Extended Target Tracking Method with Partially Overlapping Multi-sensor Presence Monitoring Areas

Abstract

In the extended target tracking method disclosed by the present invention, the multi-sensor existence monitoring area partially overlaps, the Gaussian component used when filtering the current measurement set is the Gaussian component predicted at the current moment, not the Gaussian component after any sensor measurement update, The results of each sensor filter update are independent of each other; when each sensor measurement is involved in filter update, the result Gaussian items of all sensors are judged: if the component of the Gaussian item estimated by the sensor that represents the target position falls in the overlapping area, The weights are averaged and optimized; finally, the weights, mean and covariance of the Gaussian items estimated by all sensors are combined into a Gaussian component as the predicted Gaussian component at the next moment. The extended target tracking method in which the multi-sensor presence monitoring areas of the present invention partially overlap eliminates the problem of overestimation of the number of targets caused by overlapping areas and the problem of serious loss of measurement information caused by non-overlapping areas.

Description

technical field [0001] The invention belongs to the technical field of target tracking and target positioning methods, and relates to a target tracking method in a multi-sensor environment, in particular to an extended target tracking method in which multi-sensor presence monitoring areas partially overlap. Background technique [0002] With the continuous improvement of the accuracy of modern sensors, the sensor not only receives one measurement when monitoring a single target, but more often multiple measurements generated by one target can be monitored, which can generate more than one An object that measures information is called an extended object. [0003] The extended target tracking algorithm under multi-sensor conditions aims to select the appropriate algorithm to fuse the measurement information received by each sensor. After the fused information is filtered, it can more accurately reflect the real state of the group target. In the process of fusion, multiple sen...

AI Technical Summary

Problems solved by technology

The multi-sensor fusion algorithm disclosed above can be directly applied to the extended target tracking process. However, the existing multi-sensor fusion algorithms all assume that the sensor monitoring areas do not overlap, that is, each sensor has its own independent monitoring area , do not interfere with each other

In an actual multi-sensor environment, the monitoring areas of the sensors often overlap with different situations.

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