Array Pattern Nulling for Interference Cancellation using a Modified Invasive Weed Optimization with Laplace Distribution for Defence Applications

Abstract

Adaptive antenna arrays are essential in defense, radar, and secure communications, where accurate reception of desired signals is required despite interference and noise. Real-world environments contain multiple jammers, dynamic interference, moving sources, and time-varying noise, which reduce the effectiveness of conventional beamforming and null-steering techniques. To address these challenges, this work analyzes an adaptive array pattern-nulling technique based on Modified Invasive Weed Optimization with Laplace distribution (MLIWO). After signal acquisition and digitization, high-resolution algorithms such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT) are used to estimate interference directions and spatial characteristics. MLIWO then iteratively optimizes the array weight vector to preserve main-lobe fidelity while producing deep nulls in interference directions. The optimized weights form the desired radiation pattern, evaluated using null depth (ND), side-lobe level (SLL), array gain, and signal to interference plus noise ratio (SINR). MATLAB results show that MLIWO achieves significant interference suppression, obtaining ND values of −80.75 dB at −10° and −88.87 dB at −42°, outperforming IWO and crow search optimization (CSO).