WiFi/PDR integrated navigation with robustly constrained Kalman filter

Nowadays, global navigation satellite systems (GNSSs) are widely used in location-based services (LBSs) as they can provide high-accuracy position services continuously. However, their performance deteriorates in indoor scenarios, in which GNSS signal reception is limited or completely impossible. In this paper, an enhanced constrained Kalman filter is presented to enhance the indoor positioning performance of a LBS and for use in a WiFi/pedestrian dead reckoning (PDR) integrated navigation algorithm. A robust scheme for computing the gross error in constrained conditions is suggested to make the performance of the constrained condition model in the WiFi/PDR integrated system more robust. The results of simulation analysis indicate that the robustly constrained Kalman filter can reliably determine gross errors in constrained conditions. An indoor field experiment was conducted to test the performance of the proposed filter algorithm, and the results show that the improved filter can eliminate the effect of gross error from constrained conditions in the WiFi/PDR system.