Linear TDOA-based Measurements for Distributed Estimation and Localized Tracking

Abstract

We propose a linear time-difference-of-arrival (TDOA) measurement model to improve 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 κ-redundant observability and faulttolerant 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.

Consensus-based Networked Tracking in Presence of Heterogeneous Time-Delays

“…Due to these drawbacks, the linearized distributed tracking is not very accurate. What we suggested [4], [5], is a target-independent 1 and time-invariant linear measurement matrix (as in general LTI setups) which is compatible with most existing distributed filtering schemes. What is new in this paper is to take into account possible time delays in information exchange between every two sensors which makes the networked filtering scenario even more challenging.…”

“…Fig. 2: A comparison on the Kalman Filtering performance between our linear measurement model ( 5)-( 7) and the (linearized) nonlinear model ( 3)-( 4) [5]. For the latter case, instead of the exact target position p (which is unknown), its estimated value p i is needed in ( 3)-( 4).…”

“…This research area has recently gained attention in filtering and estimation over networks and graph signal processing with different applications from system monitoring [2] to target tracking and localization [3]. In this paper, we consider distributed TDOA-based tracking with linear measurements that is shown to outperform the nonlinear counterparts in distributed scenarios, see details in [4], [5]. Motivated by this, we further extended those research papers to address possible time delays in the data transmission networks among sensors.…”