Indoor Pedestrian Positioning Tracking Algorithm with Sparse Anchor Nodes

Abstract

In order to solve the indoor pedestrian positioning and tracking problems under the condition of sparse anchor nodes, this paper presents a new tracking scheme which predicts the staff position under the condition of indoor location fingerprints based on particle filter. In the proposed algorithm, the indoor topology is adopted to constrain and correct the results. Simulation results show that the proposed algorithm can significantly improve the accuracy of indoor pedestrian positioning and tracking more than the Kalman filter and k-nearest neighbor (KNN) algorithms. The simulation results also show that under the condition of sparse nodes deployment good tracking results can still be achieved through the adoption of indoor topology and the average positioning error is about 1.9 m.

Hybrid area exploration–based mobility‐assisted localization with sectored antenna in wireless sensor networks

“…However, in path planning model, the trajectory of MA nodes is planned strategically to localize all the unknown nodes . In the random mobility model–based localization scheme, there may arise a condition called sparse anchor node condition . The sparse anchor node condition can be overcome by the path planning–based localization scheme.…”

“…The conventional mobility‐assisted localization schemes, wherein MA nodes are mounted with omnidirectional antenna, are bound to suffer from collinearity problem and sparse anchor node condition . In collinearity problem, the deployed static node fails to receive MA node position information from at least three noncollinear positions as a result of which the static nodes fail to precisely trilaterate its location.…”