RSS‐based indoor localisation using MDCF

Abstract

As a low-cost distance measurement method, received signal strength (RSS) is often used for indoor wireless sensor localisation. However, RSS values can be easily influenced by multi-path fading, noise and other environmental parameters. This decreases the accuracy and stability of estimated distance. To improve localisation accuracy, this study proposes a multiplicative distance-correction factor (MDCF) to counteract the inaccuracy of estimated distance. In the same indoor environment, the product of this CF and estimated distance is regarded as a good approximation of real distance between unknown node and an anchor node. Then, two location estimated methods based on MDCF (MDCF-grid and MDCF-particle swarm optimisation) are proposed. The experimental results confirm that the proposed location estimation methods can significantly improve localisation accuracy without extra hardware in practical indoor scenarios.

Compressive sensing for localisation in wireless sensor networks: an approach for energy and error control

“…Compressed sensing, in particular, has been applied to node localisation in [12][13][14][15] all of which depend on signal intensity distance measurements. In [12,13], anchor-based indoor localisation algorithms are considered, in which the RSS method of distance measurement is employed rather than the TOA method due to the significant multipath effect in complex indoor environments which severely affect TOA measurements. While the present work is not the first to incorporate CS in the WSN localisation context, there are a number of novel aspects to it.…”

LocURa: A New Localisation and UWB-Based Ranging Testbed for the Internet of Things

“…While it is possible to address the localisation issue using various signals, measuring the TOF between a pair of nodes has been one of the most reliable and precise solutions. Although the received power level, which can be expressed as a Received Signal Strength Indication (RSSI), is readily available on most radio platforms, it is strongly challenged by fading phenomena in dynamic indoor environments [1]. Using the signal's Angle Of Arrival (AOA) could significantly reduce the number of reference nodes but the hardware constraints are often prohibitive for small form-factor devices (antenna arrays...).…”

Robust Shrinkage Range Estimation Algorithms Based on Hampel and Skipped Filters

“…Namely, the more accurate is the distance measurement; the better is the localization accuracy. Range estimation problems under lineof-sight (LOS) environments have been studied in previous works [1][2][3][4][5]. In [1], the ad hoc closed-form hybrid TOA/RSS range estimation algorithm is developed.…”

“…In [4], the best unbiased and linear minimum mean square range estimates are studied in the context of RSS-based range estimation. Also, a range estimation method based on the multiplicative distance-correction factor (MDCF) is developed to attenuate the inaccuracy for the estimated range, where grid based optimization and particle swarm optimization are employed [5].…”