Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-tagged Objects

TL;DR

This paper introduces an online UAV path planning algorithm for joint detection and tracking of multiple radio-tagged objects, effectively handling noisy signals and dynamic object behaviors in real-time monitoring scenarios.

Contribution

It develops a partially observable Markov decision process with a novel multi-object TBD filter that accounts for maneuvering objects and maintains safe distances.

Findings

Outperforms detection-based methods in low SNR environments.

Handles multiple objects with birth, death, and mode switching.

Efficient multi-object likelihood function for real-time processing.

Abstract

We consider the problem of online path planning for joint detection and tracking of multiple unknown radio-tagged objects. This is a necessary task for gathering spatio-temporal information using UAVs with on-board sensors in a range of monitoring applications. In this paper, we propose an online path planning algorithm with joint detection and tracking because signal measurements from these objects are inherently noisy. We derive a partially observable Markov decision process with a random finite set track-before-detect (TBD) multi-object filter, which also maintains a safe distance between the UAV and the objects of interest using a void probability constraint. We show that, in practice, the multi-object likelihood function of raw signals received by the UAV in the time-frequency domain is separable and results in a numerically efficient multi-object TBD filter. We derive a TBD filter with a jump Markov system to accommodate maneuvering objects capable of switching between different dynamic modes. Our evaluations demonstrate the capability of the proposed approach to handle multiple radio-tagged objects subject to birth, death, and motion modes. Moreover, this online planning method with the TBD-based filter outperforms its detection-based counterparts in detection and tracking, especially in low signal-to-noise ratio environments.