Novel Bayesian Track-Before-Detection for Drones Based VB-Multi-Bernoulli Filter and a GIGM Implementation

Abstract

Joint detection and tracking of drones is a challenging radar technology; especially estimating their states with unknown measurement variances. The Bayesian track-before-detect (TBD) approach is an efficient way to detect low observable targets. In this paper, we proposed a new variational Bayesian (VB)-TBD technique for drones based on Multi-Bernoulli filter, which implemented with unknown measurement variances. Current implementation includes an analytical Gaussian inverse Gamma mixtures solution, which applied to estimate augmented kinematic drones state under the same circumstance. The results demonstrate that the proposed filter is more accurate than other Multi-Bernoulli filters in cardinality estimation. The proposed algorithm estimates the fluctuated parameters for each drone and it has no difficulty in handling the crossing of multiple drones. The Optimal Subpattern Assignment (OSPA) distances of proposed algorithm under different SNR is less than the other filters. It can be seen that at SNR (-5dB), the proposed algorithm and the other filters settle to errors 51m, 125m and 200m, respectively.