A Multi-Bernoulli Filter Distributed Fusion Method

Abstract

The invention discloses a multi-Bernoulli filter distributed fusion method, which includes using a multi-Bernoulli filter for each sensor node of the sensor network to realize local filtering, and receiving the multi-Bernoulli filter of its adjacent nodes through a communication link The posterior distribution, and then based on the generalized covariance intersection information entropy, realize the joint grouping of target tracks between sensors, and finally calculate the parameters of the multi-Bernoulli filter distributed fusion under each target track grouping, and obtain the fusion posterior Multi-Bernoulli distribution parameters. The invention can efficiently realize the multi-Bernoulli filter distributed fusion under the condition of a large number of targets, and has the advantages of small approximation error, low implementation complexity and the like.

Description

technical field [0001] The invention belongs to the field of multi-sensor fusion, and in particular relates to a multi-Bernoulli filter distributed fusion method. Background technique [0002] Single-sensor detection systems cannot achieve accurate and effective detection of time-varying multiple targets due to their single detection angle of view, single detection capability, and complex and diverse detection environments. A sensor network detection system based on the multi-directional scattering mechanism is urgently needed to Meet the requirements of stable and continuous detection tasks for multiple targets in complex detection scenarios. On the other hand, with the improvement of computing, storage, and communication capabilities of miniaturized detectors, the multi-sensor network system constructed by this type of detector is favored, and the system provides a platform for collecting diverse target information for multi-target detection. [0003] Multi-sensor and mul...

AI Technical Summary

Problems solved by technology

However, when the multi-Bernoulli filter-based GCI fusion analytical form mathematical expression realizes the fusion of multi-objective posterior distributions, there are disadvantages such as high cost of computing resources and complex fusion structure, which makes the unoptimized distributed fusion algorithm unable to meet Distributed sensor networks require high real-time processing of local node information and short delays in information transmission between nodes. Therefore, there is an urgent need for an unoptimized distributed fusion algorithm with robust performance, high efficiency, and an efficient implementation method with a simple fusion structure. , to approach or meet the actual needs of distributed real-time processing, is the key to apply the distributed multi-Bernoulli filter fusion technology to a wider range of practical application scenarios

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