Target tracking method of passive multi-sensor based on layered particle filtering

Abstract

The invention discloses a target tracking method of a passive multi-sensor based on layered particle filtering. A sub-state including an azimuth angle, a change ratio of the azimuth angle, a change ratio of a logarithm radial distance and auxiliary parameters and a sub-state Psi including the logarithm radial distance are constructed to realize a structure of hierarchical filtering through rewriting a system equation of a logarithm polar coordinate and adding the auxiliary parameters indicative of process noise intensity and radial distance ratio, wherein a first layer updates the auxiliary parameters in a second layer by means of sequential importance sampling method according to observation information of various sensors; Psi is iterated and updated; and the auxiliary parameters are combined to obtain estimation of the target state; finally a fusion output result of the target state is obtained according to an optimal information fusion method. Values of the auxiliary parameters can be estimated in real time by using a method of the layered particle filtering, and errors introduced by a maximum value of the noise intensity in the use process of the filtering is avoided, such that the problem of the target tracking can be effectively solved under the conditions of unknown process noise intensity and unmeasured distance.

Description

technical field [0001] The invention belongs to the technical field of guidance and relates to target tracking. Specifically, it is a passive multi-sensor target tracking method based on layered particle filter, which can be used in systems such as infrared guidance and information fusion operations. Background technique [0002] Passive multi-sensor target tracking is essentially a nonlinear tracking problem, and the application of nonlinear filtering to passive multi-sensor target tracking has become a current research hotspot. The most typical representatives of nonlinear filtering are Extended Kalman Filter (EKF) and Inorganic Kalman Filter (UKF). Due to the high degree of nonlinearity in passive sensor target tracking, EKF needs to be linearized, which will increase the system error and cause the filter to be unstable or even diverge. UKF uses a limited number of deterministic sample points to approximate the probability distribution of the state, transfers the sample...

AI Technical Summary

Problems solved by technology

Recently, some scholars have proposed quadrature Kalman filter (QKF) and volumetric Kalman filter (CKF) methods with better performance, but these methods are limited by the Gaussian assumption and are only suitable for occasions with low nonlinearity. Restricts its application under the condition of high tracking accuracy

[0003] Particle filter has excellent performance in solving nonlinear and non-Gaussian problems. Its essence is to use some weighted particles to represent the posterior probability distribution of the state. It has been widely used in signal processing, automation and other fields, but in some cases Divergence still exists. Although scholars have improved its performance in passive multi-sensor target tracking to a certain extent by optimizing the proposed density distribution function and resampling, they cannot fundamentally solve the problem of divergence.

Mainly due to the following two reasons: (1) The process noise intensity is unknown

In practice, it is difficult to obtain the noise intensity when the target is moving. The filtering process is usually replaced by the maximum value. When the deviation from the true value is large, the filter will naturally cause divergence; (2) The radial distance is not measurable

Since the passive sensor can only obtain the angle information of the target, and cannot observe the distance information, the same observation angle may correspond to different target states. This situation is not considered when using particle approximation, resulting in performance degradation.

Images