Receding Horizon Team Optimal Decentralized Filter for Discrete Multi-agent Systems with Delay

We investigate the receding horizon team-optimal decentralized filter for discrete-time multi-agent systems subject to delayed information sharing. By minimizing the team estimation error covariance over a finite horizon under unbiasedness constraints, both batch and recursive implementations of the RHTODF are derived. It is shown that the resulting estimator admits a decomposed structure consisting of a common-information component and local innovation-based correction terms, which enables distributed implementation under delayed communication constraints. Numerical simulations demonstrate that the proposed RHTODF achieves improved estimation accuracy and smaller estimation error than the local Kalman filter, thereby illustrating the effectiveness of the proposed finite-horizon team-optimal filtering framework.