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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