Cooperative localization (CL) is a popular research topic in the area of localization. Research is becoming more focused on Unmanned Aerial Vehicles (UAVs) and robots and less on pedestrians. This is because UAVs and robots can work in formation, but pedestrians cannot. In this study, we develop an adaptive decentralized cooperative localization (DCL) algorithm for a group of firefighters. Every member maintains a local filter and estimates the position and the relative measurement noise covariance is estimated rather than a fixed value. We derived the explicit expressions for the inter-member collaboration instead of using approximations. This method reduces the influence of non-line-of-sight (NLOS) errors in the ultra-wideband (UWB) ranging on the CL, eliminating the need for fixed UWB anchors. The proposed algorithm was validated by two experiments designed in the building and forest environments. The experimental results demonstrate that the proposed algorithm improved the accuracy of localization, and the proposed algorithm suppressed the localization errors by 14.23% and 47.01% compared to the decentralized cooperative localization extended Kalman filter (DCLEKF) algorithm, respectively.

There are many demands for the cooperative localization (CL) of multiple people, such as firefighter rescue. The classical foot-mounted inertial navigation based on zero velocity update (ZUPT) suffers from accumulating error due to the low-cost inertial sensor, and the pre-placed anchors in the ultra-wideband (UWB) system limit the application in an unknown environment. In this study, a group of sensors including the inertial measurement unit (IMU), magnetometer, barometer, and UWB sensor is used. Through the different characteristics of sensors and the position relationship between people, a cooperative localization system using an extended Kalman filter for three-dimensional firefighter tracking is proposed. Ranging information between firefighters from UWB is utilized, and couplings introduced by relative measurement are estimated. Two experiments are designed to verify the proposed algorithm in building and forest environments. Compared with the results of single-person inertial navigation, the average positioning precision of the algorithm in the building and forest is, respectively, improved by 38.93% and 79.01%. This approach successfully suppresses the divergence of positioning errors, and fixed UWB anchors are not needed.