A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers

Abstract

Due to the ease of development and inexpensiveness, indoor localization systems are getting a significant attention but, with recent advancement in context and location aware technologies, the solutions for indoor tracking and localization had become more critical. Ranging methods play a basic role in the localization system, in which received signal strength indicator-(RSSI-) based ranging technique gets the most attraction. To predict the position of an unknown node, RSSI measurement is an easy and reliable method for distance estimation. In indoor environments, the accuracy of the RSSI-based localization method is affected by strong variation, specially often containing substantial amounts of metal and other such reflective materials that affect the propagation of radio-frequency signals in nontrivial ways, causing multipath effects, dead spots, noise, and interference. This paper proposes an adaptive smoother based location and tracking algorithm for indoor positioning by making fusion of RSSI and link quality indicator (LQI), which is particularly well suited to support context aware computing. The experimental results showed that the proposed mathematical method can reduce the average error around 25%, and it is always better than the other existing interference avoidance algorithms.

ZigBee phase shift measurement approach to mobile inspection robot indoor positioning techniques

“…RSSI is one of the most commonly used range free positioning technique among RF networks. The method, widely described in [4,15,28], is radio signal strength of the received signal from transmitters placed at known positions. The foundation of this method is fading of signal along with increasing distance to its source, which can be denoted as:…”

Advanced Smoothing Approach of RSSI and LQI for Indoor Localization System

“…In particular, it is difficult to implement an accurate localization performance because of the range errors being only a few meters in RSSI based fingerprint method. Also, because noises are easily included in the RSSI-based localization by disturbances such as multipath and electronic equipment, several filtering techniques have been proposed for reducing these noise problems [5, 7, 9–11, 13]. These methods reduce the noise and errors from actual signal by using the RSSI values, LQI values, and a fusion of RSSI and LQI values.…”

ALRD: AoA Localization with RSSI Differences of Directional Antennas for Wireless Sensor Networks

“…Two methods, Estimating Direction-of-Arrival (EDoA) [5] and Rotatable Antenna Localization (RAL) [16], utilize the property of directional antennas to estimate the AoA of a signal. EDoA estimates the AoA of an incoming signal by using a mechanically actuated parabolic reflector.…”

“…By using the estimated AoAs and locations of two distinct beacon nodes, a sensor node can then calculate its own location with a localization error of 76 cm within a 10 × 10-meter indoor area. Two enhanced methods were further proposed to reduce the localization error by a factor of 10% [16]. EDoA and RAL both need a long time to rotate the antenna or the reflector for observing the variation of the RSSI values while estimating the AoA.…”

“…Conversely, range-based schemes usually have better accuracy. They measure the time of arrival (ToA) [2, 3], time difference of arrival (TDoA) [4–6], angle of arrival (AoA) [2, 4, 15, 17, 18, 20, 21, 27], and received signal strength indicator (RSSI) [3, 7–14, 16, 20, 21] to estimate the distances or angles between pairs of nodes, which in turn are used to calculate the locations of nodes. Most kinds of measurement are taken with extra auxiliary hardware.…”