A Bayesian Filter Target Tracking Algorithm

Abstract

The invention discloses a Bayesian filtering target tracking algorithm. The first step of the method of the invention is to obtain the one-step prediction and estimation of the target state at the next time from the optimal estimation of the target state at time k-1 through a motion model; the second step is to After the observation value of the target at time k is obtained by the radar observation station, the distance information and angle information of the target relative to the radar are converted into the Cartesian coordinate position information of the target by using the random variable fixed-point sampling non-linear transformation method; the third step is to convert the target state One-step prediction of prior information and radar observation reverse estimation likelihood function two parts of information, through the product fusion of the probability likelihood product rule of the present invention, finally obtain the posterior estimation of the target state at k time, store the target state and update the time and enter the next step One round of iterations, the present invention has the characteristics of higher precision, better robustness, and more concise algorithm structure, and has high practical value in radar, multi-sensor, maneuvering and multi-target tracking.

Description

A Bayesian Filter Target Tracking Algorithm technical field [0001] The present invention relates to a Bayesian filtering target tracking algorithm, which belongs to the field of target tracking. Background technique Target tracking has a wide range of applications in both military and civilian fields, such as aerial surveillance, satellite and spacecraft tracking to And intelligent transportation and video surveillance. The target tracking problem is essentially a state estimation problem, the core of which is the filtering algorithm. [0003] According to the difference of the dynamic system space model, the filtering problem can be divided into linear filtering and nonlinear filtering. last life In the 1970s, the Kalman filter was successfully applied to the field of target tracking, as the most classic line in the field of target tracking. In the case of linear Gaussian, the filtering result of Kalman filter is under the criteria of minimum variance and maximum li...

AI Technical Summary

Problems solved by technology

Facing the problem of nonlinear filtering, a large number of scholars and experts have proposed many effective nonlinear filtering algorithms in the past 30 years, the most famous ones are extended Kalman filter (EKF), unscented Kalman filter (Unscented Kalman filter). filter, UKF), particle filter (Particle filter, PF), but they all have some problems, such as the extended Kalman filter algorithm has low linearization accuracy and needs to calculate the complex Jacobian matrix; the general unscented Kalman filter algorithm exists Complicated calculations, filtering divergence and even distortion problems, particle filtering has problems of large calculation and weight degradation

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