Today I read a paper titled “Stochastic Surveillance Strategies for Spatial Quickest Detection”
The abstract is:
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