Logo image
Back
Sensor Assignment Algorithms to Improve Observability While Tracking Targets
Journal article   Open access   Peer reviewed

Sensor Assignment Algorithms to Improve Observability While Tracking Targets

Lifeng Zhou and Pratap Tokekar
IEEE transactions on robotics, v 35(5), pp 1206-1219
Oct 2019
DOI: https://doi.org/10.1109/TRO.2019.2920749
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1109/TRO.2019.2920749View
Published, Version of Record (VoR) Restricted

Abstract

Approximation algorithms Approximation assignment algorithms Greedy algorithms nonlinear observability measures Observability planning Robot sensing systems scheduling and coordination sensor-based control Symmetric matrices Target tracking
In this paper, we study two sensor assignment problems for multitarget tracking with the goal of improving the observability of the underlying estimator. We consider various measures of the observability matrix as the assignment value function. We first study the general version where the sensors must form teams to track individual targets. If the value function is monotonically increasing and submodular, then a greedy algorithm yields a 1/2-approximation. We then study a restricted version where exactly two sensors must be assigned to each target. We present a 1/3-approximation algorithm for this problem, which holds for arbitrary value functions (not necessarily submodular or monotone). In addition to approximation algorithms, we also present various properties of observability measures. We show that the inverse of the condition number of the observability matrix is neither monotone nor submodular, but present other measures that are. Specifically, we show that the trace and rank of the symmetric observability matrix are monotone and submodular and the log determinant of the symmetric observability matrix is monotone and submodular when the matrix is nonsingular. If the target's motion model is not known, the inverse cannot be computed exactly. Instead, we present a lower bound for distance sensors. In addition to theoretical results, we evaluate our results empirically through simulations.

Metrics

5 Record Views
25 citations in Web of Science
29 citations in Scopus

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#11 Sustainable Cities and Communities

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Web of Science research areas
Robotics
Logo image