Distributed Target Tracking in Multi-Agent Networks via Sequential Quadratic Alternating Direction Method of Multipliers
Ola Shorinwa, Mac Schwager · 2023
We present a distributed algorithm for multi-agent target tracking, posed as a maximum a-posteriori (MAP) optimization problem. MAP estimation is, in general, a non-convex optimization that depends on each agent’s local observation of the target, necessitating a distributed algorithm. In our algorithm, each agent solves a series of local optimization problems to estimate the target’s trajectory, while communicating with its one-hop neighbors over a communication network. The agents do not communicate their raw observations, which may be high dimensional (e.g., images), and they do not rely on a central coordinating node or leader, minimizing the communication bandwidth requirements of our approach. We utilize the sequential quadratic programming (SQP) paradigm, with distributed computation of the ensuing sub-problems achieved via the consensus alternating direction method of multipliers (C-ADMM). We empirically demonstrate faster convergence of our algorithm to a locally optimal solution compared to other distributed methods. In addition, our algorithm achieves about the same communication overhead as the best competing distributed algorithm.