限られた視野を持つ移動センサによる未知数目標の探索・追跡のためのGM-PHDフィルタ
GM-PHD Filter for Searching and Tracking an Unknown Number of Targets with a Mobile Sensor with Limited FOV
視野の限られたロボットで、数が未知の移動目標を探索・追跡するため、ベイズ有限集合に基づくGM-PHDフィルタを拡張し、ガウス過程回帰で非線形軌道を予測する手法を提案した。
著者: Yoonchang Sung, Pratap Tokekar
分類: cs.RO
原文アブストラクト
We study the problem of searching for and tracking a collection of moving targets using a robot with a limited Field-Of-View (FOV) sensor. The actual number of targets present in the environment is not known a priori. We propose a search and tracking framework based on the concept of Bayesian Random Finite Sets (RFSs). Specifically, we generalize the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter which was previously applied for tracking problems to allow for simultaneous search and tracking with a limited FOV sensor. The proposed framework can extract individual target tracks as well as estimate the number and the spatial density of targets. We also show how to use the Gaussian Process (GP) regression to extract and predict non-linear target trajectories in this framework. We demonstrate the efficacy of our techniques through representative simulations and a real data collected from an aerial robot.
