A networked radar-based anti-deception jamming method

Abstract

The application discloses an anti-deception jamming method based on networked radars, which comprises the following steps: configuring networked radar systems as virtual isomorphic networking radar mode, virtual multi-base radar mode and virtual active / passive radar mode respectively; obtaining the anti-deception jamming success rate of the virtual isomorphic networking radar mode; obtaining the anti-deception jamming success rate of the virtual multi-base radar mode; obtaining the anti-deception jamming success rate of the virtual active / passive radar mode; selecting the mode corresponding to the maximum value among the anti-deception jamming success rates of the virtual isomorphic networking radar mode, the virtual multi-base radar mode and the virtual active / passive radar mode as the final mode. In the process of establishing the multi-station radar, the application can ensure that radars with multiple functions and systems are deployed at the same time, overcome the shortcomings of the existing single multi-station radar system, and meet different anti-jamming performance requirements.

Description

Technical Field

[0001] This invention belongs to the field of radar technology, specifically relating to an anti-deception jamming method based on networked radar. Background Technology

[0002] With the development of hardware systems, jammers, after intercepting radar transmission signals, rapidly modulate and relay them, generating one or more deceptive false targets with similar characteristics to real targets on the enemy radar's display. Range deception jamming is an important jamming style. To counter deceptive jamming, monostation radar systems have developed various countermeasures, such as frequency diversity, digital radio frequency storage quantization feature analysis, and target kinematic feature analysis. However, monostation radar systems can only perceive targets and the environment from a single perspective, acquiring lower-dimensional information and having limited anti-jamming performance. Therefore, multistation radar systems have emerged. Compared to monostation radar systems, multistation radar systems have a more complex configuration. Through coordinated control at different locations, they obtain denser target feature information. Utilizing unique information processing methods, they share and fuse highly redundant information, enhancing the overall anti-jamming performance of the radar system. Meanwhile, with the development of the information age, simply combining single or multiple radars is no longer sufficient to meet future needs; anti-jamming performance needs further improvement. A new type of networked radar system has also emerged, which can provide multi-dimensional, multi-mode, and multi-system target detection services simultaneously through shared service management and joint processing. Networked radar no longer presents itself in a traditional fixed structure; it possesses prominent features such as on-demand reconstruction of virtual radar and a flexible and open system architecture. Therefore, research on anti-jamming methods based on networked radar systems is of great significance.

[0003] Most existing anti-spoofing jamming methods focus on single multi-station radar system configurations. The basic principle is to differentiate between deceptive targets by exploiting the difference in "spatial homology" between real and false targets. For real targets in space, after time alignment and data registration among radars, their position and velocity information fall within the error range determined by measurement errors. However, for deceptive targets, after time alignment and data registration among radars, their position and velocity information do not fall within this error range. Based on these differences, existing data-level fusion anti-spoofing jamming methods differentiate between real and false targets by the degree of correlation between data points.

[0004] However, in real-world environments, under conditions of resource constraints and ensuring the utilization rate of existing equipment, the construction of multi-station radar systems cannot guarantee the simultaneous deployment of radars with various functions and systems. This results in the drawback of a single configuration for multi-station radar systems, which sometimes fails to meet the requirements for anti-jamming performance. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides an anti-deception jamming method based on networked radar.

[0006] The technical problem to be solved by this invention is achieved through the following technical solution:

[0007] This invention provides a method for resisting deception-based jamming based on networked radar, comprising:

[0008] The networked radar system is configured into a virtual isomorphic networked radar mode, a virtual multistatic radar mode, and a virtual active / passive radar mode, respectively. The virtual isomorphic networked radar mode includes N first virtual active radars, the virtual multistatic radar mode includes 1 T / R station radar and 1 R station radar, and the virtual active / passive radar mode includes 1 second virtual active radar and 1 virtual passive radar, wherein N≥2.

[0009] In the virtual isomorphic network radar mode, the error covariance matrix of the measurement value of the first virtual active radar is obtained based on the measurement value of the first virtual active radar, and the anti-spoofing interference success rate of the virtual isomorphic network radar mode is determined based on the error covariance matrix of the measurement value of the first virtual active radar.

[0010] In the virtual multi-static radar mode, the error covariance matrix of the measurement values ​​of the T / R station radar and the error covariance matrix of the measurement values ​​of the R station radar are obtained based on the measurement values ​​of the T / R station radar and the R station radar. The anti-spoofing jamming success rate of the virtual multi-static radar mode is determined based on the error covariance matrix of the measurement values ​​of the T / R station radar and the error covariance matrix of the measurement values ​​of the R station radar.

[0011] In the virtual active / passive radar mode, the azimuth and elevation angles of the target relative to the virtual passive radar are obtained based on the measurement values ​​of the second virtual active radar, and the error covariance matrix of the azimuth and elevation angles is obtained based on the azimuth and elevation angles of the target relative to the virtual passive radar, so as to determine the anti-deception jamming success rate of the virtual active / passive radar mode based on the preset error covariance matrix.

[0012] The mode corresponding to the maximum value among the anti-spoofing jamming success rates of the virtual isomorphic network radar mode, the virtual multi-static radar mode, and the virtual active / passive radar mode is selected as the final mode.

[0013] Optionally, the error covariance matrix of the first virtual active radar measurement values ​​is obtained based on the measurement values ​​of the first virtual active radar, and the anti-spoofing interference success rate of the virtual isomorphic networking radar mode is determined based on the error covariance matrix of the first virtual active radar measurement values, including...

[0014] N first virtual active radars are set up, and the measurement values ​​of the N first virtual active radars are obtained. The N first virtual active radars illuminate the same area. The measurement values ​​of the first virtual active radars include the distance between the first virtual active radar and the target, the azimuth angle between the first virtual active radar and the target, and the radial velocity of the target.

[0015] The measurement values ​​of the N first virtual active radars are sequentially time-aligned and spatially aligned to transform them into a unified coordinate system, wherein the coordinate position of the nth first virtual active radar in the unified coordinate system is Z. n =[x,y] T , Where, r n Let θ be the distance between the nth virtual active radar and the target. n Let [x] be the azimuth angle between the nth virtual active radar and the target. n ,y n [] represents the position coordinates of the nth virtual active radar, where 1 ≤ n ≤ N;

[0016] The error covariance matrix of the measurement values ​​of the nth virtual active radar is obtained from the coordinate position of the nth virtual active radar in the unified coordinate system.

[0017] A first discrimination statistic is constructed based on the first variance obtained from the error covariance matrix of the measurement values ​​of the nth first virtual active radar and the error covariance matrix of the measurement values ​​of the mth first virtual active radar. The true target and the false target are determined based on the first discrimination statistic, so as to determine the anti-deception jamming success rate of the virtual isomorphic networking radar mode.

[0018] Optionally, the error covariance matrix of the nth first virtual active radar is expressed as:

[0019] P n =E[dZ n dZ n T ] = T n Λ n T n T

[0020] Among them, P n Let E[·] be the error covariance matrix of the nth virtual active radar, and E[·] be the expected sign. Λ n =diag(σ r,n 2 ,σ θ,n 2 ), where diag is a diagonal matrix function, σ r,n Let σ be the ranging accuracy of the nth virtual active radar. θ,n Let be the angle measurement accuracy of the first virtual active radar of the nth unit.

[0021] Optionally, a first discrimination statistic is constructed based on the first variance obtained from the error covariance matrix of the measurement values ​​of the nth first virtual active radar and the error covariance matrix of the measurement values ​​of the mth first virtual active radar. The true and false targets are then determined based on the first discrimination statistic to determine the anti-spoofing jamming success rate of the virtual isomorphic network radar mode, including:

[0022] Based on the error covariance matrix of the measurement values ​​of the nth first virtual active radar and the error covariance matrix of the measurement values ​​of the mth first virtual active radar, the first variance of the measurement values ​​of the nth first virtual active radar minus the measurement values ​​of the mth first virtual active radar is obtained. The first variance is expressed as:

[0023] Σ nm =E[d(Z) n -Z m )d(Z n -Z m ) T ] = P n +P m

[0024] Where, Σ nm Z is the first variance. m To unify the coordinate position of the m-th virtual active radar in the coordinate system, P m Let Z be the error covariance matrix of the measurement values ​​of the m-th virtual active radar, d(·) be the differential sign, and Z be the error covariance matrix of the first virtual active radar. n -Z m To unify the difference between the coordinate positions of the nth virtual active radar and the mth virtual active radar in the coordinate system, d(Z) n -Z m ) for Z n -Z m Perform differentiation operations;

[0025] Based on the first variance, the coordinate position of the m-th first virtual active radar in the unified coordinate system, and the coordinate position of the n-th first virtual active radar in the unified coordinate system, a first discrimination statistic is obtained. The first discrimination statistic is expressed as:

[0026] d nm =(Z n -Z m ) T Σ nm -1 (Z n -Z m )

[0027] Where, d nm This is the first identification statistic;

[0028] Determine the relationship between the first discrimination statistic and the first threshold. If the first discrimination statistic is less than or equal to the first threshold, it is a true target; if the first discrimination statistic is greater than the first threshold, it is a false target.

[0029] Based on the Monte Carlo experiment, the anti-deception jamming success rate of the virtual isomorphic network radar mode is determined according to the identified real and false targets.

[0030] Optionally, the error covariance matrix of the measurement values ​​of the T / R station radar and the error covariance matrix of the measurement values ​​of the R station radar are obtained based on the measurement values ​​of the T / R station radar and the R station radar, and the anti-spoofing jamming success rate of the virtual multi-static radar mode is determined based on the error covariance matrix of the measurement values ​​of the T / R station radar and the error covariance matrix of the measurement values ​​of the R station radar, including:

[0031] Set up one T / R station radar and one R station radar, and acquire the measurement values ​​of the T / R station radar and the R station radar. The measurement values ​​of the T / R station radar include the distance between the T / R station radar and the target and the azimuth angle between the T / R station radar and the target. The measurement values ​​of the R station radar include the distance from the target to the T / R station radar and the distance from the target to the R station radar, as well as the azimuth angle from the target to the R station radar.

[0032] The measurement values ​​of the T / R station radar and the R station radar are sequentially time-aligned and spatially aligned to transform them into a unified coordinate system. The coordinate position of the T / R station radar in the unified coordinate system is Z. 11 =[x 11 ,y 11 ] T , The coordinate position of the radar at station R in the unified coordinate system is Z. 12 =[x 12 ,y 12 ] T , Where, r T θ represents the distance between the T / R station radar and the target. T The azimuth angle between the T / R station radar and the target, [x T,y T [r] represents the location coordinates of the radar at the T / R station. R Let θ be the distance between the radar at station R and the target. R Let R be the azimuth angle between the radar and the target, [x] R ,y R [ ] represents the location coordinates of the radar at station R;

[0033] The error covariance matrix of the measurement values ​​of the T / R station radar and the error covariance matrix of the measurement values ​​of the R station radar are obtained from the coordinate positions of the T / R station radar and the R station radar respectively in a unified coordinate system.

[0034] A second discrimination statistic is constructed based on the second variance obtained from the error covariance matrix of the measurement values ​​of the T / R station radar and the error covariance matrix of the measurement values ​​of the R station radar. Based on the second discrimination statistic, true targets and false targets are determined to determine the anti-deception jamming success rate of the virtual multi-static radar mode.

[0035] Optionally, the error covariance matrix of the measurements from the T / R station radar is expressed as:

[0036] P 11 =E[dZ 11 dZ 11 T ] = T 11 Λ T T 11 T

[0037] Among them, P 11 Let E[·] be the error covariance matrix of the radar measurements at the T / R station, and E[·] be the expected sign. Λ T =diag(σ r,T 2 ,σ θ,T 2 ), σ r,T For the ranging accuracy of the T / R station radar, σ θ,T For the angle measurement accuracy of the T / R station radar;

[0038] The error covariance matrix of the radar measurements at station R is expressed as:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] Among them, P 12 Let c be the error covariance matrix of the radar measurements at station R. R1 =cosθ R c T1 =cosθ T c R2 =sinθ R c T2 =sinθ T , σ ρ Let σ be the standard deviation of the sum of the distances from the target to the T / R station radar and the distance from the target to the R station radar. θ Let be the standard deviation of the variance of the radar at station R.

[0045] Optionally, a second discrimination statistic is constructed based on the second variance obtained from the error covariance matrix of the measurements from the T / R station radar and the error covariance matrix of the measurements from the R station radar. Based on the second discrimination statistic, true and false targets are determined to ascertain the anti-spoofing jamming success rate of the virtual multi-static radar mode, including:

[0046] Based on the error covariance matrix of the T / R station radar measurements and the error covariance matrix of the R station radar measurements, the second variance of the T / R station radar measurements minus the R station radar measurements is obtained. The second variance is expressed as:

[0047] Σ=E[d(Z 11 -Z 12 )d(Z 11 -Z 12 ) T ] = P 11 +P 12

[0048] Where Σ is the second variance, d(·) is the differential sign, and Z 11 -Z 12 To unify the difference between the coordinate positions of the T / R station radar and the R station radar in the coordinate system, d(Z 11 -Z 12 ) for Z 11 -Z 12 Perform differentiation operations;

[0049] The second discrimination statistic is obtained based on the second variance, the coordinate positions of the T / R station radar in the unified coordinate system, and the coordinate positions of the R station radar in the unified coordinate system. The second discrimination statistic is expressed as follows:

[0050] d=(Z 11 -Z 12 ) TΣ -1 (Z 11 -Z 12 )

[0051] Where d is the second discrimination statistic;

[0052] Determine the relationship between the second discrimination statistic and the second threshold. If the second discrimination statistic is less than or equal to the second threshold, it is a true target; if the second discrimination statistic is greater than the second threshold, it is a false target.

[0053] Based on the Monte Carlo method, the anti-deception jamming success rate of the virtual multistatic radar mode is determined according to the identified real and false targets.

[0054] Optionally, the azimuth and elevation angles of the target relative to the virtual passive radar are obtained based on the measurements from the second virtual active radar, and the error covariance matrix of the azimuth and elevation angles is obtained based on the azimuth and elevation angles of the target relative to the virtual passive radar. The anti-spoofing jamming success rate of the virtual active / passive radar mode is determined based on the error covariance matrix of the azimuth and elevation angles, including:

[0055] Set up one second virtual active radar and one virtual passive radar, and acquire the measurement values ​​of the second virtual active radar and the virtual passive radar. The measurement values ​​of the second virtual active radar include the distance between the second virtual active radar and the target, the azimuth angle between the second virtual active radar and the target, and the elevation angle between the second virtual active radar and the target. The measurement values ​​of the virtual passive radar include the azimuth angle between the virtual passive radar and the target and the elevation angle between the virtual passive radar and the target.

[0056] The measurements from the second virtual active radar are sequentially time-aligned and spatial-aligned to transform them into a unified coordinate system, thereby obtaining the azimuth and elevation angles of the target relative to the virtual passive radar.

[0057] The error covariance matrix of the two-dimensional angle estimation deviation is obtained by constructing a transformation matrix based on the azimuth and elevation angles of the target relative to the virtual passive radar.

[0058] A third discrimination statistic is constructed based on the two-dimensional angle estimation deviation and the error covariance matrix of the two-dimensional angle estimation deviation, and the true and false targets are determined based on the third discrimination statistic to determine the anti-deception jamming success rate of the virtual active / passive radar mode.

[0059] Optionally, the error covariance matrix of the two-dimensional angle estimation deviation is obtained from the transformation matrix constructed based on the azimuth and elevation angles of the target relative to the virtual passive radar, including:

[0060] A transformation matrix is ​​constructed based on the azimuth and elevation angles of the target relative to the virtual passive radar. The transformation matrix is ​​expressed as follows:

[0061]

[0062] Where T is the transformation matrix, θ 12 The azimuth angle of the target relative to the virtual passive radar. Let r1 be the elevation angle of the target relative to the virtual passive radar, r1 be the distance between the second virtual active radar and the target, and θ1 be the azimuth angle between the second virtual active radar and the target. The elevation angle between the second virtual active radar and the target;

[0063] The error covariance matrices of azimuth and elevation angles are obtained based on the transformation matrix and the error covariance matrix of the measurements from the second virtual active radar. These error covariance matrices are expressed as follows:

[0064] P = TΛT T

[0065] Where P is the error covariance matrix of azimuth and elevation angles, and Λ is the error covariance matrix of the measurement values ​​of the second virtual active radar.

[0066] The error covariance matrix of the two-dimensional angle estimation deviation is obtained based on the error covariance matrices of the azimuth and elevation angles and the error covariance matrix of the measurements from the virtual passive radar. The error covariance matrix of the two-dimensional angle estimation deviation is expressed as follows:

[0067] Q = P + Λ'

[0068] Where Q is the error covariance matrix of the two-dimensional angle estimation deviation, and Λ' is the error covariance matrix of the measurement values ​​of the virtual passive radar.

[0069] Optionally, a third discrimination statistic is constructed based on the two-dimensional angle estimation deviation and the error covariance matrix of the two-dimensional angle estimation deviation, and the true and false targets are determined based on the third discrimination statistic to determine the anti-deception jamming success rate of the virtual active / passive radar mode, including:

[0070] A third discrimination statistic is constructed based on the two-dimensional angle estimation bias and the error covariance matrix of the two-dimensional angle estimation bias. The third discrimination statistic is expressed as follows:

[0071] Δ'=ε 12 T Q -1 ε 12

[0072] Where Δ' is the third discrimination statistic, ε 12 This is the deviation in the two-dimensional angle estimation. θ2 is the azimuth angle between the virtual passive radar and the target. The elevation angle between the virtual passive radar and the target;

[0073] Determine the relationship between the third discrimination statistic and the third threshold. If the third discrimination statistic is less than or equal to the third threshold, it is a true target; if the third discrimination statistic is greater than the third threshold, it is a false target.

[0074] Based on the Monte Carlo method, the anti-deception jamming success rate of the virtual active / passive radar mode is determined according to the identified real and false targets.

[0075] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0076] This invention establishes a virtual isomorphic network radar mode, a virtual multistatic radar mode, and a virtual active / passive radar mode. The anti-spoofing jamming success rate of the virtual isomorphic network radar mode is determined based on the error covariance matrix of the measurement values ​​of the first virtual active radar. The anti-spoofing jamming success rate of the virtual multistatic radar mode is determined based on the error covariance matrices of the measurement values ​​of the T / R station radar and the R station radar. The anti-spoofing jamming success rate of the virtual active / passive radar mode is determined based on the error covariance matrices of the target's azimuth and elevation angles relative to the virtual passive radar. This allows for the selection of the operating mode with the highest anti-spoofing jamming success rate, thus enabling on-demand resource allocation. During the construction of a multistatic radar system, it ensures the simultaneous deployment of radars with various functions and systems, overcoming the shortcomings of existing single multistatic radar systems and achieving different anti-jamming performance requirements.

[0077] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0078] Figure 1 This is a flowchart illustrating an anti-deception jamming method based on networked radar provided in an embodiment of the present invention.

[0079] Figure 2 This is a schematic diagram of the deployment and real / false target information of a homogeneous networked radar system provided in an embodiment of the present invention;

[0080] Figure 3 This is a flowchart illustrating a virtual homogeneous networking radar mode provided in an embodiment of the present invention;

[0081] Figure 4 This is a schematic diagram of the structure of a bistatic radar system deployment and real / false target information provided in an embodiment of the present invention;

[0082] Figure 5 This is a flowchart illustrating a virtual multi-static radar mode provided in an embodiment of the present invention;

[0083] Figure 6 This is a schematic diagram of the structure of an active / passive radar system deployment and real / false target information provided in an embodiment of the present invention;

[0084] Figure 7 This is a flowchart illustrating a virtual active / passive radar mode provided in an embodiment of the present invention;

[0085] Figure 8 This is a graph showing the variation of anti-jamming success rate based on location information with deception distance for a virtual isomorphic networking radar mode provided in an embodiment of the present invention.

[0086] Figure 9 This is a graph showing the change in anti-jamming success rate of a multi-base networked radar based on a virtual multi-base radar mode as a function of deception distance, provided by an embodiment of the present invention.

[0087] Figure 10 This is a graph showing the change in anti-interference success rate based on two-dimensional angle information as a function of deception distance for a virtual active / passive radar mode according to an embodiment of the present invention. Detailed Implementation

[0088] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0089] Example 1

[0090] Please see Figure 1 , Figure 1 This is a flowchart illustrating an anti-spoofing jamming method based on networked radar provided in an embodiment of the present invention. The present invention proposes an anti-spoofing jamming method based on networked radar, which includes:

[0091] Step 1: Configure the networked radar system into three modes: virtual isomorphic networked radar mode, virtual multistatic radar mode, and virtual active / passive radar mode. The virtual isomorphic networked radar mode includes N first virtual active radars, the virtual multistatic radar mode includes one T / R station radar and one R station radar, and the virtual active / passive radar mode includes one second virtual active radar and one virtual passive radar. N ≥ 2.

[0092] Here, in the virtual isomorphic network radar mode, the first virtual active radar actively detects transmitted information and receives echo data; in the virtual multi-static radar mode, the R station is silent, only receiving echo data and not transmitting information; in the virtual active / passive radar mode, the virtual passive radar only receives the target's echo to the virtual active radar, does not transmit signals to actively detect, and cannot obtain target distance information.

[0093] Step 2, please refer to Figure 2 and Figure 3 In the virtual isomorphic networked radar mode, the error covariance matrix of the measurement value of the first virtual active radar is obtained based on the measurement value of the first virtual active radar, and the anti-deception interference success rate of the virtual isomorphic networked radar mode is determined based on the error covariance matrix of the measurement value of the first virtual active radar.

[0094] In one specific embodiment, step 2 may include:

[0095] Step 2.1: Set up N first virtual active radars and acquire the measurement values ​​of the N first virtual active radars. The N first virtual active radars illuminate the same area. The measurement values ​​of the first virtual active radars include the distance between the first virtual active radar and the target, the azimuth angle between the first virtual active radar and the target, and the radial velocity of the target.

[0096] For example, if N is 3, then three first virtual active radars (isomorphic radars) are set up with position coordinates [x1,y1], [x2,y2], and [x3,y3], respectively. Assume that within the same CPI, their measurement values ​​are [r1,θ1,ν1], [r2,θ2,ν2], and [r3,θ3,ν3], where r... n Let θ be the distance between the nth virtual active radar and the target. n Let ν be the azimuth angle between the nth virtual active radar and the target. n Let n be the radial velocity of the target relative to the nth first virtual active radar, where 1 ≤ n ≤ N.

[0097] Step 2.2: Perform time and space alignment on the measurement values ​​of N first virtual active radars in sequence to transform them into a unified coordinate system.

[0098] Specifically, assuming the networked radars illuminate the same area, but due to the different sampling intervals of the various first virtual active radars, the time difference of the target echo and the delay of the false target echo differ, resulting in asynchronous measurement values. Therefore, time alignment and spatial alignment are required to transform them to a unified coordinate system. For the nth first virtual active radar, let its measurement value be [r n ,θ n ,ν n The coordinate position in the unified coordinate system is denoted as Z. n=[x,y] T , [·] T This is the matrix transpose symbol. [x n ,y n [ ] represents the position coordinates of the first virtual active radar of the nth unit.

[0099] Step 2.3: Obtain the error covariance matrix of the measurement values ​​of the nth virtual active radar from the coordinate position of the nth virtual active radar in the unified coordinate system.

[0100] Specifically, the spatial transformation in step 2.2 only uses the range and azimuth information of the measurements. After transformation to rectangular coordinates, the error covariance matrix of the measurements of the nth virtual active radar is expressed as:

[0101] P n =E[dZ n dZ n T ] = T n Λ n T n T

[0102] Among them, P n Let E[·] be the error covariance matrix of the nth virtual active radar, and T be the expected sign. n Let be the transformation matrix of the nth virtual active radar. diagΛ n =diag(σ r,n 2 ,σ θ,n 2 ), where diag is a diagonal matrix function, σ r,n Let σ be the ranging accuracy of the nth virtual active radar. θ,n Let be the angle measurement accuracy of the first virtual active radar of the nth unit.

[0103] Step 2.4: Construct a first discrimination statistic based on the first variance obtained from the error covariance matrix of the measurement values ​​of the nth first virtual active radar and the error covariance matrix of the measurement values ​​of the mth first virtual active radar. Then, determine the true target and the false target based on the first discrimination statistic to determine the anti-deception jamming success rate of the virtual isomorphic networking radar mode.

[0104] In this embodiment, the spatial correlation of a real target determines that its measurements generated by all node radars are relatively "concentrated" when transformed to a unified coordinate system. The degree of concentration is determined by the measurement errors of each radar. For false targets, since the jammer generates false targets for each node radar along the line connecting the radar and the target, without accurate knowledge of the distribution parameters of each node radar in the network, the false targets generated by each node radar are relatively "dispersed" under a unified coordinate system. Based on this characteristic, hypothesis testing can be performed using selected statistics to distinguish between real and false targets.

[0105] In one specific embodiment, step 2.4 may include:

[0106] Step 2.41: Based on the error covariance matrix of the measurement value of the nth first virtual active radar and the error covariance matrix of the measurement value of the mth first virtual active radar, obtain the first variance of the measurement value of the nth first virtual active radar minus the measurement value of the mth first virtual active radar.

[0107] Specifically, for the measurement values ​​of the nth virtual active radar in the model, assume H0 corresponds to the real target and H1 corresponds to the false target. Transforming to a unified Cartesian coordinate system, the errors of the measurement values ​​of each virtual active radar approximately follow a Gaussian distribution with zero mean, i.e., dZ n ~N(0,P n Furthermore, the errors in the measurements of each first virtual active radar are independent. Under this assumption, the difference between the measurements of any two first virtual active radars also approximately follows a Gaussian distribution with zero mean, i.e., Z0. n -Z m ~N(0,Σ nm ), Σ nm The first variance is expressed as:

[0108] Σ nm =E[d(Z) n -Z m )d(Z n -Z m ) T ] = P n +P m

[0109] Among them, Z m To unify the coordinate position of the m-th virtual active radar in the coordinate system, P m Let Z be the error covariance matrix of the measurement values ​​of the m-th virtual active radar, d(·) be the differential sign, and Z be the error covariance matrix of the first virtual active radar. n -Z m To unify the difference between the coordinate positions of the nth virtual active radar and the mth virtual active radar in the coordinate system, d(Z)n -Z m ) for Z n -Z m Perform differentiation operations.

[0110] Step 2.42: Obtain the first discrimination statistic based on the first variance, the coordinate position of the m-th first virtual active radar in the unified coordinate system, and the coordinate position of the n-th first virtual active radar in the unified coordinate system.

[0111] Specifically, the Mahalanobis distance between the measurements of the two first virtual active radars is selected as the hypothesis test metric, that is, the Mahalanobis distance is used as the first discrimination statistic, which is expressed as:

[0112] d nm =(Z n -Z m ) T Σ nm -1 (Z n -Z m )

[0113] Where, d nm The first discrimination statistic is the Mahalanobis distance between the measurement value of the nth first virtual active radar and the measurement value of the mth first virtual active radar.

[0114] Step 2.43: Determine the relationship between the first discrimination statistic and the first threshold. If the first discrimination statistic is less than or equal to the first threshold, it is a true target. If the first discrimination statistic is greater than the first threshold, it is a false target.

[0115] Specifically, under the condition that H0 holds, the Mahalanobis distance follows χ. 2 Distribution. The identification rule is:

[0116]

[0117] Where η1 is the first threshold, which is determined by the significance level α. ε is the dimension of the measurement value. In the virtual isomorphic network radar mode, the value is 2 in this embodiment.

[0118] Step 2.44: Based on the Monte Carlo experiment, determine the anti-deception jamming success rate of the virtual isomorphic network radar mode according to the identified real and false targets.

[0119] Specifically, after determining the true and false targets through the above steps, the Monte Carlo experiment method is used to evaluate the anti-jamming performance of this working mode by using the false target identification success rate, i.e., the anti-deception jamming success rate, as the evaluation index, and obtaining the anti-deception jamming success rate of the virtual isomorphic network radar mode.

[0120] Step 3, please refer to Figure 4 and Figure 5 In the virtual multistatic radar mode, the error covariance matrix of the measurement values ​​of the T / R station radar and the R station radar are obtained based on the measurement values ​​of the T / R station radar and the R station radar. The anti-spoofing jamming success rate of the virtual multistatic radar mode is determined based on the error covariance matrix of the measurement values ​​of the T / R station radar and the R station radar.

[0121] In this embodiment, the homogeneous networked radar system can effectively identify non-cooperative false targets, but it cannot effectively counter cooperative deception jamming. Further research is needed on methods to counter cooperative deception jamming using heterogeneous networked radar. For multi-station networked radar systems, the R-station radar is set to silent mode. Because the R-station radar is silent, it only receives echo data and does not transmit information, so the jammer cannot detect its location and therefore cannot perform cooperative deception on the entire multi-station radar system.

[0122] In one specific embodiment, step 3 may include:

[0123] Step 3.1: Set up one T / R station radar and one R station radar, and acquire the measurement values ​​of the T / R station radar and the R station radar. The measurement values ​​of the T / R station radar include the distance between the T / R station radar and the target and the azimuth angle between the T / R station radar and the target. The measurement values ​​of the R station radar include the distance from the target to the T / R station radar and the distance from the target to the R station radar, as well as the azimuth angle from the target to the R station radar.

[0124] Specifically, one T / R station radar and one R station radar are set up. The location coordinates of the T / R station radar are [x T ,y T The location coordinates of the radar at station R are [x R ,y R Assuming their measured values ​​within the same CPI are [r] T ,θ T ] and [ρ R ,θ R ], where r T θ represents the distance between the T / R station radar and the target. T ρ represents the azimuth angle between the T / R station radar and the target. R Let θ be the sum of the distances from the target to the T / R station radar and the distance from the target to the R station radar. R The azimuth angle between the radar at station R and the target.

[0125] Step 3.2: Perform time alignment and spatial alignment on the measurement values ​​of the T / R station radar and the R station radar in sequence to transform them into a unified coordinate system.

[0126] Specifically, the measurement values ​​of the T / R station radar and the R station radar are sequentially time-aligned and spatially aligned. After spatial alignment, the coordinate position of the T / R station radar in the unified coordinate system is Z. 11 =[x 11 ,y 11 ] T , [x T ,y T [ ] represents the location coordinates of the radar at the T / R station.

[0127] For the radar measurement value [ρ at station R] R ,θ R To obtain the position coordinates Z in a unified rectangular coordinate system, it is necessary to obtain the coordinates Z. 12 =[x 12 ,y 12 ] T And its error covariance matrix P2. From the positional relationship, we can obtain:

[0128]

[0129] From the above formula, the coordinate position of the radar at station R in the unified coordinate system can be obtained as Z. 12 =[x 12 ,y 12 ] T , Where, r R Let [x] be the distance between the radar at station R and the target. R ,y R [ ] represents the location coordinates of the radar at station R.

[0130] Step 3.3: Obtain the error covariance matrix of the measurement values ​​of the T / R station radar and the R station radar respectively from the coordinate positions of the T / R station radar and the R station radar under the unified coordinate system.

[0131] Specifically, the error covariance matrix of the T / R station radar measurements is P. 11 P 11 Represented as:

[0132] P 11 =E[dZ 11 dZ 11 T ] = T 11 Λ T T 11 T

[0133] in, Λ T =diag(σ r,T 2,σ θ,T 2 ), σ r,T For the ranging accuracy of the T / R station radar, σ θ,T This refers to the angle measurement accuracy of the T / R station radar.

[0134] The measurement errors of the R-station radar are independent zero-mean Gaussian white noise, corresponding to the range and ρ. R The standard deviations of the variances of the R-station radars are σ and σ, respectively. ρ σ θ Then the error covariance matrix of the radar measurements at station R is expressed as:

[0135]

[0136]

[0137]

[0138]

[0139]

[0140] Among them, P 12 Let c be the error covariance matrix of the radar measurements at station R. R1 =cosθ R c T1 =cosθ T c R2 =sinθ R c T2 =sinθ T , σ ρ Let σ be the standard deviation of the sum of the distances from the target to the T / R station radar and the distance from the target to the R station radar. θ Let be the standard deviation of the variance of the radar at station R.

[0141] Step 3.4: Construct a second discrimination statistic based on the second variance obtained from the error covariance matrix of the measurement values ​​of the T / R station radar and the error covariance matrix of the measurement values ​​of the R station radar, and determine the true target and false target based on the second discrimination statistic to determine the anti-deception jamming success rate of the virtual multi-static radar mode.

[0142] In this embodiment, the spatial correlation of a real target determines that its measurements generated by all node radars are relatively "concentrated" when transformed to a unified coordinate system. The degree of concentration is determined by the measurement errors of each radar. For false targets, since the jammer generates false targets for each node radar along the line connecting the radar and the target, without accurate knowledge of the distribution parameters of each node radar in the network, the false targets generated by each node radar are relatively "dispersed" under a unified coordinate system. Based on this characteristic, hypothesis testing can be performed using selected statistics to distinguish between real and false targets.

[0143] In one specific embodiment, step 3.4 may include:

[0144] Step 3.41: Based on the error covariance matrix of the T / R station radar measurement values ​​and the error covariance matrix of the R station radar measurement values, obtain the second variance of the T / R station radar measurement values ​​minus the R station radar measurement values.

[0145] Specifically, based on the spatial correlation of real targets, hypothesis testing can be performed using selected statistics to distinguish between real and false targets. The difference in coordinate positions of measurements from T / R station radar and R station radar in a unified rectangular coordinate system approximately follows a Gaussian distribution with zero mean, i.e., Z... 11 -Z 12 ~N(0,Σ).

[0146] Therefore, the second variance is expressed as:

[0147] Σ=E[d(Z 11 -Z 12 )d(Z 11 -Z 12 ) T ] = P 11 +P 12

[0148] Where Σ is the second variance, d(·) is the differential sign, and Z 11 -Z 12 To unify the difference between the coordinate positions of the T / R station radar and the R station radar in the coordinate system, d(Z 11 -Z 12 ) for Z 11 -Z 12 Perform differentiation operations.

[0149] Step 3.42: Based on the second variance, the coordinate positions of the T / R station radars in the unified coordinate system, and the coordinate positions of the R station radars in the unified coordinate system, the second discrimination statistic is obtained. The second discrimination statistic is expressed as:

[0150] d=(Z 11 -Z 12) T Σ -1 (Z 11 -Z 12 )

[0151] Where d is the second discrimination statistic.

[0152] Step 3.43: Determine the relationship between the second discrimination statistic and the second threshold. If the second discrimination statistic is less than or equal to the second threshold, it is a true target. If the second discrimination statistic is greater than the second threshold, it is a false target.

[0153] Specifically, under the assumption that the target corresponds to the real target, the second discrimination statistic d follows a chi-square distribution with 2 degrees of freedom, i.e., d ~ χ². 2 Setting a significance level α, the second threshold is obtained as follows:

[0154] η2=χ2 2 (1-α)

[0155] The decision rule for hypothesis testing is:

[0156]

[0157] Where η2 is the second threshold.

[0158] Step 3.44: Based on the Monte Carlo experiment, determine the anti-deception jamming success rate of the virtual multistatic radar mode according to the identified real and false targets.

[0159] Specifically, after determining the true and false targets through the above steps, the Monte Carlo experiment method is used to evaluate the anti-jamming performance of this working mode by using the false target identification success rate, i.e., the anti-deception jamming success rate, as the evaluation index, and obtaining the anti-deception jamming success rate of the virtual isomorphic network radar mode.

[0160] Step 4, as follows Figure 6 and Figure 7 As shown, in the virtual active / passive radar mode, the azimuth and elevation angles of the target relative to the virtual passive radar are obtained based on the measurement values ​​of the second virtual active radar. The error covariance matrix of the azimuth and elevation angles is obtained based on the azimuth and elevation angles of the target relative to the virtual passive radar. The anti-deception jamming success rate of the virtual active / passive radar mode is determined based on the error covariance matrix of the azimuth and elevation angles.

[0161] In this embodiment, in addition to the cooperative deception jamming method provided in step 3, another effective suppression method is also provided, namely, using an active / passive networked radar system. Since the passive radar operates in passive mode and does not radiate energy outward, the jammer cannot carry out cooperative deception jamming against it.

[0162] In one specific embodiment, step 4 may include:

[0163] Step 4.1: Set up one second virtual active radar and one virtual passive radar, and acquire the measurement values ​​of the second virtual active radar and the virtual passive radar. The measurement values ​​of the second virtual active radar include the distance between the second virtual active radar and the target, the azimuth angle between the second virtual active radar and the target, and the elevation angle between the second virtual active radar and the target. The measurement values ​​of the virtual passive radar include the azimuth angle between the virtual passive radar and the target and the elevation angle between the virtual passive radar and the target.

[0164] Specifically, the position coordinates of the second virtual active radar are set to [x1, y1, z1], and the position coordinates of the virtual passive radar are set to [x2, y2, z2]. Assuming that within the same CPI, the measurement value of the second virtual active radar is... The measurement value of the virtual passive radar is Where r1 is the distance between the second virtual active radar and the target, and θ1 is the azimuth angle between the second virtual active radar and the target. θ1 is the elevation angle between the second virtual active radar and the target, and θ2 is the azimuth angle between the virtual passive radar and the target. This represents the elevation angle between the virtual passive radar and the target.

[0165] Step 4.2: Perform time and space alignment on the measurement values ​​of the second virtual active radar in sequence to transform them into a unified coordinate system and obtain the azimuth and elevation angles of the target relative to the virtual passive radar.

[0166] Specifically, the coordinates [x] in the rectangular coordinate system can be obtained using the measurements of the second virtual active radar in the polar coordinate system. t1 ,y t1 ,z t1 Specifically:

[0167]

[0168] Based on the above formula, the azimuth angle and elevation angle of the target relative to the virtual passive radar can be obtained. They are represented as follows:

[0169]

[0170]

[0171] Where, θ 12 The azimuth angle of the target relative to the virtual passive radar. The elevation angle of the target relative to the virtual passive radar.

[0172] Step 4.3: Obtain the error covariance matrix of the two-dimensional angle estimation deviation by constructing the transformation matrix based on the azimuth and elevation angles of the target relative to the virtual passive radar.

[0173] In one specific embodiment, step 4.3 may include:

[0174] Step 4.31: Construct a transformation matrix based on the target's azimuth and elevation angles relative to the virtual passive radar. The transformation matrix is ​​expressed as:

[0175]

[0176] Where T is the transformation matrix.

[0177] Step 4.32: Obtain the error covariance matrices of azimuth and elevation angles based on the transformation matrix and the error covariance matrix of the measurements from the second virtual active radar.

[0178] Here, the azimuth angle θ 12 and pitch angle The error covariance matrix is ​​expressed as:

[0179] P = TΛT T

[0180] Where P is the azimuth angle θ 12 and pitch angle The error covariance matrix is ​​given by Λ, where Λ is the error covariance matrix of the measurements from the second virtual active radar. To improve the ranging accuracy of the second virtual active radar, To improve the azimuth accuracy of the second virtual active radar, This refers to the elevation angle accuracy of the second virtual active radar.

[0181] Step 4.33: Obtain the error covariance matrix of the two-dimensional angle estimation deviation based on the error covariance matrices of the azimuth and elevation angles and the error covariance matrix of the measurement values ​​of the virtual passive radar.

[0182] Specifically, let the two-dimensional angle estimation deviation be:

[0183]

[0184] Therefore, the error covariance matrix of the two-dimensional angle estimation bias is expressed as:

[0185] Q = P + Λ'

[0186] Where Q is the error covariance matrix of the two-dimensional angle estimation bias, and Λ' is the error covariance matrix of the virtual passive radar measurements. To improve the azimuth accuracy of a virtual passive radar This is for the accuracy of the virtual passive radar's elevation angle measurement.

[0187] Step 4.4: Construct a third discrimination statistic based on the two-dimensional angle estimation deviation and the error covariance matrix of the two-dimensional angle estimation deviation, and determine the true target and false target based on the third discrimination statistic to determine the anti-deception jamming success rate of the virtual active / passive radar mode.

[0188] In one specific embodiment, step 4.4 may include:

[0189] Step 4.41: Construct the third discrimination statistic based on the two-dimensional angle estimation bias and the error covariance matrix of the two-dimensional angle estimation bias.

[0190] Here, the discrimination statistic based on the two-dimensional angle statistic (i.e., the third discrimination statistic) is expressed as:

[0191] Δ'=ε 12 T Q -1 ε 12

[0192] Where Δ' is the third discrimination statistic.

[0193] Step 4.42: Determine the relationship between the third discrimination statistic and the third threshold. If the third discrimination statistic is less than or equal to the third threshold, it is a true target. If the third discrimination statistic is greater than the third threshold, it is a false target.

[0194] Specifically, since the ranging and angle measurement errors of radar follow independent zero-mean Gaussian distributions, the two-dimensional angle estimation bias ε 12 It also approximately follows a Gaussian distribution. Under the assumption that this objective is a true objective, the third test statistic Δ' follows a chi-square distribution with 2 degrees of freedom, i.e., Δ' ~ χ². 2 With the same number of measurements, the degrees of freedom are twice that based on azimuth statistics.

[0195] By setting a significance level α, the third threshold is obtained as follows:

[0196] η'=χ2 2 (1-α)

[0197] The decision rule for hypothesis testing is:

[0198]

[0199] Where η' is the third threshold.

[0200] Step 4.43: Based on the Monte Carlo experiment, determine the anti-deception jamming success rate of the virtual active / passive radar modes according to the identified real and false targets.

[0201] Specifically, after determining the true and false targets through the above steps, the Monte Carlo experiment method is used to evaluate the anti-jamming performance of this working mode by using the false target identification success rate, i.e., the anti-deception jamming success rate, as the evaluation index, and obtaining the anti-deception jamming success rate of the virtual active / passive radar mode.

[0202] It should be noted that this embodiment does not limit the specific execution steps of steps 2 to 4. Steps 2, 3 and 4 of this embodiment should not be regarded as the specific execution order of the three working modes. Those skilled in the art can adjust their execution order according to actual needs. They can be executed sequentially or simultaneously.

[0203] Step 5: Select the mode corresponding to the maximum value among the anti-spoofing jamming success rates of the virtual isomorphic network radar mode, the virtual multi-static radar mode, and the virtual active / passive radar mode as the final mode.

[0204] In this embodiment, the radar transmission signal is:

[0205]

[0206] The target echo signal received by the radar is:

[0207]

[0208] The jammer adds a relay delay to the target echo, and the expression for the false target echo signal is:

[0209]

[0210] Where s(t) is the transmitted signal, r(t) is the target echo signal, and r j (t) represents the echo signal from the false target, where t is the time variable, T is the pulse width, and A is the pulse width. k The amplitude of the echo signal is given by φ, the speed of light is given by c, φ0 is given by the initial phase, and Δt is given by Δt. r Δt is the time delay between the target and the radar. j To relay the time-delayed wave, f c Where is the carrier frequency, and K is the frequency modulation coefficient.

[0211] In this embodiment, the networked radar system is first configured with operating modes such as virtual isomorphic networked radar mode, virtual multi-base radar mode, and virtual active / passive radar mode. Then, based on the different operating modes obtained from the configuration, the Cramer-Rao lower bound (CRLB) for the measurement estimation error corresponding to each mode is established to obtain the positioning accuracy, ranging accuracy, and angle measurement accuracy values. The anti-spoofing jamming success rate corresponding to different operating modes is obtained. The Monte Carlo experimental method is used to evaluate the anti-spoofing jamming success rate index of the networked radar system using simulation data. Finally, the operating mode is switched according to the actual application scenario of the networked radar and the requirements of the anti-spoofing jamming success rate index to meet the requirements.

[0212] The beneficial effects of the present invention will be verified and explained through simulation experiments below.

[0213] (I) Simulation Experiment Conditions

[0214] The signal parameters set for the virtual isomorphic networked radar mode are as follows: the number of real targets in space is 1, the position coordinates are [5, 5, 5] km, the actual velocity vector is [50, 50, 50] m / s, the information of each radar is shown in Table 1 below, the number of active false targets is 1, and its deception range is assumed to be from... Change to m, the success rate of identifying active false targets was obtained by statistical analysis of 5000 Monte Carlo simulation experiments.

[0215] Table 1 Parameters of Virtual Homogeneous Networked Radar Mode

[0216] Radar type Location coordinates (km) Distance measurement accuracy (m) Angular measurement accuracy (°) Speed ​​measurement accuracy (m / s) Radar 1 [0,0,0] 25 0.6 5 Radar 2 [0,10,0] 25 0.6 5

[0217] In the hypothesis testing model, the significance level α = 0.01 and the discrimination threshold η1 = 9.21.

[0218] The signal parameters set for the virtual multistatic radar mode are as follows: the number of real targets in space is 1, the position coordinates are [5, 5, 5] km, the actual velocity vector is [50, 50, 50] m / s, the information of each radar is shown in Table 2 below, the number of active false targets is 1, and its deception range is assumed to be from... Change to The success rate of identifying active false targets was obtained by statistically analyzing 5000 Monte Carlo simulation experiments.

[0219] Table 2 Parameters of Virtual Multistatic Radar Mode

[0220] Radar type Location coordinates (km) Distance measurement accuracy (m) Angular measurement accuracy (°) T / R station [0,0,0] 25 0.1 R Station [0,10,0] 50 0.1

[0221] In the hypothesis testing model, the significance level α = 0.01 and the discrimination threshold η² = 9.21.

[0222] The signal parameters set for the virtual active / passive radar mode are as follows: the number of real targets in space is 1, the position coordinates are [5, 5, 5] km, the actual velocity vector is [50, 50, 50] m / s, and the information of each radar is shown in Table 3 below. The number of active false targets is 1, and its deception range is assumed to be from... Change to The success rate of identifying active false targets was obtained by statistically analyzing 10,000 Monte Carlo simulation experiments.

[0223] Table 2 Parameters of Virtual Active / Passive Radar Modes

[0224]

[0225] In the hypothesis testing model, the significance level α = 0.01 and the discrimination threshold η3 = 9.21.

[0226] (II) Simulation Experiment Content and Result Analysis

[0227] This invention provides three operating modes: virtual isomorphic network radar mode, virtual multistatic radar mode, and virtual active / passive radar mode. Simulation results for these modes are as follows: Figure 8 , Figure 9 , Figure 10 , Figure 8 This is a graph showing the change in anti-jamming success rate based on location information with deception distance for the virtual isomorphic network radar mode. The horizontal axis represents the deception distance, and the vertical axis represents the anti-jamming success rate. Figure 9 This is a graph showing the anti-jamming success rate of a multi-base networked radar system in a virtual multi-base radar mode, varying with the deception distance. The horizontal axis represents the deception distance, and the vertical axis represents the anti-jamming success rate. Figure 10 This is a graph showing the anti-jamming success rate as a function of deception distance based on two-dimensional angle information for virtual active / passive radar modes. The horizontal axis represents the deception distance, and the vertical axis represents the anti-jamming success rate. Since the utilization rate of measurement information by the networked radar system varies in each operating mode, and the anti-jamming performance also differs, this invention, after evaluating the effectiveness of each operating mode, allows for the selection of an appropriate operating mode to implement electronic countermeasures based on the actual environment and needs. This effectively addresses the drawback of single-mode configuration in multi-station systems, verifying the advanced nature of this invention.

[0228] This invention studies a data fusion anti-deception jamming method based on a networked radar system. It is more flexible than existing methods, has the ability to configure resources on demand, and realizes a series of radar operating modes such as traditional bistatic / multistatic radar, homogeneous networked radar, and active / passive radar. It overcomes the shortcomings of existing single multistatic radar systems and achieves different anti-jamming performance requirements.

[0229] This invention studies a data-level fusion anti-spoofing jamming method on a networked radar system. The system will obtain different operating modes according to the configuration, establish the Cramer-Rao lower bound of the measurement estimation error for each mode, obtain the positioning accuracy, ranging accuracy, and angle measurement accuracy values, and obtain the anti-spoofing jamming success rate corresponding to different operating modes. The Monte Carlo experimental method is used to evaluate the anti-spoofing jamming success rate index of the networked radar system using simulation data.

[0230] The method for evaluating the anti-jamming performance of a networked radar system provided by this invention evaluates the effectiveness of the adopted working mode. If the mode cannot meet the anti-jamming performance requirements, the current mode is switched to a mode that meets the requirements, thereby ultimately achieving the goal of improving the anti-jamming performance of the networked radar system.

[0231] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0232] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0233] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings and the disclosure in carrying out the claimed invention. In this specification, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. While certain measures are described in different embodiments, this does not mean that these measures cannot be combined to produce good results.

[0234] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for resisting deceptive jamming based on networked radar, characterized in that, include: The networked radar system is configured into a virtual isomorphic networked radar mode, a virtual multistatic radar mode, and a virtual active / passive radar mode, respectively. The virtual isomorphic networked radar mode includes N first virtual active radars, the virtual multistatic radar mode includes 1 T / R station radar and 1 R station radar, and the virtual active / passive radar mode includes 1 second virtual active radar and 1 virtual passive radar, wherein N≥2. In the virtual isomorphic network radar mode, the error covariance matrix of the measurement value of the first virtual active radar is obtained based on the measurement value of the first virtual active radar, and the anti-spoofing interference success rate of the virtual isomorphic network radar mode is determined based on the error covariance matrix of the measurement value of the first virtual active radar. In the virtual multi-static radar mode, the error covariance matrix of the measurement values ​​of the T / R station radar and the error covariance matrix of the measurement values ​​of the R station radar are obtained based on the measurement values ​​of the T / R station radar and the R station radar. The anti-spoofing jamming success rate of the virtual multi-static radar mode is determined based on the error covariance matrix of the measurement values ​​of the T / R station radar and the error covariance matrix of the measurement values ​​of the R station radar. In the virtual active / passive radar mode, the azimuth and elevation angles of the target relative to the virtual passive radar are obtained based on the measurement values ​​of the second virtual active radar, and the error covariance matrix of the azimuth and elevation angles is obtained based on the azimuth and elevation angles of the target relative to the virtual passive radar. The anti-deception jamming success rate of the virtual active / passive radar mode is determined based on the error covariance matrix of the azimuth and elevation angles. The mode corresponding to the maximum value among the anti-spoofing jamming success rates of the virtual isomorphic network radar mode, the virtual multi-static radar mode, and the virtual active / passive radar mode is selected as the final mode.

2. The anti-spoofing jamming method based on networked radar according to claim 1, characterized in that, The error covariance matrix of the first virtual active radar measurement values ​​is obtained based on the measurement values ​​of the first virtual active radar. The anti-spoofing interference success rate of the virtual isomorphic network radar mode is then determined based on the error covariance matrix of the first virtual active radar measurement values. N first virtual active radars are set up, and the measurement values ​​of the N first virtual active radars are obtained. The N first virtual active radars illuminate the same area. The measurement values ​​of the first virtual active radars include the distance between the first virtual active radar and the target, the azimuth angle between the first virtual active radar and the target, and the radial velocity of the target. The measurement values ​​of the N first virtual active radars are sequentially time-aligned and spatially aligned to transform them into a unified coordinate system, wherein the coordinate position of the nth first virtual active radar in the unified coordinate system is: Where, r n Let θ be the distance between the nth virtual active radar and the target. n Let [x] be the azimuth angle between the nth virtual active radar and the target. n ,y n [] represents the position coordinates of the nth virtual active radar, where 1 ≤ n ≤ N; The error covariance matrix of the measurement values ​​of the nth virtual active radar is obtained from the coordinate position of the nth virtual active radar in the unified coordinate system. A first discrimination statistic is constructed based on the first variance obtained from the error covariance matrix of the measurement values ​​of the nth first virtual active radar and the error covariance matrix of the measurement values ​​of the mth first virtual active radar. The true target and the false target are determined based on the first discrimination statistic, so as to determine the anti-deception jamming success rate of the virtual isomorphic networking radar mode.

3. The anti-spoofing jamming method based on networked radar according to claim 2, characterized in that, The error covariance matrix of the nth virtual active radar is expressed as: P n =E[dZ n dZ n T ]=T n L n T n T Among them, P n Let E[·] be the error covariance matrix of the nth virtual active radar, and E[·] be the expected sign. diag is a diagonal matrix function, σ r,n Let σ be the ranging accuracy of the nth virtual active radar. θ,n Let be the angle measurement accuracy of the first virtual active radar of the nth unit.

4. The anti-spoofing jamming method based on networked radar according to claim 3, characterized in that, A first discrimination statistic is constructed based on the first variance obtained from the error covariance matrix of the measurement values ​​of the nth first virtual active radar and the error covariance matrix of the measurement values ​​of the mth first virtual active radar. The true and false targets are then determined based on the first discrimination statistic to determine the anti-spoofing jamming success rate of the virtual isomorphic network radar mode, including: Based on the error covariance matrix of the measurement values ​​of the nth first virtual active radar and the error covariance matrix of the measurement values ​​of the mth first virtual active radar, the first variance of the measurement values ​​of the nth first virtual active radar minus the measurement values ​​of the mth first virtual active radar is obtained. The first variance is expressed as: Σ nm =E[d(Z n -WITH m )d(Z n -WITH m ) T ]=P n +P m Where, Σ nm Z is the first variance. m To unify the coordinate position of the m-th virtual active radar in the coordinate system, P m Let Z be the error covariance matrix of the measurement values ​​of the m-th virtual active radar, d(·) be the differential sign, and Z be the error covariance matrix of the first virtual active radar. n -Z m To unify the difference between the coordinate positions of the nth virtual active radar and the mth virtual active radar in the coordinate system, d(Z) n -Z m ) for Z n -Z m Perform differentiation operations; Based on the first variance, the coordinate position of the m-th first virtual active radar in the unified coordinate system, and the coordinate position of the n-th first virtual active radar in the unified coordinate system, a first discrimination statistic is obtained. The first discrimination statistic is expressed as: d nm =(Z n -WITH m ) T Σ nm -1 (WITH n -WITH m ) Where, d nm This is the first identification statistic; Determine the relationship between the first discrimination statistic and the first threshold. If the first discrimination statistic is less than or equal to the first threshold, it is a true target; if the first discrimination statistic is greater than the first threshold, it is a false target. Based on the Monte Carlo experiment, the anti-deception jamming success rate of the virtual isomorphic network radar mode is determined according to the identified real and false targets.

5. The anti-spoofing jamming method based on networked radar according to claim 1, characterized in that, Based on the measurements from the T / R station radar and the R station radar, the error covariance matrix of the T / R station radar measurements and the error covariance matrix of the R station radar measurements are obtained. Then, based on the error covariance matrices of the T / R station radar measurements and the R station radar measurements, the anti-spoofing jamming success rate of the virtual multi-static radar mode is determined, including: Set up one T / R station radar and one R station radar, and acquire the measurement values ​​of the T / R station radar and the R station radar. The measurement values ​​of the T / R station radar include the distance between the T / R station radar and the target and the azimuth angle between the T / R station radar and the target. The measurement values ​​of the R station radar include the distance from the target to the T / R station radar and the distance from the target to the R station radar, as well as the azimuth angle from the target to the R station radar. The measurement values ​​of the T / R station radar and the R station radar are sequentially time-aligned and spatially aligned to transform them into a unified coordinate system. The coordinate position of the T / R station radar in the unified coordinate system is Z. 11 =[x 11 ,y 11 ] T , The coordinate position of the radar at station R in the unified coordinate system is Z. 12 =[x 12 ,y 12 ] T , Where, r T θ represents the distance between the T / R station radar and the target. T The azimuth angle between the T / R station radar and the target, [x T ,y T [r] represents the location coordinates of the radar at the T / R station. R Let θ be the distance between the radar at station R and the target. R Let R be the azimuth angle between the radar and the target, [x] R ,y R [ ] represents the location coordinates of the radar at station R; The error covariance matrix of the measurement values ​​of the T / R station radar and the error covariance matrix of the measurement values ​​of the R station radar are obtained from the coordinate positions of the T / R station radar and the R station radar respectively in a unified coordinate system. A second discrimination statistic is constructed based on the second variance obtained from the error covariance matrix of the measurement values ​​of the T / R station radar and the error covariance matrix of the measurement values ​​of the R station radar. Based on the second discrimination statistic, true targets and false targets are determined to determine the anti-deception jamming success rate of the virtual multi-static radar mode.

6. The anti-spoofing jamming method based on networked radar according to claim 5, characterized in that, The error covariance matrix of the measurements from the T / R station radar is expressed as: P 11 =E[dZ 11 dZ 11 T ]=T 11 L T T 11 T Among them, P 11 Let E[·] be the error covariance matrix of the radar measurements at the T / R station, and E[·] be the expected sign. Λ T =diag(σ r,T 2 ,σ θ,T 2 ), σ r,T For the ranging accuracy of the T / R station radar, σ θ,T For the angle measurement accuracy of the T / R station radar; The error covariance matrix of the radar measurements at station R is expressed as: Among them, P 12 Let c be the error covariance matrix of the radar measurements at station R. R1 =cosθ R c T1 =cosθ T c R2 =sinθ R c T2 =sinθ T , σ ρ Let σ be the standard deviation of the sum of the distances from the target to the T / R station radar and the distance from the target to the R station radar. θ Let be the standard deviation of the variance of the radar at station R.

7. The anti-spoofing jamming method based on networked radar according to claim 6, characterized in that, A second discrimination statistic is constructed based on the second variance obtained from the error covariance matrix of the measurements from the T / R station radar and the error covariance matrix of the measurements from the R station radar. Based on this second discrimination statistic, true and false targets are determined to ascertain the anti-spoofing jamming success rate of the virtual multi-static radar mode, including: Based on the error covariance matrix of the T / R station radar measurements and the error covariance matrix of the R station radar measurements, the second variance of the T / R station radar measurements minus the R station radar measurements is obtained. The second variance is expressed as: Σ=E[d(Z 11 -WITH 12 )d(Z 11 -WITH 12 ) T ]=P 11 +P 12 Where Σ is the second variance, d(·) is the differential sign, and Z 11 -Z 12 To unify the difference between the coordinate positions of the T / R station radar and the R station radar in the coordinate system, d(Z 11 -Z 12 ) for Z 11 -Z 12 Perform differentiation operations; The second discrimination statistic is obtained based on the second variance, the coordinate positions of the T / R station radar in the unified coordinate system, and the coordinate positions of the R station radar in the unified coordinate system. The second discrimination statistic is expressed as follows: d=(Z 11 -WITH 12 ) T Σ -1 (WITH 11 -WITH 12 ) Where d is the second discrimination statistic; Determine the relationship between the second discrimination statistic and the second threshold. If the second discrimination statistic is less than or equal to the second threshold, it is a true target; if the second discrimination statistic is greater than the second threshold, it is a false target. Based on the Monte Carlo method, the anti-deception jamming success rate of the virtual multistatic radar mode is determined according to the identified real and false targets.

8. The anti-spoofing jamming method based on networked radar according to claim 1, characterized in that, The azimuth and elevation angles of the target relative to the virtual passive radar are obtained based on the measurements from the second virtual active radar. The error covariance matrix of the azimuth and elevation angles is then obtained based on these values. The anti-spoofing jamming success rate of the virtual active / passive radar mode is determined based on the error covariance matrix of the azimuth and elevation angles, including: Set up one second virtual active radar and one virtual passive radar, and acquire the measurement values ​​of the second virtual active radar and the virtual passive radar. The measurement values ​​of the second virtual active radar include the distance between the second virtual active radar and the target, the azimuth angle between the second virtual active radar and the target, and the elevation angle between the second virtual active radar and the target. The measurement values ​​of the virtual passive radar include the azimuth angle between the virtual passive radar and the target and the elevation angle between the virtual passive radar and the target. The measurements from the second virtual active radar are sequentially time-aligned and spatial-aligned to transform them into a unified coordinate system, thereby obtaining the azimuth and elevation angles of the target relative to the virtual passive radar. The error covariance matrix of the two-dimensional angle estimation deviation is obtained by constructing a transformation matrix based on the azimuth and elevation angles of the target relative to the virtual passive radar. A third discrimination statistic is constructed based on the two-dimensional angle estimation deviation and the error covariance matrix of the two-dimensional angle estimation deviation, and the true and false targets are determined based on the third discrimination statistic to determine the anti-deception jamming success rate of the virtual active / passive radar mode.

9. The anti-spoofing jamming method based on networked radar according to claim 8, characterized in that, The error covariance matrix of the two-dimensional angle estimation bias is obtained by constructing a transformation matrix based on the azimuth and elevation angles of the target relative to the virtual passive radar, including: A transformation matrix is ​​constructed based on the azimuth and elevation angles of the target relative to the virtual passive radar. The transformation matrix is ​​expressed as follows: Where T is the transformation matrix, θ 12 The azimuth angle of the target relative to the virtual passive radar. Let r1 be the elevation angle of the target relative to the virtual passive radar, r1 be the distance between the second virtual active radar and the target, and θ1 be the azimuth angle between the second virtual active radar and the target. The elevation angle between the second virtual active radar and the target; The error covariance matrices of azimuth and elevation angles are obtained based on the transformation matrix and the error covariance matrix of the measurements from the second virtual active radar. These error covariance matrices are expressed as follows: P=TΛT T Where P is the error covariance matrix of azimuth and elevation angles, and Λ is the error covariance matrix of the measurement values ​​of the second virtual active radar. The error covariance matrix of the two-dimensional angle estimation deviation is obtained based on the error covariance matrices of the azimuth and elevation angles and the error covariance matrix of the measurements from the virtual passive radar. The error covariance matrix of the two-dimensional angle estimation deviation is expressed as follows: Q = P + Λ' Where Q is the error covariance matrix of the two-dimensional angle estimation deviation, and Λ' is the error covariance matrix of the measurement values ​​of the virtual passive radar.

10. The anti-spoofing jamming method based on networked radar according to claim 9, characterized in that, A third discrimination statistic is constructed based on the two-dimensional angle estimation bias and the error covariance matrix of the two-dimensional angle estimation bias. Based on this third discrimination statistic, true and false targets are determined to ascertain the anti-deception jamming success rate of the virtual active / passive radar mode, including: A third discrimination statistic is constructed based on the two-dimensional angle estimation bias and the error covariance matrix of the two-dimensional angle estimation bias. The third discrimination statistic is expressed as follows: D'=e 12 T Q -1 e 12 Where Δ' is the third discrimination statistic, ε 12 This is the deviation in the two-dimensional angle estimation. θ2 is the azimuth angle between the virtual passive radar and the target. The elevation angle between the virtual passive radar and the target; Determine the relationship between the third discrimination statistic and the third threshold. If the third discrimination statistic is less than or equal to the third threshold, it is a true target; if the third discrimination statistic is greater than the third threshold, it is a false target. Based on the Monte Carlo method, the anti-deception jamming success rate of the virtual active / passive radar mode is determined according to the identified real and false targets.

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