Information-Seeking Sensor Selection for Ocean-of-Things

Abstract

We propose a general sensor selection (SS) methodology for ocean-of-things (OoT) where a sensing network performs multiobject tracking (MOT) under resource constraints. SS methods address the combinatorial problem of determining the best subset of sensors that maximizes a suitable reward function for a fixed cardinality. The novelty of this article is twofold. First, we propose a tractable information-theoretic reward function for MOT-OoT with an unknown and time-varying number of objects such as ocean vessels. A tractable reward function is essential in order to rapidly evaluate a sensor subset, which is crucial in the high-dimensional problems encountered in OoT. Second, we propose a general cross-entropy SS (CE-SS) methodology that efficiently estimates the probabilities of sensor activations and determines the optimal sensor subset according to the proposed reward function and under the imposed cardinality constraint. The CE-SS algorithm avoids exhaustive searching over the space of all sensor subsets, which is intractable for most OoT applications. The CE-SS methodology, coupled with the proposed reward function, is capable of selecting sensors that lead to more accurate estimates than random selection for both the number of vessels and their trajectories. We demonstrate the effectiveness of our method via numerical simulation in serveral scenarios, including multivessel tracking for OoT with an emulated network of acoustic sensors deployed off the coast of Italy.