the initial target signal is saved, and the likelihood ratio of the received broadband signal is determined based on the full data vector to obtain the reference signal template. Figure 9 and Figure 10 , Figure 9 This is a diagram illustrating the interference identification steps based on the full data vector at the beginning of the target-interference overlap phase, as provided in an embodiment of the present invention. Figure 10 This is a schematic diagram of full data vector identification provided in an embodiment of the present invention. Step S300 includes the following steps S310 to S330:

[0102] S310. Perform constant false alarm rate detection on the received broadband signal and extract complete broadband features from the noise.

[0103] Specifically, by performing constant false alarm rate (CFAR) detection on a broadband signal, it can be determined whether a target signal exists in the broadband signal. When the target signal is determined to be present, the complete broadband feature, i.e., HRRP, is extracted from the noise.

[0104] When extracting broadband features, since HRRP is translationally sensitive, this embodiment of the invention processes HRRP by padding with zeros to ensure that the distance image vector of HRRP within the distance window meets the requirements.

[0105] S320. Divide the extracted complete broadband features into undetermined target feature segments and normalize the undetermined target feature segments.

[0106] Specifically, the complete broadband features are divided into one or more potential target feature segments and normalized for use in subsequent tracking algorithms.

[0107] S330. Input each target feature segment into the broadband tracking algorithm for tracking calculation to obtain the feature segment filtering result.

[0108] Specifically, in this embodiment of the invention, the input signal level Kalman filtering algorithm is used to track and calculate each undetermined target feature segment. The resulting feature segment filtering result is a state matrix and a covariance matrix, which correspond one-to-one with the input undetermined target feature segment.

[0109] S340. Generate a temporary complex signal template based on the filtering results of the feature segments.

[0110] Specifically, the corresponding posterior complex signal HRRP is extracted from the filtered feature segment results. The HRRP with translation features is then processed by padding with zeros and aligning to obtain a temporary complex signal template. It can be understood that this temporary complex signal template also corresponds one-to-one with each target feature segment to be determined.

[0111] S350. Perform likelihood ratio discrimination based on the full data vector and the temporary complex signal template to determine the temporary complex signal template of the correct target feature segment as the reference signal template.

[0112] Specifically, according to step S220, the full data vector uses each temporary complex signal template as a broadband feature to be processed to generate a full data vector corresponding to each target feature segment to be determined.

[0113] The likelihood ratio test is established using the Neyman-Pearson criterion. Based on the received broadband signal during the target-interference overlap phase, the probability expression is constructed as follows:

[0114]

[0115] Where z represents the test statistic, H1 represents the case where the detected result is the target, H0 represents the case where the detected result is interference (non-target), S represents the target, and J represents interference.

[0116] Based on the full data vector, the probability in case H1 is:

[0117]

[0118] The probability in case H0 is:

[0119]

[0120] In a complex vector, the real and imaginary parts are independent, and each complex vector is also independent. Therefore, the target signal S of length K in complex vector form... mO It can be composed of the real part S mR And the imaginary part S mI The interference signal J, in complex vector form, consists of a 2K vector. O It can also be derived from its actual part J R And the imaginary part J I Composed of various factors, the above probability expression can therefore be represented as:

[0121]

[0122] The likelihood ratio expression based on the full data vector is:

[0123]

[0124] Where LR represents the likelihood ratio and γ represents the likelihood ratio threshold.

[0125] Further simplification of the likelihood ratio expression yields:

[0126]

[0127] The variables P and Q in the likelihood ratio expression are calculated as follows:

[0128]

[0129]

[0130] The calculation of variable P includes the detection quantity z, which is an important part of the likelihood ratio function, while Q is a constant and can be ignored. Therefore, if we isolate the part of P as T(z), then T(z) follows a non-centered chi-square distribution.

[0131] but:

[0132]

[0133]

[0134] Where T(z) is the probability of the probability test statistic, let the statistic T(z) / σ J 2 The probability density function is f T (z), β is the threshold, P fa For a constant false alarm rate, then:

[0135]

[0136] T(z) and coefficients Together, they determine the relationship between the overall likelihood ratio LR and the threshold β, therefore the likelihood ratio discriminant expression is as follows:

[0137]

[0138] Where T(z) is the probability of the test statistic; T(z) / σ J 2 It is a statistic; β is the false alarm rate threshold corresponding to the entire data vector; It is the variance of the target signal; is the variance of the interference signal; LR is the likelihood ratio; γ is the likelihood ratio discrimination threshold of the full data vector; Jamming indicates that the discrimination result is interference; Target indicates that the discrimination result is target.

[0139] By identifying the interference at the beginning of the target-interference overlap phase and saving the correct target signal HRRP template, it is beneficial to use traditional broadband signal midpoint tracking throughout the entire target-interference overlap phase. Traditional broadband signal midpoint tracking is a tracking method that obtains HRRP by pulse compression of the echo by broadband radar, selects the center point of HRRP for ranging, and uses the distance measurement value for tracking filtering. In this embodiment of the invention, when performing traditional broadband signal midpoint tracking, the accurate data vector likelihood ratio of step S400 is used to identify and maintain tracking without losing the target.

[0140] S400. At the beginning of the target and interference separation phase, the likelihood ratio of the precise data vector of the target tracking filtering result is determined based on the reference signal template to identify the target and interference.

[0141] Specifically, refer to Figure 10 , Figure 10This is a diagram of interference identification steps based on precise data vectors at the beginning of the target and interference separation stage provided by an embodiment of the present invention. Step S400 includes the steps of extracting complete broadband features, dividing the target feature segments to be determined, and performing likelihood ratio discrimination based on precise data vectors. The steps of extracting complete broadband features are similar to those of step S310, and the steps of dividing the target feature segments are similar to those of step S320, and will not be described again here.

[0142] The specific steps of the likelihood ratio determination based on the precise data vector in this embodiment of the invention include:

[0143] Based on the precise data vector generated in step S230, a likelihood ratio test is established using the Neyman-Pearson criterion. The probability expression is constructed based on the broadband signal received during the target-interference separation phase:

[0144]

[0145] Where H1 represents the detected target, H0 represents the detected interference (non-target), and A is the target signal amplitude. J This represents the amplitude of the interference signal.

[0146] The derivation process of the likelihood ratio based on the exact data vector is consistent with the derivation process of the likelihood ratio based on the full data vector in S350. Therefore, the discriminant of the likelihood ratio based on the exact data vector is:

[0147]

[0148] Where T(z) is the probability of the test statistic, which includes the functional variable of the test statistic; ζ is the false alarm rate threshold of the exact data vector; The variance of the target signal amplitude; ξ is the variance of the interference signal amplitude; ξ is the likelihood ratio discrimination threshold of the precise data vector.

[0149] By using the likelihood ratio discriminant based on the precise data vector, and performing likelihood ratio discrimination on the precise data vector of the target tracking filtering result according to the reference signal template, the target can be accurately identified when it separates from the interference, which is beneficial for continuous tracking after the target maneuvers and turns.

[0150] This invention enables wideband target tracking using signal-level Kalman filtering during the interference-free phase of a triangular situation, achieving high-precision tracking results. At the start of the target-interference overlap phase, interference is identified using likelihood ratio discrimination based on the full data vector, and the target signal HRRP template from the start of the phase is saved. During this phase, traditional wideband signal midpoint tracking is employed, and continuous interference identification is performed on the target using likelihood ratio discrimination based on precise data vectors to maintain tracking without losing the target. In the target-interference separation phase, the saved target signal HRRP template is used to identify the target again using likelihood ratio discrimination based on precise data vectors, re-locking the target and entering a new round of signal-level Kalman filtering high-precision wideband tracking.

[0151] Among them, the signal-level Kalman filtering method is a broadband tracking method integrating tracking and measurement. It fully utilizes the high resolution and amplitude and phase information of complex HRRP (High-Resolution Ranging and Tracking) to achieve broadband integrated ranging and tracking based on complex signals in a sequential manner. Because it performs data processing and tracking at the signal level, it achieves extremely high tracking accuracy. The steps of this filtering algorithm can be divided into state transition, HRRP complex signal prediction, innovation estimation, and state update. Among them, state transition and HRRP complex signal prediction are the prediction steps in filtering, providing prior predictions of the target state and target signal; while innovation estimation and state update are the update steps in filtering, completing the posterior update of the current signal from the signal dimension, thereby realizing the posterior update of the target state.

[0152] This invention also provides a system for identifying range-induced interference in broadband radar tracking, comprising: a first module for performing three-stage mathematical modeling on the broadband signal of the radar to obtain a broadband signal model set; wherein the three stages include an interference-free stage, a target-interference overlap stage, and a target-interference separation stage; the broadband signal model set includes a first broadband signal model, a second broadband signal model, and a third broadband signal model; a second module for generating a full data vector or a precise data vector based on the broadband signal to be processed according to the broadband signal model set; a third module for performing likelihood ratio discrimination on the received broadband signal based on the full data vector at the beginning of the target-interference overlap stage to obtain a reference signal template; and a fourth module for performing likelihood ratio discrimination on the precise data vector of the target tracking filtering result based on the reference signal template at the beginning of the target-interference separation stage to determine the target and interference.

[0153] This invention also provides an electronic device, including a processor and a memory; the memory is used to store a program; the processor executes the program to implement the method described above.

[0154] This invention also provides a computer-readable storage medium storing a program that is executed by a processor to implement the method described above.

[0155] The embodiments of the present invention have the following beneficial effects:

[0156] 1. It can perform real-time and accurate range drag interference identification of signal frames in a triangular situation where target maneuvering and range drag interference coexist, and greatly improves the interference discrimination accuracy, which is beneficial to radar target tracking.

[0157] 2. The complete HRRP feature information of the radar broadband echo was thoroughly mined, and the complete broadband features were extracted. The resulting full data vector and high-precision data vector provided a more accurate identification effect for the subsequent likelihood ratio test.

[0158] 3. The identification method of this invention can be used for real-time tracking and identification in each frame, which is beneficial for tracking maneuvering targets in a triangular situation.

[0159] The beneficial effects of the embodiments of the present invention are described below with reference to specific application scenarios and experimental data:

[0160] First, a simulation experiment was conducted on an application scenario of the method for identifying range drag interference in broadband radar tracking according to an embodiment of the present invention. The experimental parameters are shown in Tables 1 and 2. Table 1 is a radar parameter table for an application scenario provided by an embodiment of the present invention, and Table 2 is a target motion parameter table for an application scenario provided by an embodiment of the present invention.

[0161] Table 1

[0162]

[0163] Table 2

[0164]

[0165] The first stage represents steady-state tracking of the target until interference occurs. The target's initial velocity is -80 m / s², and under uniform acceleration, the acceleration is -1 m / s² in frames 1-59. 2 The target's acceleration increased from 60 frames to 512 frames and then to 50 m / s². 2 The simulation involves turning, and the interference appears in frame 60. It inherits the target's motion state and overlaps with the target.

[0166] The second stage indicates that the jamming overlaps with the target. Between frames 61 and 150, the radar performs midpoint tracking for HRRP.

[0167] The third phase indicates that the target will completely separate from the jamming within frames 151-500. The radar will then relock onto the target and re-enter steady-state tracking.

[0168] In this application scenario, SNR represents the signal-to-noise ratio; and These represent the variances of interference and noise, respectively. A higher JNR indicates a greater degree of distinction between interference / noise and the target, making it easier to identify interference in the embodiments of this invention. In the embodiments of this invention, let P... VC P is the probability that a target covered by interference will be identified as a pure target. VT P is the probability of identifying a target as a target. VJ Let P be the probability of identifying the interference as the target. In contrast, in traditional methods, let P... RC P is the probability that a target covered by interference will be identified as a pure target. RT P is the probability of identifying a target as a target. RJ This represents the probability of identifying interference as a target.

[0169] At the moment when interference begins in the first and second stages, the identification results based on the full data vector of the embodiments of the present invention and the conventional method are as follows: Figure 14 As shown, from Figure 12 , Figure 13 and Figure 14 It can be seen from this:

[0170] 1. Under the same SNR and JNR, P VC / P RC It is approximately 10%, P VT Higher than P RT The method of this invention is characterized to have a lower probability of receiving a target as a target when the target overlaps with interference than the conventional method, but a higher degree of acceptance of the correct pure target.

[0171] 2. As JNR increases, P VC There has also been an improvement. This is because as JNR increases, and The increased gap between targets and the reduced similarity between targets after interference and superimposed noise improve the identification effect.

[0172] In the second stage, the HRRP identification results based on accurate data vector interference identification for interference and target overlap are as follows: Figure 15 As shown, from Figure 12 , Figure 14 and Figure 15 It can be seen that:

[0173] 1. In the second stage with the same SNR and JNR, P VC / P RCThe figure is approximately 50%, indicating that the identification results based on accurate data vectors are better than those of traditional methods.

[0174] 2. Under the same conditions as in the first stage, compare the P generated by the full data vector discrimination. VC It is lower than the P generated by precise data vector discrimination. VC The full data vector contains more precise information to distinguish between interference and the target.

[0175] In the third stage, the results of separating interference and separating the target based on accurate data vectors are as follows: Figure 16 , Figure 17 and Figure 18 As shown, from Figure 16 , Figure 17 and Figure 18 As can be seen from this, similar conclusions exist to those of the first phase of the experiment: under the same SNR and JNR, P VJ / P RJ It is approximately 26%, P VT Higher than P RT In the third stage, the pure target identification based on accurate data vectors has a higher acceptance probability than traditional methods, while its acceptance probability for interference is lower than that of traditional methods.

[0176] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0177] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0178] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0179] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0180] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0181] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0182] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method of discriminating range-drag jamming in wideband radar tracking, characterized by, include: A three-stage mathematical modeling of the radar's broadband signal is performed to obtain a broadband signal model set; wherein, the three stages include an interference-free stage, a target-interference overlap stage, and a target-interference separation stage; the broadband signal model set includes a first broadband signal model, a second broadband signal model, and a third broadband signal model; Based on the broadband signal model set, a full data vector or an accurate data vector is generated according to the broadband signal to be processed. At the beginning of the target and interference overlap phase, the received broadband signal is subjected to likelihood ratio discrimination based on the full data vector to obtain a reference signal template; At the beginning of the target and interference separation phase, the target and interference are determined by performing a likelihood ratio discrimination on the precise data vector of the target tracking filtering result based on the reference signal template. The step of generating a full data vector or an exact data vector based on the broadband signal model set includes: Based on the first broadband signal model, the broadband signal to be processed in the interference-free stage is generated into a full data vector. Based on the second broadband signal model, a full data vector is generated for the broadband signal to be processed during the overlap phase between the target and the interference. Based on the third broadband signal model, an accurate data vector is generated from the broadband signal to be processed in the target and interference separation stage. The steps for generating the full data vector are as follows: The complex vector of the broadband signal to be processed is divided into a real part and an imaginary part; The real parts are combined sequentially to form a real part vector; The imaginary parts are combined sequentially to form an imaginary part vector; The real part vector and the imaginary part vector are combined to form a complete data vector; The steps for generating accurate data vectors are as follows: Determine the target signal amplitude and noise amplitude in the broadband signal to be processed during the target and interference separation stage; Determine the precise data vector based on the target signal amplitude and the noise amplitude.

2. The method of claim 1, wherein the method is characterized by: The three-stage mathematical modeling of the radar's broadband signal yields a broadband signal model set, including: In the interference-free phase, the broadband signal is mathematically modeled based on the target signal and noise to obtain the first signal model; During the target and interference overlap and separation phases, broadband signals are modeled based on target signals, interference signals, and noise to obtain second and third signal models.

3. The method of claim 1, wherein, At the beginning of the target-interference overlap phase, a reference signal template is obtained by performing a likelihood ratio determination on the received broadband signal based on the full data vector to obtain a reference signal template, including: The received broadband signal is subjected to constant false alarm rate detection, and the complete broadband features are extracted from the noise. The extracted complete broadband features are divided into undetermined target feature segments, and the undetermined target feature segments are normalized. Each of the undetermined target feature segments is input into a broadband tracking algorithm for tracking calculation to obtain the feature segment filtering result; A temporary complex signal template is generated based on the filtering results of the feature segments; The full data vector and the temporary complex signal template are subjected to likelihood ratio discrimination based on the full data vector, and the temporary complex signal template of the correct target feature segment is determined as the reference signal template.

4. The method of claim 1, wherein, The expression for the likelihood ratio criterion of the full data vector is: The expression for the accurate data vector likelihood ratio discrimination is: in, It is the probability of the test statistic; It is a statistic; This is the false alarm rate threshold corresponding to the entire data vector; It is the variance of the target signal; It is the variance of the interference signal; The variance of the target signal amplitude; The variance of the interference signal amplitude; It is the likelihood ratio; It is the likelihood ratio of the full data vector for discrimination; The false alarm rate threshold for precise data vectors; It is the accurate data vector likelihood ratio discrimination threshold; This indicates that the identification result is interference; The identification result is the target.

5. A system for implementing the method of discrimination of range-drag interference in wideband radar tracking as claimed in any one of claims 1-4, characterized in that, include: The first module is used to perform mathematical modeling of the radar's broadband signal in three stages to obtain a broadband signal model set; wherein, the three stages include an interference-free stage, a target-interference overlap stage, and a target-interference separation stage; the broadband signal model set includes a first broadband signal model, a second broadband signal model, and a third broadband signal model. The second module is used to generate a full data vector or an accurate data vector based on the broadband signal model set and the broadband signal to be processed. The third module is used to perform likelihood ratio discrimination on the received broadband signal based on the full data vector at the beginning of the target and interference overlap phase to obtain a reference signal template. The fourth module is used to determine the target and interference by performing a likelihood ratio discrimination on the precise data vector of the target tracking filtering result based on the reference signal template at the beginning of the target and interference separation stage.

6. An electronic device, comprising: Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The storage medium stores a program that is executed by a processor to implement the method as described in any one of claims 1 to 4.

Images