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Advances, Systems and Applications

Table 1 The MSE comparison of different methods

From: A SINS/DVL/USBL integrated navigation and positioning IoT system with multiple sources fusion and federated Kalman filter

Method Contribution
AFKF [16] weighted coefficient
Improved AFKF [17] factors adjusted automatically
Transfer alignment [18] divided the high-dimensional state vector into two parts
Improved covariance [19] derived a real-time estimates of improved covariance
SINS/GPS/CNS/Radar integrated system [11] calculated the state parameters with dual-state detection
Joint filter to fuse data [20] INS/CNS/DVL combined system
Federated unscented Kalman filter [21] with different vehicle motion models to estimate
Federated hybrid filter [22] utilizes a minimum variance criterion to fuse
An adaptive filter [23] conquer the performance degradation
Federated filter with a feedback scheme [24] GNSS/INS/visual odometry combined positioning system
Federated Kalman filter for indoor positioning distance is estimated through RSS