An Efficient Implementation of the Multiple-Model Generalized Labeled Multi-Bernoulli Filter for Track-Before-Detect of Point Targets Using an Image Sensor

• Published in: IEEE transactions on aerospace and electronic systems 2021-12, Vol.57 (6), p.4416-4432
• Main Authors: Cao, Chenghu, Zhao, Yongbo
• Format: Article
• Language: English
• Subjects: Computational modeling; Convergence; Lattice-reduction-aided Gibbs sampler with flexible proposal; Maneuvering targets; Markov processes; multiple-model generalized labeled multi-Bernoulli; Noise prediction; Radar tracking; Radio frequency; Sampling; Signal to noise ratio; Target detection; Target tracking; track-before-detect model; Tracking; tracking multiple maneuvering targets with low signal-to-noise rate (SNR); Trajectory
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2021.3091756

In this article, an efficient implementation of the multiple-model generalized labeled multi-Bernoulli filter based on track-before-detect (TBD) measurement model, called as MM-GLMB-TBD filter, is presented for tracking maneuvering targets with low signal-to-noise rate (SNR) by integrating the prediction and update into a single step. Based on Gibbs sampling solution to truncating the GLMB densities, the convergence behavior is taken into consideration to reduce computational burden of MM-GLMB-TBD filter. In this article, the lattice-reduction Gibbs sampling with flexible proposal is presented to effectively truncate the filtering densities in the MM-GLMB-TBD filter with geometric ergodicity and better exponential convergence rate. The simulation results demonstrate that the proposed method is particularly suitable for multiple weak targets tracking solution based TBD measurement model due to faster convergence rate. Finally, it is verified from the results that the proposed method is highly robust to variance in different low SNRs.