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

Abstract

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.

Keywords

• 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