Track estimation with binary derivative observations

TL;DR

This paper introduces methods for estimating target trajectories using binary derivative sensor data, including a max-likelihood approach and a new tracking algorithm for binary sensor networks that estimates position and velocity.

Contribution

It proposes a novel max-likelihood estimation technique leveraging binary derivative observations and introduces an innovative target tracking algorithm for binary sensor networks.

Findings

Max-likelihood estimation improves trajectory accuracy using binary derivative data.

The new tracking algorithm effectively estimates position and velocity in binary sensor networks.

Position correction and velocity analysis significantly enhance tracking performance.

Abstract

We focus in this paper in the estimation of a target trajectory defined by whether a time constant parameter in a simple stochastic process or a random walk with binary observations. The binary observation comes from binary derivative sensors, that is, the target is getting closer or moving away. Such a binary observation has a time property that will be used to ensure the quality of a max-likelihood estimation, through single index model or classification for the constant velocity movement. In the second part of this paper we present a new algorithm for target tracking within a binary sensor network when the target trajectory is assumed to be modelled by a random walk. For a given target, this algorithm provides an estimation of its velocity and its position. The greatest improvements are made through a position correction and velocity analysis.