Distributed ADMM for Target Localization using Radar Networks

Traditional target tracking using monostatic radar systems typically rely on centralized or decentralized architectures, where all data is transmitted to a fusion center for processing the position and velocity of mobile agents. This approach introduces a single point of failure and can lead to increased data transmission times, particularly when the fusion center is far from individual radar nodes. To overcome these issues, we introduce a distributed Alternating Direction Method of Multipliers (ADMM) for target localization using a radar network, wherein each radar node shares its observed data only with its immediate neighboring nodes, and yet achieves consensus with the radar network on the estimated target locations and velocities. We conducted simulations incorporating critical parameters such as the number of radar nodes and Signal-to-Noise Ratio (SNR) to assess their impact on estimation accuracy and convergence speed. The results demonstrate that the proposed DADMM effectively eliminates the single point of failure. We highlight the additional benefits of our proposed framework, and present directions for future work.