Distributed Target Tracking in Multi-Agent Networks via Sequential Quadratic Alternating Direction Method of Multipliers

@inproceedings{shorinwa_distributed_2023,
  address = {San Diego, CA, USA},
  title = {Distributed {Target} {Tracking} in {Multi}-{Agent} {Networks} via {Sequential} {Quadratic} {Alternating} {Direction} {Method} of {Multipliers}},
  isbn = {9798350328066},
  url = {https://ieeexplore.ieee.org/document/10156402/},
  abstract = {We present a distributed algorithm for multiagent 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.},
  language = {en},
  urldate = {2023-08-29},
  booktitle = {2023 {American} {Control} {Conference} ({ACC})},
  publisher = {IEEE},
  author = {Shorinwa, Ola and Schwager, Mac},
  month = may,
  year = {2023},
  pages = {341--348},
  month_numeric = {5}
}