Bayesian theory-based multi-sensor detecting and tracking combined processing method

Abstract

The invention discloses a Bayesian theory-based multi-sensor detecting and tracking combined processing method, mainly used for solving the problem that the traditional sensor fusion system has poor performance. The implementation process of the method comprises the following steps of: 1, setting a motion model of a target; 2, setting an observation model of the target; initializing all sensors for predicting probability distribution; 4, calculating posterior probability distribution of the target in a combined state by each sensor according to respective observation and transmitting the posterior probability distribution to a fusion center; 5, performing fusion by the fusion center to obtain a posterior probability of the existence of the target after fusion; 6, detecting whether the target exists according to a set detection threshold; 7, performing fusion by the fusion center to obtain a posterior probability of the motion state of the target after fusion; 8, forecasting the combined state of the target by every sensor; and 9, repeating the steps from the step 4 to the step 8 to detect and track the target continuously. The Bayesian theory-based multi-sensor detecting and tracking combined processing method has the advantage of good detection performance and can be used for detecting and tracking the target on the basis of observation data.

Description

technical field [0001] The invention belongs to the technical field of radar, relates to a target detection and tracking method, and can be used for detection and tracking processing of a target with a small signal-to-noise ratio. Background technique [0002] For multi-sensor fusion, detection fusion and target state estimation fusion are often two separate processes. [0003] The multi-sensor detection fusion system performs fusion processing on the observation data of each sensor or the judgment of each sensor, so as to make the detection performance of the fusion system better. The multi-sensor detection fusion system consists of a fusion center and multiple sensors. The fusion methods of the fusion system can be divided into two types: centralized and distributed. In the centralized fusion mode, each sensor directly transmits its observation data to the fusion center, and the fusion center conducts hypothesis testing based on the observation data of all sensors to form...

AI Technical Summary

Problems solved by technology

[0005] Compared with the single sensor, although the detection and tracking performance of the above-mentioned multi-sensor detection fusion system and multi-sensor target state fusion system have been improved, they do not fully utilize the information provided by the observation data, thus limiting their performance. improvement

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