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Maximum correntropy volume Kalman filtering method based on statistical linear regression

A technology of maximum correlation entropy and Kalman filtering, which is applied in the field of signal processing, can solve problems affecting the anti-noise ability of the volumetric Kalman filter, and achieve the effect of improving Lupine and anti-noise ability

Inactive Publication Date: 2017-03-08
SOUTHWEST UNIVERSITY
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AI Technical Summary

Problems solved by technology

However, the volumetric Kalman filter derived from the Bayesian filtering framework, in order to apply the numerical integration method, it is necessary to assume that the noise term is a Gaussian distribution, which seriously affects the anti-noise ability of the volumetric Kalman filter in practical applications

Method used

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  • Maximum correntropy volume Kalman filtering method based on statistical linear regression
  • Maximum correntropy volume Kalman filtering method based on statistical linear regression
  • Maximum correntropy volume Kalman filtering method based on statistical linear regression

Examples

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Embodiment 1

[0089] Such as Figure 1-3 As shown, the above Kalman filter method is used to track the turning of the maneuvering target, and the discrete state space model is as follows:

[0090]

[0091]

[0092] in x k and y k is the location, and Represents the velocity in the x and y directions, r k and θ k respectively represent the distance and angle measured by the radar, and the turning speed ω k The simulation is set to:

[0093]

[0094] Other parameters in the simulation are set to T=1, (s x ,s y )=(0,0). v k and w k is Gaussian noise with mean 0 and covariance matrix Q and R, where

[0095]

[0096] and

[0097] According to the above-mentioned maximum correlation entropy volumetric Kalman filtering method based on statistical linear regression, the filtering algorithm is first initialized. The actual initial value of the state and the initial value of the covariance matrix P 0 respectively set to P 0 =diag([100,10,100,10,1×10 -4 ]), the...

Embodiment 2

[0099] Replace the simulation model with a univariate non-stationary growth model. The state space model is as follows:

[0100]

[0101]

[0102] where q k =1. According to the above-mentioned maximum correlation entropy volumetric Kalman filter method based on statistical linear regression and then simulated, we can get Figure 4 and 5 . The initial value and variance of the state are set as P 0 =1. Compared with Example 1, Example 2 changes the noise r in the model k statistical properties. exist Figure 4 In the simulation of , a new noise item is added on the basis of the noise of the original model with a probability of 0.5 σ r =1. in the same way in Figure 5 The noise term added in the simulation is From Figure 4 and 5 It can be seen that, with the increase of the non-Gaussian degree of the mixed Gaussian noise, the performance of the algorithm is more and more improved than that of CKF.

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Abstract

The invention belongs to the technical field of signal processing, and providing a maximum correntropy volume Kalman filtering method based on statistical linear regression. After steps of filter initializing, time predicting, fixed-point iterating, error covariance matrix updating and the like are performed, the state of a nonlinear system can be estimated. Based on a maximum correntropy Kalman filtering device, a maximum correntropy cost function is integrated into the frame of a volume Kalman filtering device by utilizing a statistic linear regression method in the statistics, and then is applied to estimation to the state of the nonlinear system. Compared with the traditional volume Kalman filtering method in a nonlinear system state estimation process, the maximum correntropy volume Kalman filtering method based on statistical linear regression has remarkable improvement on the robustness and anti-noise capability; and the method can be widely applied to the nonlinear system, in particular when a noise item of the system is non-gaussian.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a maximum correlation entropy volumetric Kalman filtering method based on statistical linear regression. Background technique [0002] System state estimation is an important problem in signal processing. The Kalman filter method is one of the main methods to solve the state estimation of the linear system. It makes full use of the state space model and observation data of the system. By solving the optimization problem, the error of the state estimation is minimized, so as to obtain the optimal state of the system. estimate. Nonlinear Kalman filters include unscented Kalman filter, volumetric Kalman filter, and sparse grid integrator filter, all of which are effective tools for state estimation of nonlinear systems. [0003] The state space model is: [0004] [0005] in f(·), h(·) represent the state vector, observation vector, state transition function and ob...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03H17/02
CPCH03H17/0202H03H2017/0205
Inventor 王世元尹超钱国兵冯亚丽段书凯王丽丹
Owner SOUTHWEST UNIVERSITY
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