Stochastic Surveillance Strategies for Spatial Quickest Detection

TL;DR

This paper develops stochastic routing and detection algorithms for autonomous vehicles to efficiently identify anomalies in an environment, balancing detection speed and false alarms, with adaptive strategies outperforming stationary ones.

Contribution

It introduces combined anomaly detection and adaptive vehicle routing algorithms for persistent surveillance, enhancing detection efficiency in noisy environments.

Findings

Adaptive routing improves detection speed over stationary policies.

Proposed algorithms effectively balance false alarms and detection delay.

Numerical simulations validate the effectiveness of the strategies.

Abstract

We design persistent surveillance strategies for the quickest detection of anomalies taking place in an environment of interest. From a set of predefined regions in the environment, a team of autonomous vehicles collects noisy observations, which a control center processes. The overall objective is to minimize detection delay while maintaining the false alarm rate below a desired threshold. We present joint (i) anomaly detection algorithms for the control center and (ii) vehicle routing policies. For the control center, we propose parallel cumulative sum (CUSUM) algorithms (one for each region) to detect anomalies from noisy observations. For the vehicles, we propose a stochastic routing policy, in which the regions to be visited are chosen according to a probability vector. We study stationary routing policy (the probability vector is constant) as well as adaptive routing policies (the probability vector varies in time as a function of the likelihood of regional anomalies). In the context of stationary policies, we design a performance metric and minimize it to design an efficient stationary routing policy. Our adaptive policy improves upon the stationary counterpart by adaptively increasing the selection probability of regions with high likelihood of anomaly. Finally, we show the effectiveness of the proposed algorithms through numerical simulations and a persistent surveillance experiment.