期刊
ISA TRANSACTIONS
卷 114, 期 -, 页码 277-290出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2020.12.042
关键词
Cubature Kalman Multi-Bernoulli Filter; Micro-drones tracking; Approximated variational Bayesian; TBD
A novel approach using Cubature Kalman and Multi-Bernoulli filters, combined with variational Bayesian-TBD to estimate measurement variances, is proposed to address the detection and tracking of micro-drones. Simulation results confirm the effectiveness and robustness of the algorithm in estimating the movement state of micro-drones.
The problem of nonlinear tracking and detection of small unmanned aerial vehicles and micro-drone targets is very challenging and has received great attention recently. Recently, the Cubature Kalman-multi-Bernoulli filter which employs a third-degree spherical-radical cubature rule has been presented to handle the nonlinear models. The Cubature Kalman filter is more principled and accurate in mathematical terms. In addition, a recent multi-Bernoulli filter based on variational Bayesian approximation has been presented with the estimation the fluctuation of variances of measurement. However, Cubature Kalman and variational Bayesian-Multi-Bernoulli filters are unsuitable for tracking the micro-drones because of the unknown probability of detection. As we known, the track-before-detect (TBD) schemes was an effective method for tracking the small objects. In this work, a novel robust Cubature Kalman-Multi-Bernoulli filter with variational Bayesian-TBD is proposed jointing with estimate the fluctuated variances of measurement. The improved filter is an effective method to solve the problem of detection profile estimation for micro-drones. A novel implementation with a non-linear Cubature Kalman Gaussian mixture and Inverse Gamma approximation is presented to estimate a hybrid kinematic state of micro-drones. The simulation results confirm the effectiveness and robustness of the proposed algorithm. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据