Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques
Document Type
Conference Proceeding
Publication Title
IEEE Aerospace Conference Proceedings
Abstract
This paper presents two different methods for track-to-track fusion of drone tracks. The sensors are unbiased radars with fixed locations. The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn the online adjustment of the fusion weights between the two tracks. An objective function utilizing the covariance of the fused tracks is used by the first algorithm while a cost function based on the Kullback-Leibler (KL) divergence measure is used in the second case for training the LSTM. The two methods are compared with other baseline methods using performance metrics such as SIAP and OSPA. Simulations are done for a single object (drone) and repeated for multiple objects in the presence of two radars to demonstrate the validity of the two proposed techniques. The JPDA (Joint Probability Data Association) with fixed gating and moderate clutter is used in the case of multiple objects. Stone Soup was chosen as the radar simulation environment.
DOI
10.1109/AERO58975.2024.10521258
Publication Date
1-1-2024
Recommended Citation
S. Fares et al., "Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques," IEEE Aerospace Conference Proceedings, Jan 2024.
The definitive version is available at https://doi.org/10.1109/AERO58975.2024.10521258