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.

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.

Maximum two-feet distance constraint and ultra-wideband (UWB) technology are commonly used in pedestrian inertial navigation, but they suffer from poor precision and pre-installed anchors. In this work, a multi-person cooperative navigation method based on geometric constraints of foot-to-foot and person-to-person distances measured by UWB is proposed. Combined with gait analysis in kinematics, the gait is divided into the stance and swing phases. For each phase, attitude, position, and the related characteristics of two feet are analyzed and modeled respectively. Furthermore, considering the influence of non-line-of-sight (NLOS) conditions on UWB ranging, the person-to-person distances are formed into the reticulate geometric constraint. An extended Kalman filter is established to estimate the attitude and position of each agent optimally according to the geometric constraints and position vectors. Comparative experiments are designed in various environments. Compared with the traditional inertial navigation system (INS) method, the proposed method improves the end-to-end error by 40% to 70% and the average error by 35% to 72%, respectively. The results show that the proposed method has higher accuracy and better robustness to the environments than the traditional INS method.

Location-based service is vital for firefighters. To reduce the deviation of the inertial navigation system (INS) and to suppress the non-line-of-sight (NLOS) error of the ultrawideband (UWB) positioning, a multiperson decentralized cooperative localization method is proposed in this study. Every firefighter has a foot-mounted inertial navigation system, which uses extended kalman filter (EKF) to estimate firefighter’s attitude and position. Furthermore, a group of firefighters construct a decentralized cooperative localization system. To suppress the accumulation error of INS and estimate the optimal position for every firefighter according to the relative ranging measured by UWB, a combined filter which fused particle filter (PF) and EKF is proposed. The cooperative localization method uses firefighters as mobile anchors and sets three fixed anchors. To suppress the NLOS error, the combined filter adaptively adjusts the observation vector according to UWB ranging. The experimental results show, the average and maximum positioning errors of the decentralized cooperative localization method have been suppressed by 60.50% to 61.77% and 59.61% to 65.83% compared with the conventional INS method, respectively. Therefore, the proposed method improves the positioning accuracy and has good robustness to environment.