An anti-interference estimation and credibility evaluation method for UAV ultra-wideband positioning

Abstract

The present invention belongs to the field of unmanned aerial vehicle positioning, and specifically relates to an anti-interference estimation and credibility evaluation method for unmanned aerial vehicle ultra-wideband positioning. First, in view of the problem of unmanned aerial vehicle ultra-wideband and dynamic information fusion positioning, a nonlinear state augmented positioning system model including an unmanned aerial vehicle dynamics model, an ultra-wideband measurement equation and a dynamic interference model is established; secondly, a composite particle filter algorithm containing an interference compensation sampling link is designed to achieve simultaneous compensation and suppression of dynamic interference and ultra-wideband measurement noise; finally, the credibility index of the system is calculated in real time according to the Fisher information matrix of the system and the number of effective particles during actual operation, and the credibility evaluation of the positioning system and the estimation algorithm is completed. This method combines embedded dynamic characterization, fine anti-interference estimation algorithm design and credibility evaluation, and can effectively deal with the anti-interference positioning estimation problem under the coexistence of unmanned aerial vehicle dynamic interference and ultra-wideband measurement noise.

Description

Technical Field

[0001] The invention belongs to the field of unmanned aerial vehicle positioning, and in particular relates to an anti-interference estimation and credibility evaluation method for unmanned aerial vehicle ultra-wideband positioning, and is applicable to an unmanned aerial vehicle ultra-wideband positioning system. Background Art

[0002] In recent years, quadcopter drones have been widely used in various fields, and accurate positioning technology is the basis for drones to perform subsequent tasks, especially in complex environments where GPS signals are weak or unavailable, reliable indoor positioning becomes the key. Ultra-wideband technology has the advantages of low complexity, low power spectrum density of transmitted signals, insensitivity to channel fading, low interception capability, and high positioning accuracy, providing a solution for indoor positioning of drones. However, traditional ultra-wideband positioning technology has certain limitations when facing dynamic aircraft, and it is difficult to fully cope with the impact of environmental interference during flight. In order to solve these problems, ultra-wideband positioning needs to be combined with the dynamic model of the drone and advanced filtering technology to improve accuracy and robustness.

[0003] The dynamic model of the UAV can predict its position and speed based on the input of the UAV and provide prior information of the state. By embedding the UAV dynamic model, the positioning system can not only locate based on the observed data, but also use the model to predict the state, thereby reducing the dependence on the observed data and improving the robustness. Particle filtering is a nonlinear filtering method based on Bayesian estimation, which can handle non-Gaussian noise and nonlinear systems. In the dynamic positioning system of the UAV, the relationship between the state and the observation is often nonlinear, which is difficult for the classic Kalman filter to handle effectively. The particle filter can adapt to complex state and observation relationships by using multiple particles to represent the state space distribution. In addition, the interference observer can identify and estimate interference by monitoring the system errors in real time, thereby enhancing the system's anti-interference ability.

[0004] At the same time, positioning systems often face uncertain factors in practical applications, including measurement noise, external interference, etc. In order to quantify the system's trust in state estimation, it is necessary to calculate the system's credibility index to help the system judge the reliability of the estimation results when facing complex environments.

[0005] Chinese invention patent application CN109557506 proposed a method for precise positioning of UWB UAVs, but there are two problems: (1) When the UAV moves at high speed, the UWB positioning update rate is difficult to keep up with the UAV's motion dynamics; (2) The impact of external environmental interference on the positioning results is not considered. Chinese invention patent application CN114993299 proposed a method and system for assisting UAV positioning based on UWB, using an UWB module for positioning, but there are two problems: (1) UWB positioning only considers the kinematic model of the system, without considering the UAV dynamics model; (2) There is no quantification of the credibility of the state estimation results to judge the reliability of the estimation results. Summary of the invention

[0006] In order to overcome the shortcomings of the prior art, the present invention proposes an anti-interference estimation and credibility evaluation method for UAV ultra-wideband positioning to improve the accuracy and robustness of positioning. This method embeds a UAV dynamics model, finely characterizes and processes the composite interference, and simultaneously performs system credibility analysis, providing a UAV positioning solution with autonomous anti-interference and dynamic adaptability.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] A method for anti-interference estimation and credibility evaluation of UAV ultra-wideband positioning includes the following steps:

[0009] Step 1, establishing a nonlinear state augmented positioning system model including a UAV dynamics model, an ultra-wideband measurement model and a dynamic interference model;

[0010] Step 2, design a composite particle filter algorithm containing an interference compensation sampling link to achieve simultaneous compensation and suppression of dynamic interference and ultra-wideband measurement noise;

[0011] Step 3: Calculate the system's credibility index in real time based on the system's Fisher information matrix and the number of effective particles during actual operation to complete the credibility evaluation of the positioning system and estimation algorithm.

[0012] The beneficial effects of the present invention compared with the prior art are: the present invention is applicable to the UAV ultra-wideband positioning system, and through the embedded UAV dynamics model, the composite interference is finely characterized and processed, and the system credibility analysis is performed at the same time. This method can provide high-precision and strong robust positioning effects, especially in scenarios where the GPS signal is weak or unavailable. This method not only improves the positioning accuracy of the UAV, but also enhances the anti-interference ability of the system, and has broad application prospects. It mainly includes establishing a nonlinear state augmented positioning system model including a UAV dynamics model, an ultra-wideband measurement model and a dynamic interference model; designing a composite particle filter algorithm containing an interference compensation sampling link to achieve simultaneous compensation and suppression of dynamic interference and ultra-wideband measurement noise; according to the system's Fisher information matrix and the number of effective particles during actual operation, the system's credibility index is calculated in real time to complete the credibility evaluation of the positioning system and the estimation algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is the implementation step of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0015] First of all, the UAV ultra-wideband positioning method involved in the present invention can autonomously estimate and compensate for interference, and realize accurate estimation of the state of the UAV. First, the proposed method embeds the dynamic model of the UAV, and predicts the state of the UAV by inputting control quantities such as force and torque. Considering the interference factors such as wind and airflow in the actual environment, the state space is expanded and the interference state is introduced, so that the system can simultaneously estimate the dynamic state and external interference state of the UAV. Then, a composite particle filter is used to estimate the state of the UAV. The particle filter is recursively updated based on the dynamic model and sensor observation, and the optimal state estimate is retained by resampling. At the same time, an interference observer is designed to estimate the interference and feed it back to the particle filter for joint estimation. Finally, the system also introduces credibility calculation as a performance evaluation indicator of the system, and uses the observability of the system and the uniformity of the weights in the particle filtering process to measure the credibility of the positioning results.

[0016] like Figure 1 As shown, the specific implementation steps of the anti-interference estimation and credibility evaluation method of the UAV ultra-wideband positioning of the present invention are as follows:

[0017] Step 1: Establish a nonlinear state augmented positioning system model including the UAV dynamics model, ultra-wideband measurement model and dynamic interference model:

[0018] The nonlinear state augmented positioning system model including the UAV dynamics model, the ultra-wideband measurement model and the dynamics interference model is as follows:

[0019] (1)

[0020] Among them, the augmented state , the augmented process noise vector , , , , is the identity matrix, is the UWB measurement value. Where, is the system state that satisfies:

[0021] ,

[0022] Among them, the system status Contains the three-axis position and speed of the drone. is a nonlinear system model, is the input channel coefficient matrix of the dynamic disturbance, is a nonlinear measurement model, is the process noise that follows a Gaussian distribution , is the process noise variance, is the measurement noise that follows a Gaussian distribution , Represents the UAV dynamic disturbance. is the state variable of the interference subsystem, satisfying:

[0023] ,

[0024] in, is the state matrix of the interference subsystem, is the frequency parameter of the dynamic disturbance, is the output matrix of the interference subsystem; is the process noise of the interference subsystem, satisfying ; is the variance of the process noise of the interference subsystem; Formula (1) is the nonlinear state augmented positioning system model including the UAV dynamics model, ultra-wideband measurement model and dynamic interference model.

[0025] Step 2: Design a composite particle filter algorithm with interference compensation sampling link to achieve simultaneous compensation and suppression of dynamic interference and ultra-wideband measurement noise:

[0026] For the sampling time , the resampled particle set is updated according to the system equation and the statistical characteristics of process noise, where The system state value of each particle for:

[0027] ,

[0028] in, is generated by sampling the process noise, satisfying ;

[0029] Furthermore, for each particle , design a disturbance observer to estimate the state of the dynamic disturbance subsystem; The state of the interference subsystem corresponding to each particle and its estimated error covariance The calculation is as follows:

[0030] ,

[0031] ,

[0032] in:

[0033] ,

[0034] .

[0035] Step 3: Calculate the system's credibility index in real time based on the system's Fisher information matrix and the number of effective particles during actual operation to complete the credibility evaluation of the positioning system and estimation algorithm:

[0036] The purpose of credibility analysis is to determine whether the current measurements and the efficiency of the algorithm provide enough information to reliably estimate the state of the UAV. It is related to the theoretical capability boundary and the actual operating state.

[0037] The theoretical capability boundary is evaluated using the Fisher information matrix. The probability distribution function of the ultra-wideband measurement noise is expressed as follows:

[0038] ,

[0039] in, is the standard deviation of the UWB measurement noise, It is j The measurements of the UWB anchor points, yes k The position of the drone at the moment, yes kMoment j The measurement error of the UWB anchor point;

[0040] Taking the partial derivative of the log-likelihood function of the UWB measurement value with respect to the state, we get:

[0041] ,

[0042] in, is the measured value of UWB, the base of the log function is the natural logarithm e;

[0043] The Fisher information matrix of the ultra-wideband positioning system is calculated as follows:

[0044] ,

[0045] in, is the Jacobian matrix, Represents the expectation operator.

[0046] The actual operating state of the particle filter is represented by the number of effective particles. In particle filtering, the number of effective particles is a measure used to evaluate the quality and degree of degradation of particles. The effective number of particles in the particle filter is calculated as follows:

[0047] ,

[0048] in, is the normalized particle weight, satisfying The effective number of particles is used to measure the concentration of particles. When the effective number of particles is the largest, it is equal to the total number of particles. n , which means that the particles uniformly represent the target distribution. In contrast, a lower Indicates particle degradation, where only a few particles have significant weights and the sample set lacks diversity.

[0049] The Fisher information matrix and the number of effective particles are combined to calculate the credibility index of the ultra-wideband positioning system and the estimation algorithm. as follows:

[0050] ,

[0051] in, yes k The effective number of particles at time, n is the total number of particles, represents the minimum eigenvalue of the Fisher information matrix. Finally, the credibility of the ultra-wideband positioning system is determined as follows:

[0052] ,

[0053] in, is the set credibility threshold. If the credibility index is less than the threshold, it means that the ultra-wideband positioning system is degraded. The minimum eigenvalue The corresponding eigenvector represents the degradation direction.

[0054] The contents not described in detail in the specification of the present invention belong to the prior art known to the professional and technical personnel in the field. It is easy for the technical personnel in the field to understand that the above description is only the preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for anti-interference estimation and credibility evaluation of UAV ultra-wideband positioning, characterized in that: The following steps are involved: Step 1, establishing a nonlinear state augmented positioning system model including a UAV dynamics model, an ultra-wideband measurement model and a dynamic interference model; Step 2, design a composite particle filter algorithm containing an interference compensation sampling link to achieve simultaneous compensation and suppression of dynamic interference and ultra-wideband measurement noise; Step 3: Calculate the system's credibility index in real time based on the system's Fisher information matrix and the number of effective particles during actual operation, and complete the credibility evaluation of the positioning system and estimation algorithm; The step 1 comprises: The nonlinear state augmented positioning system model including the UAV dynamics model, the ultra-wideband measurement model and the dynamics interference model is as follows: （1） Among them, the augmented state , the augmented process noise vector , , , , is the identity matrix, is the UWB measurement, where is the system state that satisfies: ， Among them, the system status Contains the three-axis position and speed of the drone. is a nonlinear system model, is the input channel coefficient matrix of the dynamic disturbance, is a nonlinear measurement model, is the process noise that follows a Gaussian distribution , is the process noise variance, is the measurement noise that follows a Gaussian distribution , is the measurement noise variance, represents the UAV dynamic disturbance, is the state variable of the interference subsystem, satisfying: ， in, is the state matrix of the interference subsystem, is the frequency parameter of the dynamic disturbance, is the output matrix of the interference subsystem; is the process noise of the interference subsystem, satisfying ; is the variance of the process noise of the interference subsystem; Formula (1) is the nonlinear state augmented positioning system model including the UAV dynamics model, ultra-wideband measurement model and dynamic interference model; The step 2 comprises: For the sampling time , the resampled particle set is updated according to the system equation and the statistical characteristics of process noise, where The system state value of each particle for: ， in, is generated by sampling the process noise, satisfying , is the total number of particles; Furthermore, for each particle , design a disturbance observer to estimate the state of the dynamic disturbance subsystem; The state of the interference subsystem corresponding to each particle and its estimated error covariance The calculation is as follows: ， ， in: ， ； The step 3 is specifically implemented as follows: The probability distribution function of ultra-wideband measurement noise is expressed as follows: ， in, is the standard deviation of the UWB measurement noise, It is j The measurements of the UWB anchor points, yes k The position of the drone at the moment, yes k Moment j The measurement error of the UWB anchor point; Taking the partial derivative of the log-likelihood function of the UWB measurement value with respect to the state, we get: ， in, is the measurement value of UWB; The Fisher information matrix of the ultra-wideband positioning system is calculated as follows: ， in, is the Jacobian matrix, , represents the expectation operator; The effective number of particles in the particle filter is calculated as follows: ， in, is the normalized particle weight, satisfying ; The Fisher information matrix and the number of effective particles are combined to calculate the credibility index of the ultra-wideband positioning system and the estimation algorithm. as follows: ， in, yes k The effective number of particles at time, n is the total number of particles, represents the minimum eigenvalue of the Fisher information matrix; finally, the credibility of the ultra-wideband positioning system is determined as follows: ， in, is the set credibility threshold. If the credibility index is less than the threshold, it means that the ultra-wideband positioning system is degraded. The minimum eigenvalue The corresponding eigenvector represents the degradation direction.

Images