Nature-inspired waveform optimisation for range spread target detection in cognitive radar
| Autor: | Qing Wang, Meng Li, Lirong Gao, Kaiming Li, Hua Chen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
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kalman filters object detection particle swarm optimisation probability radar detection antenna arrays optimisation semidefinite relaxation technique novel nature-inspired waveform optimisation framework range-spread target detection cognitive radar waveform optimisation problem target scattering coefficients nature-inspired algorithms modified particle swarm optimisation algorithm beetle antennae search algorithm nature-inspired waveform optimisation approach range spread target detection Engineering (General). Civil engineering (General) TA1-2040 |
| Zdroj: | The Journal of Engineering (2019) |
| Druh dokumentu: | article |
| ISSN: | 2051-3305 |
| DOI: | 10.1049/joe.2019.0527 |
| Popis: | The waveform optimisation problem in cognitive radar is non-convex and will have sub-optimal solutions when solved by the semi-definite relaxation (SDR) technique. Here, a novel nature-inspired waveform optimisation framework is proposed for range-spread target detection in cognitive radar. First, the waveform optimisation problem is formulated using maximum a posteriori probability and Kalman filtering to estimate the target scattering coefficients. To solve this problem more accurately and efficiently, three nature-inspired algorithms (modified particle swarm optimisation algorithm, Bat Algorithm, and Beetle Antennae Search algorithm), as a nature-inspired waveform optimisation (NIWO) approach is proposed. It is demonstrated through computer simulations that the proposed NIWO approach significantly outperforms the SDR approach, showing a promising tool for waveform optimisation in cognitive radar. |
| Databáze: | Directory of Open Access Journals |
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