Nature-inspired waveform optimisation for range spread target detection in cognitive radar

• Main Authors: Qing Wang, Meng Li, Lirong Gao, Kaiming Li, Hua Chen
• Format: Article
• Language: English
• Published: Wiley 2019-10-01
• Series: The Journal of Engineering
• Subjects: search problems; 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
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0527

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.