A method and device for collaborative identification of active false targets by broadband radar networking

Abstract

The present invention discloses a method and device for collaboratively identifying active false targets using a broadband radar network. The method comprises: obtaining a one-dimensional range profile of a target at each receiving station based on broadband radar collaborative detection; calculating a correlation coefficient between the one-dimensional range profiles of each receiving station and the other receiving stations based on the obtained one-dimensional range profiles; setting a judgment threshold based on the correlation coefficient and a preset misjudgment probability of an active false target; performing threshold detection based on the judgment threshold and the correlation coefficient to obtain a target identification result. The present invention utilizes the difference in correlation between the one-dimensional range profiles of a real target and an active false target to perform target identification, is independent of the type of deceptive interference, and is applicable to active false targets generated by any modulation.

Description

Technical Field

[0001] The present invention relates to a method and a device for collaboratively identifying active false targets by broadband radar networking, belonging to the technical field of radar detection. Background Art

[0002] Deceptive jamming involves intercepting radar transmissions, analyzing their parameters, and then transmitting a modulated, delayed signal to the radar. This creates numerous active false targets around the actual target, preventing the radar from correctly identifying it. The rapid development of advanced technologies such as large-scale integrated circuits and digital radio frequency storage has enabled jammers to precisely mimic radar transmission waveforms in an instant, achieving rapid and highly realistic active false target deception. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies in the prior art, provide a method and device for collaboratively identifying active false targets using a broadband radar network, and solve the technical problem of active false target identification.

[0004] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0005] In a first aspect, the present invention provides a method for collaboratively identifying active false targets in a broadband radar network, comprising:

[0006] Based on broadband radar cooperative detection, the one-dimensional range image of the target at each receiving station is obtained;

[0007] Based on the acquired one-dimensional range image, the correlation coefficient between each receiving station and the one-dimensional range images of other receiving stations is calculated;

[0008] Set the judgment threshold according to the correlation coefficient and the preset misjudgment probability of active false targets;

[0009] Threshold detection is performed based on the judgment threshold and correlation coefficient to obtain the target identification result.

[0010] Optionally, acquiring a one-dimensional range image of the target at each receiving station includes:

[0011] A broadband radar is used to detect a target detection area, wherein the broadband radar includes a transmitting station and N receiving stations;

[0012] Assume that each receiving station detects K real targets and Q active false targets, then:

[0013] The one-dimensional range image of the kth real target at the nth receiving station is:

[0014] X n|PT,k =ζ n|PT,k +n n,k

[0015] Where, the one-dimensional distance image X n|PT,k A p ×1 vector, A p is the number of distance units occupied by the kth real target, is a noise vector that obeys a complex Gaussian distribution, The dimension is A p ×1 all-zero vector, A p ×A p dimensional unit matrix, σ n is the noise power of the nth receiving station, k = 1, 2, 3…K, n = 1, 2, 3…N;

[0016]

[0017]

[0018] Where C i is the number of scattering points in the ith distance unit occupied by the kth real target, f0 is the carrier frequency of the transmitting signal of the transmitting station, is the ath distance unit in the i-th distance unit occupied by the k-th real target i The scattering intensity of the scattering point at the nth receiving station is: The transmitted signal reaches the ath position in the ith distance unit occupied by the kth real target. i The time required for the scattering point to return to the nth receiving station;

[0019] The one-dimensional range image of the qth active false target at the nth receiving station is:

[0020] X n|FT,q =ζ n|FT,q +n n,q

[0021] Where, the one-dimensional distance image X n|FT,q B h ×1 vector, B h is the number of range cells occupied by the qth active false target, is a noise vector that obeys a complex Gaussian distribution, The dimension is B h ×1 all-zero vector, For B h ×B h dimensional unit matrix; q = 1, 2, 3…Q;

[0022] ζ n|FT,q =β n,q exp{-j2πf0τ n,q}

[0023] Where, β n,q is the strength of the return signal of the qth active false target at the nth receiving station, τ n,q It is the time required for the transmitted signal to reach the qth active false target and then return to the nth receiving station.

[0024] Optionally, the correlation coefficient is:

[0025]

[0026] Where, ρ j,j′ is the correlation coefficient between the jth and j′th targets, j, j′=1,2,3…Q+K; is the one-dimensional range image of the j-th target at the n1-th receiving station, is the one-dimensional range image of the j′th target at the n2th receiving station, n1, n2 = 1, 2, 3…N, H is the conjugate transpose sign, and E is the mathematical expectation.

[0027] Optionally, setting the judgment threshold includes:

[0028] Correlation coefficient ρ j,j′ Perform Fisher-Z transformation:

[0029]

[0030] Where atanh is the inverse hyperbolic curve, Z j,j′ is the transformation result;

[0031] Get the transformation result Z j,j′ The mean atanh(ρ) and standard deviation

[0032]

[0033] Where JNR and JNR′ are the interference-to-noise ratios of the return signal of the same active false target at the n1th and n2th receiving stations, and P is the number of range cells occupied by the active false target.

[0034] Fisher-Z detection is used, and the preset active false target misjudgment probability P is used. l , calculate the judgment threshold η:

[0035]

[0036] Where tanh is the hyperbolic tangent, The mean is The standard deviation is The normal cumulative distribution function corresponds to atanh(P l ) function value.

[0037] Optionally, performing threshold detection according to the judgment threshold and the correlation coefficient includes:

[0038] For the n1th receiving station, determine the correlation coefficient ρ j,j′ Are the values of all less than or equal to the judgment threshold? If so, the j-th target is a real target; if not, the j-th target is an active false target;

[0039] For the n2th receiving station, determine the correlation coefficient ρ j,j′ The values of are all less than or equal to the judgment threshold. If so, the j′th target is a real target. If not, the j′th target is an active false target.

[0040] In a second aspect, the present invention provides a device for collaboratively identifying active false targets in a broadband radar network, the device comprising:

[0041] The data acquisition module is used to obtain the one-dimensional range image of the target at each receiving station based on broadband radar collaborative detection;

[0042] A coefficient calculation module is used to calculate the correlation coefficient between each receiving station and the one-dimensional range images of other receiving stations based on the acquired one-dimensional range image;

[0043] A threshold acquisition module is used to set a judgment threshold based on the correlation coefficient and the preset active false target misjudgment probability;

[0044] The target identification module is used to perform threshold detection based on the judgment threshold and correlation coefficient to obtain the target identification result.

[0045] In a third aspect, the present invention provides an electronic device, including a processor and a storage medium;

[0046] The storage medium is used to store instructions;

[0047] The processor is configured to operate according to the instructions to execute the steps of the above method.

[0048] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention provides a method and device for collaboratively identifying active false targets in a broadband radar network. The method utilizes the difference in correlation between one-dimensional range profiles of a real target and an active false target to perform target identification. The method is independent of the type of deceptive interference and is applicable to active false targets generated by any modulation. The method performs active false target identification in the context of a broadband radar network, overcoming the problems of low resolution of existing narrowband radars and low performance in identifying active false targets using multi-view scattering information of targets. The method does not require any prior knowledge of broadband radars or interference sources, and does not require known radar station layouts, making it flexible and convenient to use. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of a method for collaboratively identifying active false targets in a broadband radar network provided by the first embodiment of the present invention;

[0052] Figure 2 1 is a schematic diagram of the probability of identifying active false targets in simulation scenario 1 provided in embodiment 1 of the present invention;

[0053] Figure 3 1 is a schematic diagram of the probability of identifying a real target in the simulation scenario 1 provided in the first embodiment of the present invention;

[0054] Figure 4 This is a schematic diagram of the probability of identifying a real target in the simulation scenario 2 provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0055] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0056] Example 1:

[0057] like Figure 1 As shown, the present invention provides a method for collaboratively identifying active false targets in a broadband radar network, comprising the following steps:

[0058] 1. Based on broadband radar collaborative detection, obtain the one-dimensional range image of the target at each receiving station;

[0059] Specifically include:

[0060] 1.1. Use broadband radar to detect the target detection area. The broadband radar includes a transmitting station and N receiving stations;

[0061] 1.2. Assume that each receiving station detects K real targets and Q active false targets, then:

[0062] The one-dimensional range image of the kth real target at the nth receiving station is:

[0063] X n|PT,k =ζn|PT,k +n n,k

[0064] Where, the one-dimensional distance image X n|PT,k A p ×1 vector, A p is the number of distance units occupied by the kth real target, is a noise vector that obeys a complex Gaussian distribution, The dimension is A p ×1 all-zero vector, A p ×A p dimensional unit matrix, σ n is the noise power of the nth receiving station, k = 1, 2, 3…K, n = 1, 2, 3…N;

[0065]

[0066]

[0067] Where C i is the number of scattering points in the ith distance unit occupied by the kth real target, f0 is the carrier frequency of the transmitting signal of the transmitting station, is the ath distance unit in the i-th distance unit occupied by the k-th real target i The scattering intensity of the scattering point at the nth receiving station is: The transmitted signal reaches the ath position in the ith distance unit occupied by the kth real target. i The time required for the scattering point to return to the nth receiving station;

[0068] The one-dimensional range image of the qth active false target at the nth receiving station is:

[0069] X n|FT,q =ζ n|FT,q +n n,q

[0070] Where, the one-dimensional distance image X n|FT,q B h ×1 vector, B h is the number of range cells occupied by the qth active false target, is a noise vector that obeys a complex Gaussian distribution, The dimension is B h ×1 all-zero vector, B h ×B h dimensional unit matrix; q = 1, 2, 3…Q;

[0071] ζ n|FT,q =β n,qexp{-j2πf0τ n,q}

[0072] Where, β n,q is the strength of the return signal of the qth active false target at the nth receiving station, τ n,q It is the time required for the transmitted signal to reach the qth active false target and then return to the nth receiving station.

[0073] 2. Based on the acquired one-dimensional range image, calculate the correlation coefficient between each receiving station and the one-dimensional range images of other receiving stations; the correlation coefficient is:

[0074]

[0075] Where, ρ j,j′ is the correlation coefficient between the jth and j′th targets, j, j′=1,2,3…Q+K; is the one-dimensional range image of the j-th target at the n1-th receiving station, is the one-dimensional range image of the j′th target at the n2th receiving station, n1, n2 = 1, 2, 3…N, H is the conjugate transpose sign, and E is the mathematical expectation.

[0076] 3. Set the judgment threshold based on the correlation coefficient and the preset probability of misjudgment of active false targets; specifically,

[0077] 3.1. Correlation coefficient ρ j,j′ Perform Fisher-Z transformation:

[0078]

[0079] Where atanh is the inverse hyperbolic curve, Z j,j′ is the transformation result;

[0080] 3.2. Get the transformation result Z j,j′ The mean atanh(ρ) and standard deviation

[0081]

[0082] Where JNR and JNR′ are the interference-to-noise ratios of the return signal of the same active false target at the n1th and n2th receiving stations, and P is the number of range cells occupied by the active false target.

[0083] 3.3. Using Fisher-Z detection, according to the preset active false target misjudgment probability P l , calculate the judgment threshold η:

[0084]

[0085] Where tanh is the hyperbolic tangent, The mean is The standard deviation is The normal cumulative distribution function corresponds to atanh(P l ) function value.

[0086] 4. Perform threshold detection based on the judgment threshold and correlation coefficient to obtain the target identification result; specifically including:

[0087] For the n1th receiving station, determine the correlation coefficient ρ j,j′ Are the values of all less than or equal to the judgment threshold? If so, the j-th target is a real target; if not, the j-th target is an active false target;

[0088] For the n2th receiving station, determine the correlation coefficient ρ j,j′ The values of are all less than or equal to the judgment threshold. If so, the j′th target is a real target. If not, the j′th target is an active false target.

[0089] To verify the aforementioned method's ability to identify active decoy targets, a simulation experiment was conducted using a broadband radar network system consisting of one transmitting station and two receiving stations. The transmitting station's coordinates were [0, 0, 0] km, and the receiving stations' coordinates were [0, 0, 0] km and [10, 0, 0] km, respectively. The radar's transmitted signal was a linear frequency-modulated pulse signal with a 400 MHz bandwidth and a carrier frequency of f = 4 GHz. The target size was 50 m, and the range cell length corresponding to the transmitted signal bandwidth was 0.375 m. Within the broadband radar network's warning area, two real targets were located at coordinates [30, 40, 30] km and [32, 42, 32] km, respectively. To protect the targets, two self-defense jammers each generated an active decoy target through deceptive jamming.

[0090] Assume that the interference-to-noise ratio (JNR) of the active false target is equal to the signal-to-noise ratio (SNR) of the real target, where the interference-to-noise ratio is the ratio of the average power of the one-dimensional range image of the active false target in each range cell to the average power of the noise in each range cell; the signal-to-noise ratio is the ratio of the average power of the one-dimensional range image of the real target in each range cell to the average power of the noise in each range cell.

[0091] Simulation scenario 1: Set the misjudgment probability of active false targets to P l =0.005, 0.01, 0.05, under the conditions of JNR=SNR=4:0.5:7dB, the probability of identifying active false targets and the probability of identifying real targets are simulated. The simulation results are as follows: Figure 2 and Figure 3As shown in Figure 2, the simulation curve is obtained by performing 1×104 Monte Carlo experiments for each value of JNR.

[0092] from Figure 2 It can be seen that the false target misjudgment probability is P l = 0.005, 0.01, 0.05, the probability of identifying active false targets is approximately P FT =0.995, 0.99, 0.95 are consistent with the theoretical values. The deviation between the simulation results and the theoretical values is due to the correlation coefficient ρ j,j′ The results are caused by the approximate normal distribution after Fisher-Z transformation.

[0093] from Figure 3 It can be seen that at low signal-to-noise ratio, the probability of identifying the real target increases with the increase of SNR, and the probability of misjudging the active false target P l The larger the value, the higher the probability of identifying the true target. This is because P l The larger the value is, the larger the identification threshold η is, the greater the difference between η and the correlation coefficient of the true target is, and the higher the probability of identifying the true target is. When the SNR is equal to 6dB, under the three misjudgment probabilities, the identification probability of the true target can reach approximately 1.

[0094] Simulation scenario 2: The probability of false target identification is determined by the probability of active false target misjudgment P l To analyze the effect of target size on the probability of identifying a true target, the following simulations are performed for three targets with sizes D = 50m, 55m, and 60m:

[0095] Assume the false target misjudgment probability P l =0.005, under the conditions of JNR=SNR=4:0.5:7dB, the identification probability of the real target under three target sizes is simulated. The simulation results are as follows Figure 4 As shown in Figure 2, the simulation curve is obtained by performing 1×104 Monte Carlo experiments for each value of JNR.

[0096] from Figure 4 It can be seen that at low signal-to-noise ratios, the probability of identifying a true target increases with increasing SNR, and the larger the target size D, the higher the probability of identifying a true target. This is because larger targets occupy more range cells in their one-dimensional range profiles, the smaller the correlation between the one-dimensional range profiles of the true target at each receiving station, and the larger the difference between the true target correlation coefficient and the identification threshold η, the higher the probability of identifying a true target. At an SNR of 6 dB, the probability of identifying a true target is approximately 1 for all three target sizes.

[0097] In summary, the present invention utilizes the difference in correlation between the one-dimensional range profiles of a real target and an active false target to perform target identification. This method is independent of the type of deceptive interference and is applicable to active false targets generated by any modulation. The present invention performs active false target identification in the context of broadband radar networking, overcoming the problems of low resolution of existing narrowband radars and low performance in identifying active false targets using multi-view scattering information of the target. The present invention does not require any prior knowledge of broadband radars or interference sources, and does not require known radar station layout.

[0098] Example 2:

[0099] An embodiment of the present invention provides a device for collaboratively identifying active false targets in a broadband radar network, the device comprising:

[0100] The data acquisition module is used to obtain the one-dimensional range image of the target at each receiving station based on broadband radar collaborative detection;

[0101] A coefficient calculation module is used to calculate the correlation coefficient between each receiving station and the one-dimensional range images of other receiving stations based on the acquired one-dimensional range image;

[0102] A threshold acquisition module is used to set a judgment threshold based on the correlation coefficient and the preset active false target misjudgment probability;

[0103] The target identification module is used to perform threshold detection based on the judgment threshold and correlation coefficient to obtain the target identification result.

[0104] Example 3:

[0105] Based on the first embodiment, the present invention provides an electronic device including a processor and a storage medium;

[0106] The storage medium is used to store instructions;

[0107] The processor is configured to operate according to the instructions to execute the steps of the above method.

[0108] Example 4:

[0109] Based on the first embodiment, the embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the above method are implemented.

[0110] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0114] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for collaboratively identifying active false targets using a broadband radar network, characterized in that: include: Based on broadband radar cooperative detection, the one-dimensional range image of the target at each receiving station is obtained; Based on the acquired one-dimensional range image, the correlation coefficient between each receiving station and the one-dimensional range images of the remaining receiving stations is calculated; the correlation coefficient is: ; Where, For the The correlation coefficient between the targets, ; For the The goal is The one-dimensional range image of each receiving station, For the The goal is The one-dimensional range image of each receiving station, , is the conjugate transpose symbol, To take the mathematical expectation; Set the judgment threshold according to the correlation coefficient and the preset misjudgment probability of active false targets; The setting of the judgment threshold comprises: Correlation coefficient Perform Fisher-Z transformation: ; Where, is an inverse hyperbolic curve, is the transformation result; Get the transformation result The mean and standard deviation ; ; Where, The return signal of the same active false target is The interference-to-noise ratio of each receiving station, is the number of range cells occupied by the active decoy target; Using Fisher-Z detection, according to the preset probability of misjudgment of active false targets , calculate the judgment threshold : ; Where, is the hyperbolic tangent, The mean is , the standard deviation is The normal cumulative distribution function corresponds to The function value of ; Threshold detection is performed based on the judgment threshold and correlation coefficient to obtain the target identification result.

2. The method for collaboratively identifying active false targets using a broadband radar network according to claim 1, wherein: The obtaining of a one-dimensional range image of the target at each receiving station comprises: A broadband radar is used to detect the target detection area. The broadband radar includes a transmitting station and Receiving stations; Assume that each receiving station detects Real goals and active false targets, then: No. The real target is The one-dimensional range image of a receiving station is: ; In the formula, the one-dimensional distance image for The vector of For the The number of distance units occupied by the real target, is a noise vector that obeys a complex Gaussian distribution, The dimension is The all-zero vector, for dimensional unit matrix, For the The noise power of each receiving station, , ; ; ; Where, For the The number of real targets occupied The number of scattering points within a distance unit, is the carrier frequency of the transmitting signal of the transmitting station, For the The number of real targets occupied The distance unit The scattering point is The scattering intensity of each receiving station, To transmit the signal to The number of real targets occupied The distance unit Scattering point and then return to the The time required to reach the receiving station; No. Active false target in the The one-dimensional range image of a receiving station is: ; In the formula, the one-dimensional distance image for The vector of For the The number of range cells occupied by an active false target, is a noise vector that obeys a complex Gaussian distribution, The dimension is The all-zero vector, for dimensional unit matrix; ; ; Where, For the The return signal of an active false target is The strength of each receiving station, To transmit the signal to Active false target and then return to the The time required for each receiving station.

3. The method for collaboratively identifying active false targets using a broadband radar network according to claim 1, wherein: The threshold detection according to the judgment threshold and the correlation coefficient includes: For the Receiving stations, determine the correlation coefficient Are the values of are all less than or equal to the judgment threshold? If so, then The first target is the real target, if not, then the The target is an active false target; For the Receiving stations, determine the correlation coefficient Are the values of are all less than or equal to the judgment threshold? If so, then The first target is the real target, if not, then the The target is an active false target.

4. A device for collaboratively identifying active false targets using a broadband radar network, characterized in that: The device comprises: The data acquisition module is used to obtain the one-dimensional range image of the target at each receiving station based on broadband radar collaborative detection; The coefficient calculation module is used to calculate the correlation coefficient between each receiving station and the one-dimensional range images of other receiving stations based on the acquired one-dimensional range image; the correlation coefficient is: ; Where, For the The correlation coefficient between the targets, ; For the The goal is The one-dimensional range image of each receiving station, For the The goal is The one-dimensional range image of each receiving station, , is the conjugate transpose symbol, To take the mathematical expectation; The threshold acquisition module is used to set the judgment threshold according to the correlation coefficient and the preset active false target misjudgment probability; the setting of the judgment threshold includes: Correlation coefficient Perform Fisher-Z transformation: ; Where, is an inverse hyperbolic curve, is the transformation result; Get the transformation result The mean and standard deviation ; ; Where, The return signal of the same active false target is The interference-to-noise ratio of each receiving station, is the number of range cells occupied by the active decoy target; Using Fisher-Z detection, according to the preset probability of misjudgment of active false targets , calculate the judgment threshold : ; Where, is the hyperbolic tangent, The mean is , the standard deviation is The normal cumulative distribution function corresponds to The function value of ; The target identification module is used to perform threshold detection based on the judgment threshold and correlation coefficient to obtain the target identification result.

5. An electronic device, characterized in that: including processors and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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