Designing of current-statistical-model-based probability hypothesis density particle filter and filter

Abstract

The invention discloses the designing of current-statistical-model-based probability hypothesis density particle filter and the current-statistical-model-based probability hypothesis density particle filter. An observed value of the filter is connected with the first input end of an updating circuit. The first input end of a prediction circuit is connected with the first output end of a state estimation circuit, and the output end of the prediction circuit is connected with the second input end of the updating circuit. The output end of the updating circuit is connected with the input end of the resampling circuit. The first output end of the resampling circuit is connected with the second input end of the prediction circuit, and the second output end of the resampling circuit is connected with the input end of the state estimation circuit. By the invention, a hardware circuit realization scheme for the current-statistical-model-based probability hypothesis density particle filter is designed based on the theory of the current-statistical-model-based probability hypothesis density particle filter, and simulation results show that the tracking performance of the designing of the current-statistical-model-based probability hypothesis density particle filter and the current-statistical-model-based probability hypothesis density particle filter is similar to that of theoretical analysis and can be used for tracking problems about maneuvering multi-target movement in a clutter environment.

Description

technical field [0001] The invention relates to a design method and a hardware circuit of a particle filter, in particular to a design of a probability hypothesis density particle filter of a current statistical model and the filter. Background technique [0002] Maneuvering target tracking refers to the use of target measurement information obtained by measuring equipment, through the establishment of a reasonable and accurate target motion model, and the use of modern signal processing technologies such as stochastic processes, estimation and detection theory, and filtering algorithms, to track the motion state (position, speed, etc.) , acceleration, etc.) for estimation and detection. After decades of research and development, mobile target tracking has been widely used in the military and national economy, such as military precision guidance, anti-ballistic missile defense, satellite reconnaissance, etc. control, robot positioning, car collision avoidance and navigation...

AI Technical Summary

Problems solved by technology

However, the particle filter framework does not include a data association mechanism. When multiple targets are tracked, the number of targets changes or the targets are occluded from each other, the tracking target will be lost.

In addition, the interference between multiple targets will also affect the accuracy of tracking

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