Accurate cooperative-localization of stationary agents and tracking of mobile targets are critical for multi-agent autonomy, particularly in global navigation satellite system (GNSS)-denied environments such as maritime search-and-rescue (SAR) missions. In such settings, agents often lack reliable global positioning and complete target observability, challenging distributed perception and coordination. State-of-the-art approaches such as joint Kalman filtering was widely applied.

To address this, we propose two approaches: a sequential optimization strategy and a unified integrated optimization framework. The sequential method decouples localization and tracking—first estimating agent positions via inter-agent ranging and then performing distributed Gaussian Process (GP) tracking using alternating direction method of multipliers (ADMM) -based fusion. Although efficient and modular, this approach may suffer from error propagation. To mitigate this, we introduce integrated optimization framework that couples both tasks via a weighted multi-objective cost. A convex relaxation of the localization subproblem yields closed-form updates, and the full problem is solved in a distributed manner using ADMM.

Simulations based on GNSS-denied maritime scenarios show that both methods enhance tracking and localization performance, with the integrated framework offering superior speed. These results underscore the value of cooperative self-localization and distributed tracking in complex multi-agent environments.