A target authentication method and system based on radar scattering cross section fluctuation characteristics

Abstract

This invention relates to the field of radar technology, and specifically to a target authentication method and system based on the fluctuation characteristics of radar cross section. The method includes acquiring point trace information and the signal-to-noise ratio (SNR) information of the previous verified pulse; extracting the instantaneous SNR information of the current pulse corresponding to each point trace in the point trace information; constructing a test statistic based on the SNR information of the previous verified pulse and the instantaneous SNR information of the current pulse to obtain the test statistic; deriving the probability distribution under the assumption of true and false targets based on the test statistic to obtain the likelihood function information; calculating the test threshold based on the likelihood function information to obtain the test threshold information; and performing target authentication based on the test statistic and the test threshold information to obtain the authentication result. This invention effectively solves the problem that in practical applications, deceptive interference signals and real target echoes have highly similar characteristics in the time domain, frequency domain, and spatial domain, making them difficult to distinguish using traditional methods.

Description

Technical Field

[0001] This invention relates to the field of radar technology, and more specifically, to a target authentication method and system based on the fluctuation characteristics of radar cross section. Background Technology

[0002] Radar systems face severe challenges in dealing with highly realistic deceptive jamming generated using advanced technologies such as Digital Radio Frequency Storage (DRFM). Traditional anti-jamming methods mainly rely on the non-ideals of jamming signals during modulation and forwarding (such as subtle errors in the time and frequency domains), energy differences in specific transform domains, spatial inconsistencies among multiple radar systems, or abnormal target kinematics to achieve identification. However, these methods either have reduced effectiveness as jammer performance improves, have specific requirements for system deployment, or are only applicable to specific types of jamming. In complex scenarios with highly realistic jamming signals and limited systems, their universality, robustness, and adaptability are all insufficient. Summary of the Invention

[0003] The purpose of this invention is to provide a target authentication method and system based on the characteristics of radar cross section fluctuations, so as to improve the above-mentioned problems.

[0004] To achieve the above objectives, the embodiments of this application provide the following technical solutions:

[0005] On one hand, embodiments of this application provide a target authentication method based on radar cross-section fluctuation characteristics, the method comprising:

[0006] Acquire the dot information and the signal-to-noise ratio information of the previous verified pulse, wherein the dot information includes dot information of potential targets;

[0007] Extract the instantaneous signal-to-noise ratio information of the current pulse corresponding to each point in the point information;

[0008] Based on the signal-to-noise ratio information of the previous verified pulse and the instantaneous signal-to-noise ratio information of the current pulse, the test statistic is constructed and processed to obtain the test statistic;

[0009] Based on the test statistic, the probability distribution under the true and false target assumptions is derived to obtain the likelihood function information;

[0010] The test threshold is calculated based on the likelihood function information to obtain the test threshold information;

[0011] Target authentication is performed based on the test statistics and the test threshold information to obtain the authentication result.

[0012] Secondly, embodiments of this application provide a target authentication system based on radar cross-section fluctuation characteristics, the system comprising:

[0013] The acquisition module is used to acquire dot information and the signal-to-noise ratio information of the previous verified pulse, wherein the dot information includes dot information of potential targets;

[0014] The first processing module is used to extract the instantaneous signal-to-noise ratio information of the current pulse corresponding to each dot in the dot information;

[0015] The second processing module is used to construct and process the test statistic based on the signal-to-noise ratio information of the previous verified pulse and the instantaneous signal-to-noise ratio information of the current pulse, so as to obtain the test statistic.

[0016] The third processing module is used to derive the probability distribution under the true and false target hypotheses based on the test statistic, and obtain the likelihood function information.

[0017] The fourth processing module is used to calculate the test threshold based on the likelihood function information to obtain the test threshold information;

[0018] The fifth processing module is used to perform target authentication based on the test statistics and the test threshold information to obtain the authentication result.

[0019] Thirdly, embodiments of this application provide a target authentication device based on radar cross-section fluctuation characteristics. The device includes a memory and a processor. The memory stores a computer program; the processor executes the computer program to implement the steps of the target authentication method based on radar cross-section fluctuation characteristics described above.

[0020] Fourthly, embodiments of this application provide a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the target authentication method based on the characteristics of radar cross-section fluctuations described above.

[0021] The beneficial effects of this invention are as follows:

[0022] This invention acquires the signal-to-noise ratio (SNR) of the trace information and the previous verified pulse, and extracts the instantaneous SNR of the current pulse to construct a test statistic for adjacent pulses. Then, based on the prior knowledge that the radar cross-section of the radar target follows the classic Swerling fluctuation model, it derives the probability distribution and likelihood function of this statistic under the assumption of true and false targets. Finally, it calculates the optimal test threshold and completes the authentication decision based on the Neyman-Pearson criterion. This invention starts directly from the underlying physical statistical characteristics of the target and the interference, and achieves robust identification that does not depend on the modulation defects of the interference signal, the time-frequency domain characteristics, or the specific system configuration. This effectively solves the problem that in practical applications, the deceptive interference signal and the echo of the real target are highly similar in time, frequency, and spatial domain characteristics, and are difficult to distinguish by traditional methods.

[0023] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the target authentication method based on the fluctuation characteristics of radar cross section as described in an embodiment of the present invention.

[0026] Figure 2 This is a schematic diagram of the target authentication device structure based on the fluctuation characteristics of radar cross section as described in an embodiment of the present invention.

[0027] Figure 3 This is a schematic diagram of fitting empirical PDFs to PDFs with an exponential distribution.

[0028] Figure 4 This is the ROC curve obtained based on measured data.

[0029] Figure 5 The ROC curve is from a purely theoretical simulation.

[0030] The diagram is labeled as follows: 800, Target authentication device based on radar cross-section fluctuation characteristics; 801, Processor; 802, Memory; 803, Multimedia component; 804, I / O interface; 805, Communication component. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0032] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0033] Example 1:

[0034] This embodiment provides a target authentication method based on the fluctuation characteristics of radar cross section. It can be understood that a scenario can be set up in this embodiment, for example: when the radar is operating in a complex and dense electromagnetic countermeasures environment, when using advanced digital radio frequency storage technology to generate a deceptive jamming signal that is highly similar to the echo of a real cruise missile in terms of time delay, Doppler frequency, angle of arrival, and even the fine structure of the signal, multiple false dots that are almost indistinguishable from the trajectory characteristics of the real target will appear on the radar screen at the same time. Existing technologies that rely on analyzing the modulation defects of the jamming signal, using multi-station spatial information, or verifying kinematic laws all fail. The system cannot identify the unique real threat from many realistic false targets, which may lead to false alarms, waste of firepower resources, or even cause the tracking system to be misled and lose the real target.

[0035] See Figure 1 The figure shows that the method includes steps S1-S6.

[0036] Step S1: Obtain the dot information and the signal-to-noise ratio information of the previous verified pulse, wherein the dot information includes the dot of the potential target;

[0037] In this step, the signal-to-noise ratio (SNR) information of the previous verified pulse is the SNR value recorded and saved at that moment after the same tracked target had been authenticated as a real target in the previous processing cycle. It is understandable that before the authentication decision, the radar system cannot directly determine the true identity of the target; therefore, potential targets are candidate targets whose identities are unknown and require further identification.

[0038] Step S2: Extract the instantaneous signal-to-noise ratio information of the current pulse corresponding to each dot in the dot information;

[0039] Step S2 further includes steps S21-S24, which specifically include:

[0040] Step S21: Model the radar transmission signal based on the point information to obtain the radar transmission signal model;

[0041] The core of this step is to reconstruct or confirm the radar's transmitted signal model within the current pulse period based on the initial detection and output of the radar's trace information. The radar transmitted signal model includes the signal corresponding to the real target and the signal corresponding to the jammer. The specific modeling process for the signal corresponding to the real target is as follows:

[0042]

[0043] In the above formula, This indicates the signal corresponding to the actual target; The radar system parameters include all fixed or known gain and loss factors related to the radar system itself and the propagation path, such as radar transmitting antenna gain, receiving antenna gain, and signal propagation path loss. Represents the radar cross-section of the target; Indicates radar transmission signal; Indicates the radar signal transmission time; Indicates the target delay; This represents additive white Gaussian noise.

[0044] The specific signal modeling process corresponding to the jammer is as follows:

[0045]

[0046] In the above formula, This indicates the signal corresponding to the jammer; The loss factor represents the propagation path of the interfering signal; Indicates the amplitude of the jammer signal; Indicates radar transmission signal; Indicates the radar signal transmission time; This indicates intentionally modulated interference delay; This represents additive white Gaussian noise.

[0047] Step S22: Separate the target component and the interference component according to the radar transmission signal model to obtain first signal information and second signal information. The first signal information includes the signal corresponding to the real target, and the second signal information includes the signal corresponding to the jammer.

[0048] Step S23: Calculate the instantaneous signal-to-noise ratio corresponding to the first signal information to obtain the first instantaneous signal-to-noise ratio information;

[0049] In this step, the calculation process for the first instantaneous signal-to-noise ratio information is as follows:

[0050]

[0051] In the above formula, This indicates the signal-to-noise ratio information at the first instant; The radar system parameters include all fixed or known gain and loss factors related to the radar system itself and the propagation path, such as radar transmitting antenna gain, receiving antenna gain, and signal propagation path loss. Represents the radar cross-section of the target; Indicates the radar's transmitted signal power; The variance represents the power of additive white Gaussian noise in the receiving channel.

[0052] Step S24: Calculate the instantaneous signal-to-noise ratio corresponding to the second signal information to obtain the second instantaneous signal-to-noise ratio information.

[0053] In this step, the calculation process for the second instantaneous signal-to-noise ratio information is as follows:

[0054]

[0055] In the above formula, This indicates the instantaneous signal-to-noise ratio corresponding to the second signal information; The loss factor represents the propagation path of the interfering signal; Indicates the power of the interference signal; The variance represents the power of additive white Gaussian noise in the receiving channel.

[0056] Step S3: Based on the signal-to-noise ratio information of the previous verified pulse and the instantaneous signal-to-noise ratio information of the current pulse, construct the test statistic to obtain the test statistic;

[0057] This invention utilizes the change in SNR between adjacent pulses as a discrimination feature. Assume that at a certain moment (the k-th pulse), the radar has verified that a target within a certain range cell is a real target. When processing the next pulse (the (k+1)-th pulse), it is necessary to determine whether the newly received signal within that range cell is an echo from a real target or a deception signal from a jammer. To this end, the following binary hypothesis test is constructed:

[0058]

[0059] In the above formula, This indicates the assumption that the signal received by the (k+1)th pulse originates from deception interference; This represents the instantaneous signal-to-noise ratio observation value corresponding to the (k+1)th pulse period, i.e., the pulse currently to be authenticated; This represents the theoretical value of the instantaneous signal-to-noise ratio (SNR) corresponding to the deception interference signal in the (k+1)th pulse period, which is the second instantaneous SNR information. This implies the assumption that the signal received by the (k+1)th pulse originates from a real target. This represents the theoretical value of the instantaneous signal-to-noise ratio corresponding to the real target echo in the (k+1)th pulse period, which is the first instantaneous signal-to-noise ratio information.

[0060] The test statistic is defined as the absolute difference in SNR between adjacent pulses:

[0061]

[0062] In the above formula, This represents the test statistic; This represents the absolute difference in SNR between adjacent pulses; This represents the instantaneous signal-to-noise ratio information corresponding to the (k+1)th pulse period; This represents the instantaneous signal-to-noise ratio information corresponding to the k-th pulse period.

[0063] Step S4: Based on the test statistic, derive the probability distribution under the true and false target hypotheses to obtain the likelihood function information;

[0064] Step S4 further includes steps S41-S44, which specifically include:

[0065] Step S41: Obtain first prior knowledge, which includes that the target radar cross section follows an exponential distribution.

[0066] In this step, based on radar fluctuation model theory, the Swerling I / II model is applicable to targets composed of a large number of independent scattering centers with similar intensities (such as aircraft and UAVs). The target radar cross-section follows an exponential distribution:

[0067]

[0068] In the above formula, The probability density function representing the radar cross section of a target; The coefficients representing the exponential distribution are specifically... , Indicates the average radar cross section; This represents the radar cross-section of the target at the k-th pulse moment.

[0069] Step S42: Based on the first prior knowledge, perform probability distribution modeling of the first instantaneous signal-to-noise ratio information to obtain the first probability density function;

[0070] In this step, substituting formula (7) into formula (3) yields the probability density function of the true target SNR, i.e., the first probability density function, which is as follows:

[0071]

[0072] In the above formula, Indicates the signal-to-noise ratio information at the first instant. The probability density function is the first probability density function; The coefficients representing the exponential distribution; The variance represents the power of the additive white Gaussian noise in the receiving channel. The radar system parameters include all fixed or known gain and loss factors related to the radar system itself and the propagation path, such as radar transmitting antenna gain, receiving antenna gain, and signal propagation path loss. This indicates the power of the radar transmitted signal.

[0073] Step S43: Under the assumption of a real target, determine the first likelihood function based on the first probability density function;

[0074] exist Under the assumption of a true target signal, the first test statistic is defined as follows: ,in, This represents the first instantaneous signal-to-noise ratio information corresponding to the k-th pulse moment; This represents the first instantaneous signal-to-noise ratio information corresponding to the (k+1)th pulse moment. Substituting formula (8) into the definition of the first test statistic, we can obtain that the probability density function of the first test statistic is a bilateral exponential distribution symmetric about the zero point, specifically:

[0075]

[0076] In the above formula, Indicates in the assumption Below, the probability density function of the first test statistic is also known as the first likelihood function; This represents the first test statistic; The coefficients representing the exponential distribution; The variance represents the power of the additive white Gaussian noise in the receiving channel. Indicates radar system parameters; This indicates the power of the radar transmitted signal.

[0077] Step S44: Under the assumption of the interference signal, determine the second likelihood function based on the first probability density function.

[0078] exist Under the assumption of deceiving interference signals, the second test statistic is defined as follows: ,in, This represents the first instantaneous signal-to-noise ratio information corresponding to the k-th pulse moment; This represents the second instantaneous signal-to-noise ratio information corresponding to the (k+1)th pulse moment. Substituting formula (8) into the definition of the second test statistic, we can obtain that the probability density function of the second test statistic is a truncated exponential distribution, specifically:

[0079]

[0080] In the above formula, Indicates in the assumption Below, the probability density function of the second test statistic is the second likelihood function; This represents the second test statistic; This represents the power of the interference signal corresponding to the (k+1)th pulse period; The coefficients representing the exponential distribution; The variance represents the power of the additive white Gaussian noise in the receiving channel. Indicates radar system parameters; Indicates the radar's transmitted signal power; This represents the loss factor along the propagation path of the interfering signal. This formula represents a truncated, asymmetric distribution, which differs from the assumption of a true target. The bilateral exponential distribution symmetric about zero points forms an essential and quantifiable statistical difference, which is the core of this invention's ability to distinguish between stationary disturbances and fluctuating targets.

[0081] Step S4 further includes steps S45-S48, which specifically include:

[0082] Step S45: Obtain second prior knowledge, which includes the target radar cross section following a chi-square distribution;

[0083] In this step, based on radar fluctuation model theory, the Swerling III / IV model is applicable to targets with a dominant strong scattering point and several weak scattering points. The target's radar cross-section follows a chi-square distribution:

[0084]

[0085] In the above formula, The probability density function representing the radar cross-section of a target that follows a chi-square distribution; Indicates the chi-square distribution parameters; This represents the radar cross-section of the target.

[0086] Step S46: Based on the second prior knowledge, perform probability distribution modeling of the first instantaneous signal-to-noise ratio information to obtain the second probability density function;

[0087] In this formula, substituting formula (11) into formula (3) yields the probability density function of the true target SNR, namely the second probability density function, which is as follows:

[0088]

[0089] In the above formula, This represents the second probability density function; This indicates additive white Gaussian noise in the receiving channel; Indicates radar system parameters; Indicates the radar's transmitted signal power; This represents the chi-square distribution parameter.

[0090] Step S47: Under the assumption of a true target, determine the third likelihood function based on the second probability density function;

[0091] exist Under the assumption of a true target signal, a third test statistic is defined. ,in, This represents the first instantaneous signal-to-noise ratio information corresponding to the k-th pulse moment; Let S represent the first instantaneous signal-to-noise ratio information corresponding to the (k+1)th pulse moment. Substituting formula (12) into the definition of the third test statistic, the third likelihood function can be determined, specifically:

[0092]

[0093] In the above formula, This represents the third likelihood function; This represents the third test statistic; This indicates additive white Gaussian noise in the receiving channel; Indicates radar system parameters; Indicates the radar's transmitted signal power; Indicates the chi-square distribution parameters; The combination constant is used to simplify the formula, where... .

[0094] Step S48: Under the assumption of the interference signal, determine the fourth likelihood function based on the second probability density function.

[0095] exist Under the assumption of deceptive interference signals, the fourth test statistic is defined as follows: ,in, This represents the first instantaneous signal-to-noise ratio information corresponding to the k-th pulse moment; This represents the second instantaneous signal-to-noise ratio information corresponding to the (k+1)th pulse moment. Substituting formula (4) into the definition of the fourth test statistic and formula (12) into the definition of the fourth test statistic, we can obtain the fourth likelihood function, which is as follows:

[0096]

[0097] In the above formula, Indicates in the assumption Below, the probability density function of the fourth test statistic is also known as the fourth likelihood function; This represents the fourth test statistic; This indicates additive white Gaussian noise in the receiving channel; Indicates radar system parameters; Indicates the radar's transmitted signal power; Indicates the chi-square distribution parameters; This represents the power of the interference signal corresponding to the (k+1)th pulse period; The loss factor represents the propagation path of the interference signal.

[0098] Step S5: Calculate the test threshold based on the likelihood function information to obtain the test threshold information;

[0099] Step S5 further includes steps S51-S54, which specifically include:

[0100] Step S51: Construct an expression for calculating the false alarm probability based on the likelihood function information to obtain expression information, which includes the relationship between the false alarm probability and the test threshold.

[0101] The core of this step is to transform the derived likelihood function information into an equation that can be solved. Specifically, using the Neyman-Pearson criterion, an expression for calculating the false alarm probability is constructed. The false alarm probability is defined as the probability that the detector mistakenly identifies the target as a real target when the target is actually a deceptive interference.

[0102] In Swerling I and II models, the threshold is defined as follows: The false alarm probability can be calculated and expressed as:

[0103]

[0104] In the above formula, This represents the false alarm probability calculated in Swerling I and II models; Denotes the second likelihood function; This represents the detection threshold, or test threshold, defined in Swerling I and II models.

[0105] In Swerling III and IV models, the threshold is defined as follows: The false alarm probability can be calculated as:

[0106]

[0107] In the above formula, This represents the false alarm probability calculated in the Swerling III and IV models; This represents the fourth likelihood function; This represents the detection threshold, or test threshold, defined in the Swerling III and IV models.

[0108] Step S52: Obtain the preset false alarm probability;

[0109] In this step, the preset false alarm probability represents the highest average rate at which the system can tolerate mistaking interference for real targets per unit time, and is set based on historical experience.

[0110] Step S53: Based on the expression information and the preset false alarm probability, establish the detection threshold to obtain the calculation formula for the detection threshold;

[0111] In this step, after obtaining the expression information and the preset false alarm probability, the specific calculation formula for the detection threshold is solved in reverse, for example:

[0112] (1) Under the Swerling I / II model, due to The domain is Therefore, the specific calculation of the false alarm probability at this time needs to be discussed on a case-by-case basis. When formula (10) is substituted into formula (15), the false alarm probability can be calculated as:

[0113]

[0114] In the above formula, Indicates when The calculated false alarm probability; Indicates radar system parameters; Indicates the radar's transmitted signal power; This indicates additive white Gaussian noise in the receiving channel; This represents the power of the interference signal corresponding to the (k+1)th pulse period; The loss factor represents the propagation path of the interfering signal; The coefficient represents the exponential distribution coefficient.

[0115] Equating formula (17) to the preset false alarm probability, the detection threshold can be derived. for:

[0116]

[0117] In the above formula, This represents the preset false alarm probability; Indicates radar system parameters; Indicates the radar's transmitted signal power; This indicates additive white Gaussian noise in the receiving channel; This represents the power of the interference signal corresponding to the (k+1)th pulse period; The loss factor represents the propagation path of the interfering signal; The coefficients representing the exponential distribution; Indicates the detection threshold.

[0118] when When formula (10) is substituted into formula (15), the false alarm probability can be calculated as:

[0119]

[0120] In the above formula, Indicates when The calculated false alarm probability; Indicates radar system parameters; Indicates the radar's transmitted signal power; This indicates additive white Gaussian noise in the receiving channel; This represents the power of the interference signal corresponding to the (k+1)th pulse period; The loss factor represents the propagation path of the interfering signal; The coefficients representing the exponential distribution; Indicates the detection threshold.

[0121] Equating formula (19) to the preset false alarm probability, the detection threshold can be derived. for:

[0122]

[0123] In the above formula, Represents the inverse hyperbolic sine function; This represents the preset false alarm probability; Indicates radar system parameters; Indicates the radar's transmitted signal power; This indicates additive white Gaussian noise in the receiving channel; This represents the power of the interference signal corresponding to the (k+1)th pulse period; The loss factor represents the propagation path of the interfering signal; The coefficients representing the exponential distribution; This represents the detection threshold. Based on formulas (18) and (20), the detection threshold can be obtained. This can be specifically expressed as:

[0124]

[0125] (2) In Swerling III and IV models, it is also necessary to calculate the false alarm probability in two cases. When Substituting formula (14) into formula (16), we can obtain the false alarm probability as:

[0126]

[0127] In the above formula, Indicates when When, the calculated false alarm probability is obtained; z represents the combination constant, the purpose of which is to simplify the formula, specifically as follows: ; Indicates the detection threshold; Indicates radar system parameters; Indicates the radar's transmitted signal power; Indicates the chi-square distribution parameters; This indicates additive white Gaussian noise in the receiving channel; This represents the power of the interference signal corresponding to the (k+1)th pulse period; The loss factor represents the propagation path of the interference signal.

[0128] Equating formula (22) to the preset false alarm probability, the detection threshold can be derived. for:

[0129]

[0130] In the above formula, , , , Both are represented as combination constants, where, , , , ; This represents the lambtw function.

[0131] when When formula (14) is substituted into formula (16), the false alarm probability can be calculated as:

[0132]

[0133] In the above formula, Indicates when The calculated false alarm probability; Indicates the detection threshold; Indicates radar system parameters; Indicates the radar's transmitted signal power; Indicates the chi-square distribution parameters; This indicates additive white Gaussian noise in the receiving channel; This represents the power of the interference signal corresponding to the (k+1)th pulse period; The loss factor represents the propagation path of the interfering signal; Represents the combination constant .

[0134] Equating formula (24) to the preset false alarm probability, we can obtain:

[0135]

[0136] In the above formula, This represents the preset false alarm probability.

[0137] The detection threshold can be obtained by solving formula (25). However, since the above solution process cannot be obtained in polynomial time, this invention proposes an iterative optimization method to progressively obtain the detection threshold. First, define... , , , Four combination constants are used to simplify the formula, where, , , , Based on these four combination constants, formula (25) can be equivalently replaced as:

[0138]

[0139] By solving equation (26) using Newton's iteration method, we can obtain:

[0140]

[0141] In the above formula, This represents the optimal solution obtained by solving the equation using Newton's iterative method. , , Both represent combination constants. , , ; Indicates the detection threshold; This indicates additive white Gaussian noise in the receiving channel; This represents the power of the interference signal corresponding to the (k+1)th pulse period; The loss factor represents the propagation path of the interference signal.

[0142] By substituting formula (27), we can obtain The calculation formula is:

[0143]

[0144] Step S54: Calculate the test threshold according to the calculation formula of the test threshold.

[0145] Step S6: Perform target authentication based on the test statistics and the test threshold information to obtain the authentication result.

[0146] Step S6 further includes steps S61-S63, which specifically include:

[0147] Step S61: Compare the test statistic and the test threshold information to obtain the judgment result;

[0148] Step S62: When the judgment result is that the test statistic is greater than the test threshold information, the authentication result is that the current point is a real target;

[0149] In this step, once the current point is verified as a real target, the tracking filter is updated or intelligence is reported.

[0150] Step S63: When the judgment result is that the test statistic is less than the test threshold information, the authentication result is that the current trace is a deception interference.

[0151] In this step, points identified as deception interference can be removed in subsequent data processing (e.g., not included in the track start or not updated in the tracking gate), or suppressed at the signal level (e.g., by applying adaptive weights to the range-Doppler cell).

[0152] Example 2:

[0153] This embodiment provides a specific simulation verification process, as follows:

[0154] Simulation verification scenario

[0155] Step 1: System Parameter Settings

[0156] System parameter settings

[0157] First, set the radar system parameters, target interference, and interference parameters as shown in the table below:

[0158] Table 1 System Parameter Settings

[0159]

[0160] Step 2: Threshold Calculation

[0161] Thresholds are calculated based on formulas (21), (23), and (28). and .

[0162] Step 3: Signal Processing and Authentication Process

[0163] The point is initially associated with the tracking target, and its SNR value is marked. In the (k+1)th pulse cycle, the same distance gate (within the tracking gate) is detected again to obtain the estimated SNR value of the new point. And calculate the detection statistic. The test statistic is compared with a pre-calculated threshold. or Compare them. If the test statistic is greater than... Or the test statistic is greater than If the current point is identified as a real target, the tracking filter is updated or intelligence is reported; if the test statistic is less than 1, the current point is identified as a real target. Or the test statistic is less than If so, it is judged as deception and interference.

[0164] 2. Verification based on measured UAV RCS data

[0165] This embodiment aims to demonstrate the applicability of the method of the present invention to the fluctuation characteristics of real targets.

[0166] (1) Data preparation and scenario construction

[0167] The dataset "Drone RCS Measurements, 26-40GHz" was obtained from IEEE DataPort. RCS data for two UAV models were selected: DJI Phantom 4 Pro (vertical-vertical polarization, VV) and Walkera Voyager 4 (horizontal-horizontal polarization, HH). This data contains high-precision RCS measurements of the UAVs as a function of azimuth and pitch angles on a microwave anechoic chamber turntable.

[0168] Construct a simulated drone flight trajectory. Assume the radar is located at the origin. The initial state of the drone is Using a cooperative turning (CT) motion model, the turning rate Radar frame period s, total simulation frame.

[0169] Based on the relative geometric relationship between the radar and the UAV in each frame, the line-of-sight azimuth and pitch angles are calculated. Using these angles as an index, the corresponding RCS value for the UAV in that frame is obtained by interpolation from the measured RCS data table. This generates an RCS sequence that fluctuates over time based on real physical measurements.

[0170] (2) Fluctuation characteristics analysis and model matching

[0171] For the generated RCS sequence Perform statistical analysis and plot its empirical probability density function (PDF).

[0172] The empirical PDF is fitted to the exponentially distributed PDF (as shown in the appendix). Figure 3As shown in the figure, the two curves show a high degree of agreement, indicating that the statistical characteristics of the RCS fluctuations of the tested UAV during dynamic flight are highly consistent with the Swerling I model (pulse group fluctuations). This empirically supports the rationality of choosing the Swerling model for theoretical derivation in this invention.

[0173] (3) Semi-physical simulation and results

[0174] The above RCS sequence Convert to SNR sequence This can be incorporated into the authentication process designed in this invention.

[0175] Set different interference power Monte Carlo simulations were performed to statistically verify the performance.

[0176] Plot the ROC curve based on the measured data (see attached diagram). Figure 4 Compare this with the ROC curve from purely theoretical simulation (see attached diagram). Figure 5 A comparison was made. It can be found that the two curves are basically the same in shape. The result based on the measured data is slightly lower than the theoretical optimal curve. This is because the fluctuations of the measured data do not perfectly follow an exponential distribution, but the difference is very small.

[0177] Example 3:

[0178] This embodiment provides a target authentication system based on radar cross-section fluctuation characteristics. The system includes an acquisition module, a first processing module, a second processing module, a third processing module, a fourth processing module, and a fifth processing module, specifically including:

[0179] The acquisition module is used to acquire dot information and the signal-to-noise ratio information of the previous verified pulse, wherein the dot information includes dot information of potential targets;

[0180] The first processing module is used to extract the instantaneous signal-to-noise ratio information of the current pulse corresponding to each dot in the dot information;

[0181] The second processing module is used to construct and process the test statistic based on the signal-to-noise ratio information of the previous verified pulse and the instantaneous signal-to-noise ratio information of the current pulse, so as to obtain the test statistic.

[0182] The third processing module is used to derive the probability distribution under the true and false target hypotheses based on the test statistic, and obtain the likelihood function information.

[0183] The fourth processing module is used to calculate the test threshold based on the likelihood function information to obtain the test threshold information;

[0184] The fifth processing module is used to perform target authentication based on the test statistics and the test threshold information to obtain the authentication result.

[0185] In one specific embodiment of this disclosure, the first processing module further includes a first processing unit, a second processing unit, a third processing unit, and a fourth processing unit, specifically including:

[0186] The first processing unit is used to model the radar transmission signal based on the point information to obtain a radar transmission signal model;

[0187] The second processing unit is used to separate the target component and the interference component according to the radar transmission signal model to obtain first signal information and second signal information. The first signal information includes the signal corresponding to the real target, and the second signal information includes the signal corresponding to the jammer.

[0188] The third processing unit is used to calculate the instantaneous signal-to-noise ratio corresponding to the first signal information to obtain the first instantaneous signal-to-noise ratio information;

[0189] The fourth processing unit is used to calculate the instantaneous signal-to-noise ratio corresponding to the second signal information to obtain the second instantaneous signal-to-noise ratio information.

[0190] In one specific embodiment of this disclosure, the third processing module further includes a first acquisition unit, a fifth processing unit, a sixth processing unit, and a seventh processing unit, specifically comprising:

[0191] The first acquisition unit is used to acquire first prior knowledge, the first prior knowledge including that the target radar cross-section follows an exponential distribution.

[0192] The fifth processing unit is used to perform probability distribution modeling of the first instantaneous signal-to-noise ratio information based on the first prior knowledge to obtain the first probability density function;

[0193] The sixth processing unit is used to determine the first likelihood function based on the first probability density function under the assumption of a real target;

[0194] The seventh processing unit is used to determine the second likelihood function based on the first probability density function under the assumption of the interference signal.

[0195] In one specific embodiment of this disclosure, the third processing module further includes a second acquisition unit, an eighth processing unit, a ninth processing unit, and a tenth processing unit, specifically comprising:

[0196] The second acquisition unit is used to acquire second prior knowledge, the second prior knowledge including that the target radar cross-section follows a chi-square distribution;

[0197] The eighth processing unit is used to perform probability distribution modeling of the first instantaneous signal-to-noise ratio information based on the second prior knowledge to obtain the second probability density function;

[0198] The ninth processing unit is used to determine the third likelihood function based on the second probability density function under the assumption of a real target;

[0199] The tenth processing unit is used to determine the fourth likelihood function based on the second probability density function under the assumption of the interference signal.

[0200] In one specific embodiment of this disclosure, the fourth processing module further includes an eleventh processing unit, a third acquisition unit, a twelfth processing unit, and a thirteenth processing unit, specifically including:

[0201] The eleventh processing unit is used to construct an expression for calculating the false alarm probability based on the likelihood function information, and obtain expression information, which includes the relationship between the false alarm probability and the test threshold.

[0202] The third acquisition unit is used to acquire the preset false alarm probability;

[0203] The twelfth processing unit is used to establish the detection threshold based on the expression information and the preset false alarm probability, and obtain the calculation formula for the detection threshold.

[0204] The thirteenth processing unit is used to calculate the test threshold according to the calculation formula of the test threshold.

[0205] In one specific embodiment of this disclosure, the fifth processing module further includes a fourteenth processing unit, a fifteenth processing unit, and a sixteenth processing unit, specifically comprising:

[0206] The fourteenth processing unit is used to perform threshold comparison based on the test statistic and the test threshold information to obtain a judgment result;

[0207] The fifteenth processing unit is used to determine that the current point is a real target when the judgment result is that the test statistic is greater than the test threshold information;

[0208] The sixteenth processing unit is used to determine that the current trace is a deception interference when the judgment result is that the test statistic is less than the test threshold information.

[0209] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0210] Example 4:

[0211] Corresponding to the above method embodiments, this embodiment also provides a target authentication device based on radar cross section fluctuation characteristics. The target authentication device based on radar cross section fluctuation characteristics described below and the target authentication method based on radar cross section fluctuation characteristics described above can be referred to each other.

[0212] Figure 2 This is a block diagram illustrating a target authentication device 800 based on radar cross-section fluctuation characteristics, according to an exemplary embodiment. Figure 2 As shown, the target authentication device 800 based on radar cross section fluctuation characteristics may include: a processor 801 and a memory 802. The target authentication device 800 based on radar cross section fluctuation characteristics may also include one or more of the following: a multimedia component 803, an I / O interface 804, and a communication component 805.

[0213] The processor 801 controls the overall operation of the target authentication device 800 based on radar cross section fluctuation characteristics to complete all or part of the steps in the target authentication method based on radar cross section fluctuation characteristics. The memory 802 stores various types of data to support the operation of the target authentication device 800 based on radar cross section fluctuation characteristics. This data may include, for example, instructions for any application or method operating on the target authentication device 800 based on radar cross section fluctuation characteristics, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the target authentication device 800 based on radar cross-section fluctuation characteristics and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0214] In an exemplary embodiment, the target authentication device 800 based on radar cross section fluctuation characteristics may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the target authentication method based on radar cross section fluctuation characteristics described above.

[0215] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the target authentication method based on radar cross section fluctuation characteristics described above. For example, the computer-readable storage medium may be the memory 802 including the program instructions described above, which may be executed by the processor 801 of the target authentication device 800 based on radar cross section fluctuation characteristics to complete the target authentication method based on radar cross section fluctuation characteristics described above.

[0216] Example 5:

[0217] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the target authentication method based on radar cross-section fluctuation characteristics described above.

[0218] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the target authentication method based on radar cross-section fluctuation characteristics described in the above method embodiments.

[0219] The readable storage medium can specifically be a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, or any other readable storage medium capable of storing program code.

[0220] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0221] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A target authentication method based on radar cross-section fluctuation characteristics, characterized in that, include: Acquire the dot information and the signal-to-noise ratio information of the previous verified pulse, wherein the dot information includes dot information of potential targets; Extract the instantaneous signal-to-noise ratio information of the current pulse corresponding to each point in the point information; Based on the signal-to-noise ratio information of the previous verified pulse and the instantaneous signal-to-noise ratio information of the current pulse, the test statistic is constructed and processed to obtain the test statistic; Based on the test statistic, the probability distribution under the true and false target assumptions is derived to obtain the likelihood function information; The test threshold is calculated based on the likelihood function information to obtain the test threshold information; Target authentication is performed based on the test statistics and the test threshold information to obtain the authentication result.

2. The target authentication method based on radar cross-section fluctuation characteristics according to claim 1, characterized in that, Extracting the instantaneous signal-to-noise ratio information of the current pulse corresponding to each point in the point information, including: A radar transmission signal model is obtained by modeling based on the point information; Based on the radar transmission signal model, the target component and the interference component are separated to obtain first signal information and second signal information. The first signal information includes the signal corresponding to the real target, and the second signal information includes the signal corresponding to the jammer. Calculate the instantaneous signal-to-noise ratio corresponding to the first signal information to obtain the first instantaneous signal-to-noise ratio information; Calculate the instantaneous signal-to-noise ratio corresponding to the second signal information to obtain the second instantaneous signal-to-noise ratio information.

3. The target authentication method based on radar cross-section fluctuation characteristics according to claim 1, characterized in that, The derivation of the probability distribution under the true and false target hypotheses based on the test statistic includes: Acquire first prior knowledge, which includes that the target radar cross-section follows an exponential distribution; Based on the first prior knowledge, the probability distribution of the first instantaneous signal-to-noise ratio information is modeled to obtain the first probability density function; Under the assumption of a true target, the first likelihood function is determined based on the first probability density function; Under the assumption of interference signal, the second likelihood function is determined based on the first probability density function.

4. The target authentication method based on radar cross-section fluctuation characteristics according to claim 1, characterized in that, The derivation of the probability distribution under the true and false target hypotheses based on the test statistic includes: Acquire second prior knowledge, which includes that the target radar cross section follows a chi-square distribution; Based on the second prior knowledge, the probability distribution of the first instantaneous signal-to-noise ratio information is modeled to obtain the second probability density function; Under the assumption of a true target, the third likelihood function is determined based on the second probability density function; Under the assumption of interference signal, the fourth likelihood function is determined based on the second probability density function.

5. The target authentication method based on radar cross-section fluctuation characteristics according to claim 1, characterized in that, Target authentication is performed based on the test statistic and the test threshold information, including: The judgment result is obtained by comparing the threshold based on the test statistic and the test threshold information; When the judgment result is that the test statistic is greater than the test threshold information, the authentication result is that the current point is a real target; When the judgment result is that the test statistic is less than the test threshold information, the authentication result is that the current trace is a deception interference.

6. A target authentication system based on radar cross-section fluctuation characteristics, characterized in that, include: The acquisition module is used to acquire dot information and the signal-to-noise ratio information of the previous verified pulse, wherein the dot information includes dot information of potential targets; The first processing module is used to extract the instantaneous signal-to-noise ratio information of the current pulse corresponding to each dot in the dot information; The second processing module is used to construct and process the test statistic based on the signal-to-noise ratio information of the previous verified pulse and the instantaneous signal-to-noise ratio information of the current pulse, so as to obtain the test statistic. The third processing module is used to derive the probability distribution under the true and false target hypotheses based on the test statistic, and obtain the likelihood function information. The fourth processing module is used to calculate the test threshold based on the likelihood function information to obtain the test threshold information; The fifth processing module is used to perform target authentication based on the test statistics and the test threshold information to obtain the authentication result.

7. The target authentication system based on radar cross-section fluctuation characteristics according to claim 6, characterized in that, The first processing module includes: The first processing unit is used to model the radar transmission signal based on the point information to obtain a radar transmission signal model; The second processing unit is used to separate the target component and the interference component according to the radar transmission signal model to obtain first signal information and second signal information. The first signal information includes the signal corresponding to the real target, and the second signal information includes the signal corresponding to the jammer. The third processing unit is used to calculate the instantaneous signal-to-noise ratio corresponding to the first signal information to obtain the first instantaneous signal-to-noise ratio information; The fourth processing unit is used to calculate the instantaneous signal-to-noise ratio corresponding to the second signal information to obtain the second instantaneous signal-to-noise ratio information.

8. The target authentication system based on radar cross-section fluctuation characteristics according to claim 6, characterized in that, The third processing module includes: The first acquisition unit is used to acquire first prior knowledge, the first prior knowledge including that the target radar cross-section follows an exponential distribution. The fifth processing unit is used to perform probability distribution modeling of the first instantaneous signal-to-noise ratio information based on the first prior knowledge to obtain the first probability density function; The sixth processing unit is used to determine the first likelihood function based on the first probability density function under the assumption of a real target; The seventh processing unit is used to determine the second likelihood function based on the first probability density function under the assumption of the interference signal.

9. The target authentication system based on radar cross-section fluctuation characteristics according to claim 6, characterized in that, The third processing module includes: The second acquisition unit is used to acquire second prior knowledge, the second prior knowledge including that the target radar cross-section follows a chi-square distribution; The eighth processing unit is used to perform probability distribution modeling of the first instantaneous signal-to-noise ratio information based on the second prior knowledge to obtain the second probability density function; The ninth processing unit is used to determine the third likelihood function based on the second probability density function under the assumption of a real target; The tenth processing unit is used to determine the fourth likelihood function based on the second probability density function under the assumption of the interference signal.

10. The target authentication system based on radar cross-section fluctuation characteristics according to claim 6, characterized in that, The fifth processing module includes: The fourteenth processing unit is used to perform threshold comparison based on the test statistic and the test threshold information to obtain a judgment result; The fifteenth processing unit is used to determine that the current point is a real target when the judgment result is that the test statistic is greater than the test threshold information; The sixteenth processing unit is used to determine that the current trace is a deception interference when the judgment result is that the test statistic is less than the test threshold information.

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