Investigations on real time RSSI based outdoor target tracking using kalman filter in wireless sensor networks

Target tracking is essential for localization and many other applications in Wireless Sensor Networks (WSNs). Kalman filter is used to reduce measurement noise in target tracking. In this research TelosB motes are used to measure Received Signal Strength Indication (RSSI). RSSI measurement doesn’t require any external hardware compare to other distance estimation methods such as Time of Arrival (TOA), Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). Distances between beacon and non-anchor nodes are estimated using the measured RSSI values. Position of the non-anchor node is estimated after finding the distance between beacon and non-anchor nodes. A new algorithm is proposed with Kalman filter for location estimation and target tracking in order to improve localization accuracy called as MoteTrack InOut system. This system is implemented in real time for indoor and outdoor tracking. Localization error reduction obtained in an outdoor environment is 75%

A Geometric Modelling Approach to Determining the Best Sensing Coverage for 3-Dimensional Acoustic Target Tracking in Wireless Sensor Networks

Existing 3-dimensional acoustic target tracking methods that use wired/wireless networked sensor nodes to track targets based on four sensing coverage do not always compute the feasible spatio-temporal information of target objects. To investigate this discrepancy in a formal setting, we propose a geometric model of the target tracking problem alongside its equivalent geometric dual model that is easier to solve. We then study and prove some properties of dual model by exploiting its relationship with algebra. Based on these properties, we propose a four coverage axis line method based on four sensing coverage and prove that four sensing coverage always yields two dual correct answers; usually one of them is infeasible. By showing that the feasible answer can be only sometimes identified by using a simple time test method such as the one proposed by ourselves, we prove that four sensing coverage fails to always yield the feasible spatiotemporal information of a target object. We further prove that five sensing coverage always gives the feasible position of a target object under certain conditions that are discussed in this paper. We propose three extensions to four coverage axis line method, namely, five coverage extent point method, five coverage extended axis lines method, and five coverage redundant axis lines method. Computation and time complexities of all four proposed methods are equal in the worst cases as well as on average being equal to Θ(1) each. Proposed methods and proved facts about capabilities of sensing coverage degree in this paper can be used in all other methods of acoustic target tracking like Bayesian filtering methods

A Geometric Modelling Approach to Determining the Best Sensing Coverage for 3-Dimensional Acoustic Target Tracking in Wireless Sensor Networks

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