Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization.

Area of Science:

• Signal Processing
• Optimization Algorithms
• Wireless Communications

Background:

• Non-line-of-sight (NLoS) conditions pose significant challenges for accurate wireless device localization.
• Existing Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) algorithms struggle with nonlinear equations in NLoS environments.
• Optimization algorithms require enhancement for improved accuracy and convergence in complex positioning scenarios.

Purpose of the Study:

• To propose a novel hybrid TDOA/AOA location algorithm optimized using a combination of Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO).
• To enhance the performance and accuracy of existing location algorithms in NLoS environments.
• To improve the convergence speed and robustness of the positioning system.

Main Methods:

• Developed a hybrid TDOA/AOA location algorithm integrating CSA and PSO.
• Modified the fitness function using maximum likelihood estimation for better optimization.
• Incorporated an initial solution into the starting population to accelerate convergence and maintain diversity.
• Compared the proposed algorithm against Taylor, Chan, PSO, CPSO, and basic CSA algorithms through simulations.

Main Results:

• The proposed hybrid CSA-PSO algorithm demonstrated superior performance compared to TDOA/AOA, Taylor, Chan, PSO, CPSO, and basic CSA algorithms.
• The algorithm achieved higher node positioning accuracy.
• The enhanced method exhibited faster convergence speed.
• The algorithm showed robust performance in NLoS conditions.

Conclusions:

• The proposed TDOA/AOA hybrid location algorithm effectively addresses NLoS challenges.
• The optimization strategy significantly improves positioning accuracy and convergence speed.
• This enhanced algorithm offers a robust solution for accurate localization in complex environments.