Self-adaptive sensor management method for multi-sensor multi-target tracking

Abstract

The invention discloses a self-adaptive sensor management method for multi-sensor multi-target tracking. The method comprises the following steps of S100, according to sensor network bandwidth limitation and tracking processing real-time requirements, the number of sensors selected at each moment being stipulated to be P; S200, modeling sensor management as a partially observable Markov decision model; S300, estimating the amount of information which can be acquired by each sensor in the sensor network by using the partially observable Markov decision model; S400, P sensors with the most information amount being selected from all the sensors; S500, sorting the selected P sensors according to the sequence of the information amount from small to large; and S600, a control instruction being sent to the sensors, the selected P sensors being activated, target measurement being acquired, and target number and state estimation being realized by using a generalized label multi-Bernoulli filter.

Description

technical field [0001] The disclosure belongs to the technical field of sensor data processing, and in particular relates to an adaptive sensor management method for multi-sensor and multi-target tracking. Background technique [0002] Sensor networks composed of mobile robots or static sensing nodes have received extensive attention in the fields of monitoring and scene analysis. Multi-sensor multi-target tracking is one of the most critical low-level technologies. Its difficulties mainly include two aspects: on the one hand, there is uncertainty in the data association between measurement and target, and multi-sensor data fusion is challenging; on the other hand, The system needs to implement sensor management to obtain high-quality object measurements and reduce the energy consumption of the sensor network. In highly complex multi-objective systems, sensor management is essentially an optimal nonlinear stochastic control problem, to which standard optimal control techniq...

AI Technical Summary

Problems solved by technology

Multi-sensor multi-target tracking is one of the most critical low-level technologies. Its difficulties mainly include two aspects: on the one hand, there is uncertainty in the data association between measurement and target, and multi-sensor data fusion is challenging; on the other hand, The system needs to implement sensor management to obtain high-quality target measurements and reduce the energy consumption of the sensor network

In highly complex multi-objective systems, sensor management is essentially an optimal nonlinear stochastic control problem, for which standard optimal control techniques cannot be directly applied

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