Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target Localization.

Area of Science:

• Signal Processing
• Optimization Algorithms
• Target Localization

Background:

• Estimating passive target positions from Time Difference of Arrival (TDOA) measurements is challenging due to non-linear, non-convex objective functions.
• Traditional optimization methods are often inadequate for Maximum Likelihood (ML) estimation in these scenarios.

Purpose of the Study:

• To propose a novel hybrid optimization algorithm, Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization (CAHBPSO), for passive target localization.
• To improve the accuracy and robustness of target position estimation using noisy TDOA measurements.

Main Methods:

• Hybridization of Butterfly Optimization (BOA) and Particle Swarm Optimization (PSO) algorithms.
• Incorporation of chaos theory into PSO's inertia weight and an adaptive strategy for BOA's sensory fragrance.
• Utilizing adaptive switch probability to integrate global and local search phases.
• Employing Semidefinite Programming (SDP) to convexify the problem.

Main Results:

• The proposed CAHBPSO algorithm demonstrates superior performance compared to BOA, PSO, and SDP on CEC2014 benchmark problems.
• CAHBPSO effectively estimates passive target positions across a wide range of noise levels.
• The algorithm achieves the Cramer-Rao Lower Bound (CRLB), indicating optimal estimation accuracy.

Conclusions:

• CAHBPSO offers a robust and accurate solution for passive target localization using TDOA measurements.
• The hybrid approach effectively addresses the non-convexity issues in ML estimation.
• This method provides a significant advancement in passive target tracking and positioning systems.