Open AccessNature‐inspired waveform optimisation for range spread target detection in cognitive radar
Author(s) -
Wang Qing,
Li Meng,
Gao Lirong,
Li Kaiming,
Chen Hua
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0527
Subject(s) - waveform , computer science , radar , range (aeronautics) , algorithm , kalman filter , maximum a posteriori estimation , artificial intelligence , mathematics , engineering , maximum likelihood , telecommunications , statistics , aerospace engineering
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.