Distributed Cooperative Localization for Unmanned Systems Using UWB/INS Integration in GNSS-denied Environments

In unmanned systems, integration of inertial measurement unit (IMU) with global navigation satellite systems (GNSS) provides accurate state information when satellite signals are available. However, during GNSS-denied periods, positioning accuracy degrades rapidly due to the accumulating errors of the inertial navigation system (INS). To improve positioning accuracy in such environments, we propose a distributed cooperative localization method that leverages relative distance measurements between unmanned systems to mitigate cumulative positioning errors of INS. We first analyze how the cross-covariance matrix and the number of measurements in centralized cooperative localization systems, based on Extended Kalman Filter (EKF), impact positioning accuracy. Our theoretical analysis reveals that the cross-covariance matrix plays a key role in determining localization accuracy, and confirms that propagating the covariance matrix of its own state among individual systems within a cluster is feasible. Based on these insights, we develop a distributed cooperative localization algorithm that maintains the cross-covariance matrix while using only a subset of relative distance measurements. The performance of the proposed algorithm is validated through theoretical analysis and field experiments. The results demonstrate that: 1) Broadcasting a vehicle's covariance matrix within a cluster is feasible; 2) Compared to INS-only solution, the distributed cooperative localization method we proposed reduces root mean square error (RMSE) by approximately 50%, with positioning accuracy approaching to that of the centralized cooperative localization results; 3) Incorporating known reference point within a cluster can constrain error drift.