Spatiotemporal Location Fingerprint Generation Using Extended Signal Propagation Model

Abstract

-Fingerprinting is a widely used positioning technology for received signal strength (RSS) based wireless local area network (WLAN) positioning system. Though spatial RSS variation is the key factor of the positioning technology, temporal RSS variation needs to be considered for more accuracy. To deal with the spatial and temporal RSS characteristics within a unified framework, this paper proposes an extended signal propagation mode (ESPM) and a fingerprint generation method. The proposed spatiotemporal fingerprint generation method consists of two algorithms running in parallel; Kalman filtering at several measurement-sampling locations and Kriging to generate location fingerprints at dense reference locations. The two different algorithms are connected by the extended signal propagation model which describes the spatial and temporal measurement characteristics in one frame. An experiment demonstrates that the proposed method provides an improved positioning accuracy.

A Target Tracking Based on Bearing and Range Measurement With Unknown Noise Statistics

“…Its extended version employing Taylor series, i.e. the extended Kalman filter is also successfully applied to non-linear problems [1][2][3][4]. However, our concern in this paper is to solve the problem with unknown noise statistics regardless of weather the noise is Gaussian or not, particularly for a non-linear model.…”

“…4(b) shows the result when the noise variance scenario is (0.01, 1, 0.01). In this scenario, we need to select 0, (3,4) 0.1 i σ = for the best performance as shown in Fig. 4(b).…”

Multitarget Tracking by Particle Filtering Based on RSS Measurement in Wireless Sensor Networks

“…A salient feature of the RSS measurement is that the parameter to be estimated is directly related with the measurement while bearing and/or range measurement is not. This means that the data fusion methods, such as time of arrival (TOA) [3], time difference of arrival (TDOA) [4][5][6], or angle of arrival (AOA) [7,8], require a preprocessor before acquiring the measurement (bearing or range) while RSS [9][10][11] is almost raw data from the target. Therefore, we might be able to apply maximum likelihood (ML) method directly to RSS measurement-based estimator.…”