Aerial target threat assessment method and device based on motion parameters and signal parameters

Abstract

The present invention discloses a method and device for assessing the threat of aerial targets based on motion parameters and signal parameters. This method, which belongs to the field of situational awareness, includes the following steps: utilizing active detection equipment to radiate detection signals into the air and receive reflected signals from aerial targets; then, by analyzing the correspondence between the received and transmitted signals, determining the amplitude of the reflected signals, as well as the range, speed, and orientation of the aerial targets; and combining these four parameters to estimate the threat level of the aerial targets to the environment or platform where the active detection equipment is located. The present invention estimates the target threat level in real time based on the target motion parameters and detection signal parameters, accurately characterizing the target threat level and improving the efficiency of airspace monitoring.

Description

Technical Field

[0001] The present invention relates to the field of situational awareness, and more specifically, to a method and device for air target threat assessment based on motion parameters and signal parameters. Background Art

[0002] In the field of situational awareness, using active detection equipment to monitor the status of unidentified flying objects in the air presents a technical challenge: low monitoring efficiency. Existing target threat assessment schemes primarily include the following: 1) Calculating threat factors based on the target's motion state, then weighting these factors to determine the target's threat level, with the sum of the weights equal to 1; 2) Target threat assessment based on multi-source sensor information, combining sensor credibility, DS models, decision rules, pattern recognition, and inference networks; and 3) Using neural networks or machine learning to train and model a large number of threat target parameters and states, the resulting models are then used to assess the target's threat level in the same application scenario.

[0003] The above method has the following shortcomings in the application of aerial target monitoring:

[0004] 1) Accurately calculating the weights of each threat factor is difficult. Different scenarios have different weights, and even different experts may determine different weights for the same scenario. 2) Excessive computational steps can severely impact algorithm stability. Any single step can significantly affect the final threat assessment, and these steps are likely closely related to the specific application scenario. 3) Machine learning requires a large amount of threat target sample data, which is difficult to collect in practice. Therefore, the resulting models are not necessarily accurate, resulting in poor application results. Furthermore, the complexity of the computing platform leads to poor portability. Due to these shortcomings, the efficiency of aerial UFO status monitoring needs to be further improved. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide an aerial target threat assessment method and device based on motion parameters and signal parameters. The target threat level is estimated in real time according to the target motion parameters and detection signal parameters, which can accurately characterize the target threat level and improve the efficiency of airspace monitoring.

[0006] The object of the present invention is achieved through the following solutions:

[0007] A method for assessing aerial target threat based on motion parameters and signal parameters comprises the following steps:

[0008] Use active detection equipment to radiate detection signals into the air and receive reflected signals from aerial targets;

[0009] Then, by analyzing the correspondence between the received signal and the transmitted signal, the amplitude of the reflected signal, as well as the distance, speed and direction of the aerial target are determined. The four-dimensional parameters of amplitude, distance, speed and direction are combined to estimate the threat level of the aerial target to the environment or platform where the active detection equipment is located.

[0010] Furthermore, the estimation of the threat level of the aerial target to the environment or platform where the active detection equipment is located by combining the four-dimensional parameters of amplitude, distance, speed and direction specifically includes the following sub-steps:

[0011] S1, establish the state equation and observation equation of the target;

[0012] S2, calculate the prior estimate and the prior error covariance matrix;

[0013] S3, calculate the Kalman filter gain;

[0014] S4, calculate the posterior estimate and the posterior error covariance matrix;

[0015] S5, repeating steps S3 to S5, and obtaining the target real-time threat level after the target threat level converges.

[0016] Furthermore, in step S1, the state equation of the target is established, specifically including the sub-steps:

[0017] The state equation of the target is established as follows: k T k ] T =A[R k-1 T k-1 ] T +W k , where R k and T k They represent the scattering cross section and threat degree of the target at time k, is the state transfer matrix, t is the observation time interval, W k is the target state estimation error at time k, let W k It follows a Gaussian distribution with mean zero and variance σ.

[0018] Furthermore, in step S1, establishing the observation equation of the target specifically includes the following sub-steps:

[0019] The observation equation of the target is established as follows: Among them, Am is the target echo amplitude, D is the target threat distance, Ve is the target speed, P t is the radiation power, G t is the transmitting antenna gain, G ris the receiving antenna gain, R is the target scattering cross-sectional area, c is the speed of light, f is the frequency of the radiation signal, T is the target threat level, Az is the target direction, and D max is the maximum threat distance of the target, Ve max is the target maximum speed, v Am 、v D 、v v and v Az Represent the measurement errors of amplitude, distance, speed and direction respectively.

[0020] Furthermore, in step S2, the calculation of the a priori estimate and the a priori error covariance matrix specifically includes the following sub-steps:

[0021] Calculate the a priori estimate of R k T k ] - =A[R k-1 T k-1 ] and the prior error covariance matrix in is the k-time prior error covariance matrix, P k-1 is the k-1 moment posterior error covariance matrix, A T is the transpose of the state transfer matrix A, E is the expected operation, is the transpose of the target state estimation error at time k.

[0022] Furthermore, in step S3, the calculation of the Kalman filter gain specifically includes the following sub-steps:

[0023] The Kalman filter gain is calculated as follows: Among them, H T is the transpose of matrix H, matrix Measurement error at time k is the measurement error V at time k k The transpose of is the amplitude measurement error at time k, is the distance measurement error at time k, is the velocity measurement error at time k, is the azimuth measurement error at time k.

[0024] Furthermore, in step S4, calculating the posterior estimate specifically includes the following sub-steps:

[0025] The posterior estimate [R k T k ]=[R k T k ] - +K k([Am D Ve Az] T -h([R k T k ] - )).

[0026] Furthermore, in step S4, calculating the posterior error covariance matrix specifically includes the following sub-steps: calculating the posterior error covariance matrix as follows: h is the first-order Taylor expansion function, I is the identity matrix, K k is the Kalman filter gain.

[0027] Furthermore, in step S5, after the target threat level converges, the method further includes a sub-step of performing finite point median filtering or mean filtering to obtain the target real-time threat level.

[0028] An aerial target threat assessment device based on motion parameters and signal parameters, comprising active detection equipment and a signal processing device;

[0029] The active detection equipment is used to radiate detection signals into the air and receive reflected signals from aerial targets;

[0030] The signal processing device is used to determine the amplitude of the reflected signal by analyzing the corresponding relationship between the received signal and the transmitted signal, as well as the range, speed and direction of the aerial target, and to estimate the threat level of the aerial target to the environment or platform where the active detection equipment is located by combining the four parameters of amplitude, range, speed and direction;

[0031] The signal processing device is further used to execute the aerial target threat assessment method based on motion parameters and signal parameters as described in any one of claims 2 to 9.

[0032] The beneficial effects of the present invention include:

[0033] The present invention estimates the target threat level in real time based on target motion parameters and detection signal parameters, thereby improving airspace monitoring efficiency.

[0034] The proposed method jointly estimates the target threat level based on target motion parameters and detection signal parameters. This method continuously and in real time estimates the target threat level based on a target state change model. This method not only achieves convergence quickly but also dynamically adjusts based on target state changes. This method addresses issues such as the difficulty of setting threat factor weights, complex and time-consuming calculations, the need for a large number of training samples, and erratic threat assessment results. It thus enables low-cost and rapid threat assessment of aerial targets, improving monitoring efficiency.

[0035] This method uses Kalman filtering to assess target threat levels based on target motion parameters and radiation signal parameters. Applications have shown that this method can quickly estimate target threat levels with rapid convergence. Under stable monitoring system operating conditions, convergence is achieved after 10 to 20 consecutive measurements, and the method can accurately characterize the target threat level. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 Schematic diagram of the application scenario;

[0038] Figure 2 Estimation of UAV threat level in cruise state;

[0039] Figure 3 Estimation of the threat level of drones in attack state. DETAILED DESCRIPTION

[0040] All features disclosed in all embodiments in this specification, or steps in all methods or processes implicitly disclosed, except for mutually exclusive features and / or steps, can be combined and / or expanded or replaced in any manner.

[0041] The present invention uses the target motion parameters and detection signal parameters in combination with Kalman filtering to perform real-time assessment of the threat level of aerial targets. The specific operation steps are as follows:

[0042] Step 1: First, establish the target state equation. The target state includes the scattering cross-section and threat level. The scattering cross-section is related to the target's shape, size, material and other factors, and the threat level is related to the target platform characteristics and subjective consciousness. Therefore, the target state equation can be expressed as [R k T k ] T =A[R k-1 T k-1 ] T +W k , where R k and T k They represent the scattering cross section and threat degree of the target at time k, is the state transfer matrix, W k is the target state estimation error at time k, which is assumed to obey a Gaussian distribution with a mean of zero and a variance of σ.

[0043] Step 2: Establish the observation equation of the target. The observed quantities of the target are the echo signal amplitude, target distance, speed and direction, and its observation equation can be expressed as Where C is a constant related to system and signal parameters, D max is the maximum threat distance of the target, Ve max is the target maximum speed, v Am 、v D 、v v and v Az Represent the measurement errors of amplitude, distance, speed and direction respectively.

[0044] Step 3: Calculate the prior estimate [R k T k ] - =A[R k-1 T k-1 ] and the prior error covariance matrix in

[0045] Step 4: Calculate the Kalman filter gain where the matrix Measurement error at time k

[0046] Step 5: Calculate the posterior estimate [R k T k ]=[R k T k ] - +K k ([Am D Ve Az] T -h([R k T k ] - )) and the posterior error covariance matrix

[0047] Step 6: Finally, after multiple iterations, the target threat level (i.e. T k ) After convergence, finite point median filtering or mean filtering is performed to obtain the real-time threat level of the target.

[0048] Application scenarios of the present invention are as follows Figure 1 As shown in the figure, active detection equipment radiates detection signals into the air and receives reflected signals from aerial targets. By analyzing the correspondence between the received and transmitted signals, the amplitude of the reflected signal, as well as the range, speed, and direction of the aerial target, can be determined. Combining these four parameters, the method of the present invention can be used to estimate the threat level of the aerial target to the environment or platform where the detection equipment is located.

[0049] Because many factors influence the target's scattering cross-section, such as shape and material, operating frequency, polarization, and observation angle, the estimation of the scattering cross-section should be as consistent as possible over a short period of time, while also taking into account RF channel attenuation. The scattering cross-section measured using this non-cooperative method is not the target's true scattering cross-section. In practical applications, only the relative change in the scattering cross-section is sufficient. Similarly, the target threat level is also a relative value, but in practical applications it can be normalized. A threat level of 0 indicates that the target poses no significant threat and requires continuous observation and tracking for a short period of time. A threat level of 1 indicates that the target is highly aggressive and requires immediate action to address the threat.

[0050] Specifically, a certain experiment is used as an implementation example of the present invention, but it should be noted that the implementation methods of the present invention are not limited to this implementation example. The quadrotor drone is flying at an altitude of 1100m, a speed of 50km / h, a distance of 25km from the detection equipment, a detection signal frequency of 8GHz, a detection signal waveform of continuous wave, and a detection interval of 0.1s. It is assumed that the maximum detection range of the detection equipment is 30km and the maximum target speed is 40m / s. The calculation steps are as follows:

[0051] Step 1: Since the detection time interval is 0.1s, the state transfer matrix The state equation is

[0052] Step 2: Because D max =30km, Ve max =40m / s, then the observation equation Am=687.31×R / (1-T) 2 +v Am ,

[0053] Step 3: Set the initial target scattering cross-sectional area R0 = 1m 2 , threat level T0 = 0.1, and assuming the state estimation error is zero, then the prior estimate Prior error covariance

[0054] Step 4: Substitute the known parameters into the derivative expression to calculate the matrix The matrix P1 - , H, R are substituted into the Kalman filter gain K1=P1 - H T / (HP1 - H T +R) can be obtained

[0055] Step 5: The first beat measures the echo amplitude of 3.77mV, the target distance is 23.73km, the target speed is 3.6m / s, and the target bearing is 39 degrees. Calculate the posterior estimate Posterior error covariance

[0056] Step 6: Repeat steps 3 to 5 until the target state parameters converge after multiple iterations. After convergence, the estimated value of the target threat level can be obtained.

[0057] In the cruising state, drones have no obvious attack intentions and do not pose a significant threat to detection equipment, so their threat level is relatively low. However, objectively, drones have the possibility of attacking and pose a certain threat to detection equipment, so their threat level is not zero. Through experimental measurements, the threat level of drones fluctuates between 20% and 30%, such as Figure 2 shown.

[0058] In the attack state, the drone approaches the detection equipment rapidly, and its speed, track and other information are changing rapidly, which obviously poses a threat to the detection equipment. Through threat estimation, it is found that the drone threat level increases sharply from 20% to 90%, and converges quickly around 90%. The experimental data is as follows Figure 3 shown.

[0059] In summary, the method of the present invention can perform real-time threat estimation of aerial targets based on the target motion state and detection signal parameters, and has a fast convergence speed, thereby improving the efficiency of aerial target monitoring.

[0060] It should be noted that within the scope of protection defined in the claims of the present invention, the following embodiments can be combined and / or expanded or replaced in any logical way from the above specific implementation methods, such as disclosed technical principles, disclosed technical features or implicitly disclosed technical features.

[0061] Example 1

[0062] A method for assessing aerial target threat based on motion parameters and signal parameters comprises the following steps:

[0063] Use active detection equipment to radiate detection signals into the air and receive reflected signals from aerial targets;

[0064] Then, by analyzing the correspondence between the received signal and the transmitted signal, the amplitude of the reflected signal, as well as the distance, speed and direction of the aerial target are determined. The four-dimensional parameters of amplitude, distance, speed and direction are combined to estimate the threat level of the aerial target to the environment or platform where the active detection equipment is located.

[0065] Example 2

[0066] Based on Example 1, the method of estimating the threat level of an aerial target to the environment or platform where the active detection equipment is located by combining the four-dimensional parameters of amplitude, distance, speed and azimuth specifically includes the following sub-steps:

[0067] S1, establish the state equation and observation equation of the target;

[0068] S2, calculate the prior estimate and the prior error covariance matrix;

[0069] S3, calculate the Kalman filter gain;

[0070] S4, calculate the posterior estimate and the posterior error covariance matrix;

[0071] S5, repeating steps S3 to S5, and obtaining the target real-time threat level after the target threat level converges.

[0072] Example 3

[0073] On the basis of Example 2, in step S1, establishing the state equation of the target specifically includes the following sub-steps:

[0074] The state equation of the target is established as follows: k T k ] T =A[R k-1 T k-1 ] T +W k , where R k and T k They represent the scattering cross section and threat degree of the target at time k, is the state transfer matrix, t is the observation time interval, W k is the target state estimation error at time k, let W k It follows a Gaussian distribution with mean zero and variance σ.

[0075] Example 4

[0076] Based on Example 2, in step S1, establishing the observation equation of the target specifically includes the following sub-steps:

[0077] The observation equation of the target is established as follows: Among them, Am is the target echo amplitude, D is the target threat distance, Ve is the target speed, P t is the radiation power, G t is the transmitting antenna gain, G r is the receiving antenna gain, R is the target scattering cross-sectional area, c is the speed of light, f is the frequency of the radiation signal, T is the target threat level, Az is the target direction, and Dmax is the maximum threat distance of the target, Ve max is the target maximum speed, v Am 、v D 、v v and v Az Represent the measurement errors of amplitude, distance, speed and direction respectively.

[0078] Example 5

[0079] Based on Example 2, in step S2, the calculation of the a priori estimate and the a priori error covariance matrix specifically includes the following sub-steps:

[0080] Calculate the a priori estimate of R k T k ] - =A[R k-1 T k-1 ] and the prior error covariance matrix P k - =AP k-1 A T +Q, where is the k-time prior error covariance matrix, P k-1 is the k-1 moment posterior error covariance matrix, A T is the transpose of the state transfer matrix A, E is the expected operation, is the transpose of the target state estimation error at time k.

[0081] Example 6

[0082] On the basis of Example 2, in step S3, the calculation of the Kalman filter gain specifically includes the following sub-steps:

[0083] The Kalman filter gain is calculated as follows: Among them, H T is the transpose of matrix H, matrix Measurement error at time k is the measurement error V at time k k The transpose of is the amplitude measurement error at time k, is the distance measurement error at time k, is the velocity measurement error at time k, is the azimuth measurement error at time k.

[0084] Example 7

[0085] Based on Example 2, in step S4, calculating the posterior estimate specifically includes the following sub-steps:

[0086] The posterior estimate [R k T k ]=[R k T k ] - +K k ([Am D Ve Az] T -h([R k T k ] - )).

[0087] Example 8

[0088] On the basis of Example 2, in step S4, calculating the posterior error covariance matrix specifically includes the following sub-steps: calculating the posterior error covariance matrix in the following manner: h is the first-order Taylor expansion function, I is the identity matrix, K k is the Kalman filter gain.

[0089] Example 9

[0090] On the basis of Example 2, in step S5, after the target threat level converges, the method further includes a sub-step of performing finite point median filtering or mean filtering to obtain the target real-time threat level.

[0091] Example 10

[0092] An aerial target threat assessment device based on motion parameters and signal parameters, comprising active detection equipment and a signal processing device;

[0093] The active detection equipment is used to radiate detection signals into the air and receive reflected signals from aerial targets;

[0094] The signal processing device is used to determine the amplitude of the reflected signal by analyzing the corresponding relationship between the received signal and the transmitted signal, as well as the range, speed and direction of the aerial target, and to estimate the threat level of the aerial target to the environment or platform where the active detection equipment is located by combining the four parameters of amplitude, range, speed and direction;

[0095] The signal processing device is further used to execute the aerial target threat assessment method based on motion parameters and signal parameters described in any one of Examples 2 to 9.

[0096] The units involved in the embodiments of the present invention may be implemented in software or hardware, and the units described may also be provided in a processor. In some cases, the names of these units do not limit the units themselves.

[0097] According to one aspect of an embodiment of the present invention, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0098] As another aspect, embodiments of the present invention further provide a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not incorporated into the electronic device. The computer-readable medium carries one or more programs, and when executed by the electronic device, the electronic device implements the methods described in the above embodiments.

[0099] The parts not involved in the present invention are the same as the existing technology or can be implemented by using the existing technology.

[0100] The above technical solution is only one embodiment of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the application methods and principles disclosed in the present invention, and it is not limited to the method described in the above specific embodiment of the present invention. Therefore, the method described above is only preferred and does not have a restrictive meaning.

[0101] In addition to the above examples, those skilled in the art may obtain other embodiments based on the above disclosure or by utilizing knowledge or technology in related fields to make modifications. The features of each embodiment may be interchangeable or replaced. The modifications and changes made by those skilled in the art do not depart from the spirit and scope of the present invention and should be within the scope of protection of the claims attached to the present invention.

Claims

1. A method for assessing aerial target threat based on motion parameters and signal parameters, characterized in that: The following steps are involved: Use active detection equipment to radiate detection signals into the air and receive reflected signals from aerial targets; Then, by analyzing the correspondence between the received signal and the transmitted signal, the amplitude of the reflected signal, as well as the distance, speed and direction of the aerial target, are determined. The degree of threat posed by the aerial target to the environment or platform where the active detection equipment is located is estimated by combining these four parameters: amplitude, distance, speed and direction. The estimation of the threat level of the aerial target to the environment or platform where the active detection equipment is located by combining the four-dimensional parameters of amplitude, distance, speed and direction specifically includes the following sub-steps: S1, establish the state equation and observation equation of the target; S2, calculate the prior estimate and the prior error covariance matrix; S3, calculate the Kalman filter gain; S4, calculate the posterior estimate and the posterior error covariance matrix; S5, repeating steps S3 to S5, and obtaining the target real-time threat level after the target threat level converges; In step S1, the state equation of the target is established, specifically including the sub-steps: The state equation of the target is established as follows: ,in and Respectively The target's scattering cross-sectional area and threat level at each moment, is the state transfer matrix, t is the observation time interval, for The target state estimation error at time t is set It has a mean of zero and a variance of Gaussian distribution; In step S1, establishing the observation equation of the target specifically includes the following sub-steps: The observation equation of the target is established as follows: , , , , where Am is the target echo amplitude, D is the target threat distance, and Ve is the target speed. , is the radiated power, is the transmitting antenna gain, is the receiving antenna gain, is the target scattering cross section, is the speed of light, is the radiation signal frequency, T is the target threat level, Az is the target direction, is the maximum threat distance of the target, is the target maximum speed, 、 、 and Represent the measurement errors of amplitude, distance, speed and direction respectively.

2. The aerial target threat assessment method based on motion parameters and signal parameters according to claim 1, characterized in that: In step S2, the calculation of the a priori estimate and the a priori error covariance matrix specifically includes the following sub-steps: Compute a priori estimates and the prior error covariance matrix ,in , for The prior error covariance matrix at time t, for The posterior error covariance matrix at time t, is the state transition matrix The transpose of , E is the expected operation, for The transpose of the target state estimation error at time t.

3. The aerial target threat assessment method based on motion parameters and signal parameters according to claim 1, characterized in that: In step S3, the calculation of the Kalman filter gain specifically includes the following sub-steps: The Kalman filter gain is calculated as follows: ,in, is a matrix The transpose of the matrix , , Time measurement error , is the measurement error at time k The transpose of is the amplitude measurement error at time k, is the distance measurement error at time k, is the velocity measurement error at time k, is the azimuth measurement error at time k.

4. The aerial target threat assessment method based on motion parameters and signal parameters according to claim 1, characterized in that: In step S4, calculating the posterior estimate specifically includes the following sub-steps: The posterior estimate is calculated as follows ; h is the first-order Taylor expansion function, I is the identity matrix, is the Kalman filter gain.

5. The aerial target threat assessment method based on motion parameters and signal parameters according to claim 1, characterized in that: In step S4, calculating the posterior error covariance matrix specifically includes the following sub-steps: calculating the posterior error covariance matrix as follows , h is the first-order Taylor expansion function, I is the identity matrix, is the Kalman filter gain.

6. The method for air target threat assessment based on motion parameters and signal parameters according to claim 1, characterized in that: In step S5, after the target threat level converges, the method further includes the following sub-steps: performing finite point median filtering or mean filtering, and then obtaining the target real-time threat level.

7. An aerial target threat assessment device based on motion parameters and signal parameters, characterized in that: Including active detection equipment and signal processing equipment; The active detection equipment is used to radiate detection signals into the air and receive reflected signals from aerial targets; The signal processing device is used to determine the amplitude of the reflected signal by analyzing the corresponding relationship between the received signal and the transmitted signal, as well as the range, speed and direction of the aerial target, and to estimate the threat level of the aerial target to the environment or platform where the active detection equipment is located by combining the four parameters of amplitude, range, speed and direction; The signal processing device is further used to execute the aerial target threat assessment method based on motion parameters and signal parameters as described in any one of claims 2 to 6.

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