Multiobjective Optimization — New Formulation
and Application to Radar Signal Processing

Abstract

The present thesis aims to make an in-depth study of Multiobjective optimization (MOO), Multiobjective algorithms and Radar Pulse Compression. Following the approach of bacteria foraging technique, a new MOO algorithm Multiobjective Bacteria Foraging Optimization (MOBFO) has been proposed in this thesis. We compared the performance of our proposed algorithm with existing algorithms Nondominated Sorting Genetic Algorithm (NSGA-II) and Multiobjective Particle Swarm Optimization (MOPSO) for different test functions. In radar signal processing Pulse Compression is used for high range resolution and long range detection. The classical methods for Pulse Compression of the received signal use matched filter and mismatched filter. For improving the performance of pulse compression, a new problem formulation has been constructed that uses constrained function optimization with the help of Particle Swarm Optimization (PSO).
Artificial Neural Network (ANN) is being used for Pulse Compression that achieves a significant supression of the sidelobes. Functional Link Artificial Neural Network (FLANN)has been proposed for better sidelobes reduction than Multi Layer Perceptron (MLP)network with both lower computational and lower structural complexity. MOO approach
has been proposed to use with Radial Basis Function (RBF) for Pulse Compression that improves the accuracy and complexity of RBF network.