A method for identifying model parameters of a drone system based on navigation deception technology

Abstract

The application belongs to the field of navigation deception anti-UAV, and specifically discloses a method for identifying UAV system model parameters based on navigation deception technology. The method comprises the following steps: (1) first, the UAV is described according to a state space model; (2) a false satellite navigation deception signal is transmitted to make the UAV receive it, and then an erroneous maneuver action is generated; (3) the false satellite navigation deception signal is recorded as system input; the position, speed and acceleration information of the UAV obtained by a radar or a third-party detection device is recorded as system output; (4) a numerical state subspace identification algorithm is used to estimate the system state space model parameters and noise covariance matrix; and (5) a deception controller is designed by using the estimation result of step (4). The application has strong universality, can effectively improve the control precision of the deception controller, and provides technical support for realizing UAV point fixed deception under the condition of navigation deception.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of navigation deception anti-UAV, in particular to a method for identifying UAV system model parameters based on navigation deception technology. BACKGROUND

[0002] In order to effectively deal with the current UAV "black flight" event, the anti-UAV technology system based on "detection, disturbance, deception, control and destruction" has been proposed, among which the navigation deception technology is being applied more and more widely due to its strong universality, moderate technical realization difficulty and controllable effect. In the modeling of navigation deception anti-UAV, a state space model has been established, and a deception device is designed based on a quadratic optimal estimator and a PD compensator. However, for non-cooperative UAVs, the measurement noise and process noise of the quadratic estimator are unknown, which reduces the engineering practicability.

[0003] The present application aims to improve the practicability of navigation deception anti-UAV modeling and proposes a method for identifying UAV system model parameters based on navigation deception technology. The method takes false satellite navigation deception signals as system inputs, and the position, velocity and acceleration information of UAV obtained by radar or third-party detection equipment as system outputs. The numerical state subspace identification method is used to estimate the state space model parameters and noise covariance matrix of non-cooperative UAV system, and then the deception controller is designed. SUMMARY

[0004] The present application proposes a method for identifying UAV system model parameters based on navigation deception technology. The method takes false satellite navigation deception signals as system inputs, and the position, velocity and acceleration information of UAV as system outputs. The numerical state subspace identification algorithm is used to estimate the state space model parameters and noise covariance matrix of non-cooperative UAV system, and then the deception controller is designed. The method can be applied to the field of navigation deception anti-UAV and has strong practical value. In order to achieve the above-mentioned application purposes, the technical solution adopted by the present application is as follows:

[0005] A method for identifying UAV system model parameters based on navigation deception technology, comprising the following steps:

[0006] Step (1): First, describe the UAV according to the state space model, give the system state equation and measurement equation, and execute step (2);

[0007] Step (2): When the unmanned aerial vehicle is in a stable flight state, a false satellite navigation deception signal is transmitted to force the navigation receiver in the unmanned aerial vehicle to receive and use the false satellite navigation deception signal, thereby generating an erroneous maneuver, including deviating from the preset route and flying in a circle; if the deception of the unmanned aerial vehicle is not achieved, that is, the unmanned aerial vehicle does not generate an erroneous maneuver, including deviating from the preset route and flying in a circle, step (2) is repeated until the deception is successful, and step (3) is performed;

[0008] Step (3): The false satellite navigation deception signal transmitted is recorded, including deception position, speed, and Doppler information as system input variables; the unmanned aerial vehicle position, speed, and acceleration information obtained by the radar or third-party detection equipment are recorded as system output variables, and step (4) is performed;

[0009] Step (4): A numerical state subspace identification algorithm is used to estimate system state space model parameters and noise covariance matrix information according to the input and output variables; if the parameter and noise covariance matrix estimation cannot be completed due to insufficient data, step (2) is performed, otherwise step (5) is performed;

[0010] Step (5): A false satellite navigation deception signal and a radar or third-party detection equipment are used to form a system closed-loop identification condition, and on the basis of estimating the system state space parameters and the noise covariance matrix, a deception controller is designed to realize navigation deception anti-unmanned aerial vehicle target.

[0011] The beneficial effects of the present application are:

[0012] Strong universality: the present application is effective for unmanned aerial vehicles that use a satellite navigation system as one of the sources of space-time information, and can obtain an accurate unmanned aerial vehicle system model by using a state space subspace system identification algorithm;

[0013] Improved deception controller control accuracy: the present application can identify unmanned aerial vehicle system state space model parameters and noise covariance matrices using navigation deception technology, and use them as prior knowledge to design a deception controller, thereby greatly improving the control accuracy of the deception controller. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a flowchart of the method for identifying unmanned aerial vehicle system model parameters based on navigation deception technology designed by the present application. DETAILED DESCRIPTION

[0015] The present application will be described in detail below, and it should be understood that the content described herein is only used to explain the present application and is not limited to the present application.

[0016] The navigation deception signal generation and implementation flowchart is shown in Figure 1 The technical solution adopted by the present application is:

[0017] A method for identifying the model parameters of a UAV system based on navigation deception technology, comprising the following steps:

[0018] Step (1): First, describe the UAV according to the state space model, give the system state equation and measurement equation, as shown in formula (1), and execute step (2);

[0019]

[0020] Wherein, x k+1 =[r,v] T is the position and speed state value of the UAV at time k+1, x k is the system state value at time k, u k is the system input at time k, which is a deception signal or a function form input under deception condition, y k is the system output at time k, A, B, C, D are system parameter matrices, w k and v k are state and measurement noise respectively;

[0021] Step (2): When the UAV is in a stable flight state, a false satellite navigation deception signal is transmitted to force the navigation receiver in the UAV to receive and use the false satellite navigation deception signal, and then produce an error maneuvering action, including deviating from the preset route and flying in a circle; if the deception of the UAV is not achieved, that is, the UAV does not produce an error maneuvering action, including deviating from the preset route and flying in a circle, repeat step (2) until the deception is successful, and execute step (3);

[0022] Step (3): Record the transmitted false satellite navigation deception signal, including deception position, speed, and Doppler information as system input variables; record the UAV position, speed, and acceleration information obtained by radar or third-party detection equipment as system output variables, and execute step (4);

[0023] Step (4): Use the numerical state subspace identification algorithm to estimate the system state space model parameters and noise covariance matrix information according to the input and output variables; if the parameter and noise covariance matrix estimation cannot be completed due to insufficient data, execute step (2), otherwise execute step (5); the specific identification method is as follows: from formula (1), the noise covariance matrix can be obtained:

[0024]

[0025] Wherein, E represents the mathematical expectation operator, δ M is the Kronecker operator, O s , S s , R sare noise covariance sub-matrices obtained by matrix block, and superscript T represents matrix transpose; assuming that the data obtained in the identification process is infinite and ergodic, the estimation formula of the augmented state equation and the state equation is determined as follows:

[0026]

[0027] wherein, represents the i+1th row of the UAV state estimation value, Y i|i represents the i-th row of the system output matrix, A, B, C, and D are system parameter matrices, represents the i-th row of the UAV state estimation value, U i|i is the i-th row of the lower triangular Toeplitz matrix, ρ1 and ρ2 are and Y i|i noise estimation standard deviation matrix.

[0028] The estimation formula of the noise covariance matrix is determined as follows:

[0029]

[0030] wherein O s , S s , and R s are noise covariance sub-matrices defined in formula (2), superscript T represents matrix transpose, j is the integral filter square sum, and ρ1 and ρ2 are noise estimation standard deviation matrices defined in formula (3).

[0031] Step (5): using the false satellite navigation deception signal and radar or third-party detection equipment to form a system closed-loop identification condition, on the basis of estimating the system state space parameters and the noise covariance matrix, further designing a deception controller for realizing the navigation deception anti-UAV target.

[0032] Although the illustrative specific embodiments of the present application are described above, the present application is not limited to this range, and all the inventions and creations using the concept of the present application are within the protection scope as long as various changes are within the spirit and scope of the present application.

Claims

1. A method for identifying unmanned aerial vehicle (UAV) system model parameters based on navigation deception technology, characterized in that, Includes the following steps: Step (1): First, describe the UAV according to the state-space model and give the system state equation and measurement equation; Step (2): When the UAV is in a stable flight state, a false satellite navigation deception signal is emitted to force the navigation receiver in the UAV to receive and use the false satellite navigation deception signal, thereby generating incorrect maneuvers, including deviating from the preset route and hovering. Step (3): Record the false satellite navigation deception signal transmitted, including the deception position, velocity, and Doppler information as system input variables; record the UAV position, velocity, and acceleration information obtained by radar or third-party detection equipment as system output variables; Step (4): Using the numerical state subspace identification algorithm, the system state space model parameters and noise covariance matrix information are estimated based on the input and output variables; Step (5): Using fake satellite navigation deception signals and radar or third-party detection equipment to form system closed-loop identification conditions, based on estimating the system state space parameters and noise covariance matrix, further design a deception controller to achieve navigation deception against UAV targets.

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