An Experimental Evaluation of Position Estimation Methods for Person Localization in Wireless Sensor Networks

Abstract

Abstract. In this paper, the localization of persons by means of a Wireless Sensor Network (WSN) is considered. Persons carry on-body sensor nodes and move within a WSN. The location of each person is calculated on this node and communicated through the network to a central data sink for visualization. Applications of such a system could be found in mass casualty events, firefighter scenarios, hospitals or retirement homes for example. For the location estimation on the sensor node, three derivatives of the Kalman filter and a closed-form solution (CFS) are applied, compared, and evaluated in a real-world scenario. A prototype 65-node ZigBee WSN is implemented and data are collected in in-and outdoor environments with differently positioned on-body nodes. The described estimators are then evaluated off-line on the experimentally collected data. The goal of this paper is to present a comprehensive real-world evaluation of methods for person localization in a WSN based on received signal strength (RSS) range measurements. It is concluded that person localization in in-and outdoor environments is possible under the considered conditions with the considered filters. The compared methods allow for sufficiently accurate localization results and are robust against inaccurate range measurements.

Accuracy Estimation for Sensor Systems

“…In addition, Bayesian filters, such as Kalman and Particle filters have also been broadly used for online position estimation both in sensor networks and robotics research [24]. A recent comparative evaluation of different filters for person localization using RSS (Received Signal Strength) measurements is presented in [25]. Parameter Estimation / Learning: While much of the initial work was restricted to distributed inference of the monitored state, more recently there has been considerable interest in parameter estimation.…”

A New Search Strategy of Radio Fingerprint Matching Method in Wireless Sensor Network

“…When there are obstacles between transmitter and receiver, radio signals arrive at the receiver from different paths and contribute to RSS positively or negatively according to their phase which is called multipath effect. Experiment studies of [8], [7], [9], [10] show that device inhomogeneity, antenna orientation and height of devices also affect RSS values. So, although RSS has a correlation with distance, it shows much random variation.…”

Wireless Sensor Network 3-Dimensional Positioning Method

“…TOA ranging positioning need to measure the signal arrival time, because the synchronization error has a great influence to the measurement of the distance, so to achieve high positioning accuracy, must be between monitoring targets and monitoring equipment to ensure the strict clock synchronization, however the request this largely increased the complexity of the equipment on the implementation. Despite the RSSI positioning conforms to the requirement of low power, low cost, RSSI positioning in practice, however, because of multipath spread and non-line of sight makes signal propagation model is complex, such as the change of the external environment has a great instability, often cause the RSSI is positioning result there is a big error [13][14][15]. To avoid the complexity of TOA synchronous equipment, find better positioning precision positioning technology at the same time, people is proposed based on TDOA positioning mechanism, it only requires monitoring equipment can meet the clock accurate synchronization between conditions, which greatly reduced the complexity of the equipment, at the same time TDOA technology does not need to know to receive transmission time between the signal from transmitter to receiver, but by the time delay estimation algorithm for time delay value.…”