Tracking a Maneuvering Object by Indirect Observations with Random Delays

Abstract

A mathematical model for the target tracking problem is proposed. The model makes it possible to describe conditions when the time for an observer to receive the results of indirect observations of a moving object depends not only on the state of the observation environment but also on the state of the object itself. The source of such a model is the observation process, by stationary means, of an autonomous underwater vehicle, in which the time for obtaining up-to-date data depends on the unknown distance between the object and the observer. As part of the study of the problem, the equations of the optimal Bayesian filter are obtained. But this filter is not possible to implement. For practical purposes, it is proposed to use the conditionally minimax nonlinear filter, which has shown promising results in other complex tracking models. The conditions for the filter’s evaluation and its accuracy characteristics are given. A large-scale numerical experiment illustrating the filter’s operation and the observation system’s features with random delays are described.

Impact Analysis of Time Synchronization Error in Airborne Target Tracking Using a Heterogeneous Sensor Network

“…A time synchronization protocol is typically established to meet a specified precision level, while target tracking systems are developed assuming that the time information is sufficiently accurate. Various filtering schemes aimed at improving state estimation in the presence of delayed measurements have been proposed [22][23][24], primarily focusing on state estimation within the target tracking system. In a target tracking system employing multiple sensors, data association and state estimation within the sensor fusion rely on temporal information from local sensors.…”