Linear TDOA-based Measurements for Distributed Estimation and Localized Tracking

TL;DR

This paper introduces a linear TDOA measurement model for distributed target tracking, enabling reduced communication and enhanced fault tolerance in sensor networks through consensus-based filtering.

Contribution

It presents a novel linear TDOA model and distributed filtering protocols that improve tracking efficiency and robustness in large-scale, sparse sensor networks.

Findings

Reduces communication load in sensor networks

Maintains tracking performance under link/node failures

Provides minimal conditions for network observability

Abstract

We propose a linear time-difference-of-arrival (TDOA) measurement model to improve \textit{distributed} estimation performance for localized target tracking. We design distributed filters over sparse (possibly large-scale) communication networks using consensus-based data-fusion techniques. The proposed distributed and localized tracking protocols considerably reduce the sensor network's required connectivity and communication rate. We, further, consider $\kappa$-redundant observability and fault-tolerant design in case of losing communication links or sensor nodes. We present the minimal conditions on the remaining sensor network (after link/node removal) such that the distributed observability is still preserved and, thus, the sensor network can track the (single) maneuvering target. The motivation is to reduce the communication load versus the processing load, as the computational units are, in general, less costly than the communication devices. We evaluate the tracking performance via simulations in MATLAB.