Sensor Bias Estimation for Track-to-Track Association
| Autor: | Aybars Tokta, Ali Koksal Hocaoglu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Estimation
Computer science Heuristic (computer science) Applied Mathematics Association (object-oriented programming) Track (disk drive) 020206 networking & telecommunications 02 engineering and technology Function (mathematics) Sensor fusion Signal Processing 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering CMA-ES Algorithm Rigid transformation |
| Zdroj: | IEEE Signal Processing Letters. 26:1426-1430 |
| ISSN: | 1558-2361 1070-9908 |
| DOI: | 10.1109/lsp.2019.2934596 |
| Popis: | In this letter, we propose a heuristic method to address sensor bias estimation to improve track-to-track association accuracy. A novel multi-parameter cost function is derived from rigid transformation function and it is minimized by the covariance matrix adaptation evolution strategies algorithm. The proposed method is compared to other recognized methods under various simulation scenarios. The comparison results confirm that our approach accurately estimates sensor biases, provides higher correct association probability with low computational load compared to the competitor methods, and also it is robust to high missed and false track rates. |
| Databáze: | OpenAIRE |
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