A Feasibility Study of RIP Using 2.4 GHz 802.15.4 Radios

This paper contains a feasibility study of Radio Interferometric Positioning (RIP) implemented on a widely used 2.4 GHz radio (CC2430). RIP is a relatively new localization technique that uses signal strength measurements. Although RIP outperforms other RSS-based localization techniques, it imposes a set of unique requirements on the used radios. Therefore, it is not surprising that all existing RIP implementations use the same radio (CC1000), which operates below the 1 GHz range. This paper analyzes to what extent the CC2430 complies with these requirements. This analysis shows that the CC2430 platform introduces large and dynamic sources of errors. Measurements with a CC2430 test bed in a line-of-sight indoor environment verify this. The measurements indicate that the existing RIP algorithm cannot cope with these types of errors, and will incur a relatively low accuracy of 3.1 meter. Based on these results, we made an initial implementation of a new algorithm, which can cope with these errors, and decreases this positioning error by a factor of two to 1.5 meter accuracy

Estimating Walking Distance with a Smart Phone

Abstract-A huge body of work utilized signal strength of short range signal (such as WiFi, Bluetooth, ultra sound or Infrared) to build a radio map for indoor localization, by deploying a great number of beacon nodes in the building. The drawback of such an infrastructure-based approach is that the deployment and calibration of the system is costly and labor-intensive. To overcome that, some prior studies proposed the use of Pedestrian Dead Reckoning (PDR) for indoor localization. The PDR system does not require to build a beacon-based infrastructure, in which a small number of sensors are put on the pedestrian. These sensors (such as G-sensor and Gyro) are used to estimate the distance and direction that the user traveled. The PDR approach can be generally categorized into two types: footmounted and waist-mounted. In general, the foot-mounted system can get accurate step length, but perform poorly in estimated heading direction. On the other hand, the waistmounted system can estimate direction with high accuracy, but is hard to measure the step length. In this work, we proposed a waist-mounted based PDR using one 3-axis accelerometer and one gyroscope sensor. We utilize vertical acceleration to implement double integral for measuring the user's instant height change and use some physical features of vertical acceleration during the walking to calibrate the measurement. Then based on the Pythagoras' Theorem, we can estimate each step length based on the user's height change during his/her walking. Our experiment results show that the accuracy of placing smartphone on the waist is about 97.35% and placing smart-phone on the chest pocket is about 96.14% in estimating the user's walking distance

Adding direction to a directionless world

This thesis focuses on the feasibility of adding a new dimension to spatial context referred to as orientation. Based on directional antennas and previous work in RSSI based radio distance estimation, this work shows that using directional antennas alone, the angle between two devices can be approximated. Furthermore, this thesis shows the effect of distance estimation error on angle estimation and how the number of samples affects the error in angle estimation

Barometric phone sensors: More hype than hope!

Singapore National Research Foundation under International Research Centre @ Singapore Funding Initiative; Ministry of Education, Singapore under its Academic Research Funding Tier

A Fuzzy Logic-Based Approach for Node Localization in Mobile Sensor Networks

In most range-based localization methods, inferring distance from radio signal strength using mathematical modeling becomes increasingly unreliable and complicated in indoor and extreme environments, due to effects such as multipath propagation and signal interference. We propose FuzLoc, a range-based, anchor-based, fuzzy logic enabled system system for localization. Quantities like RSS and distance are transformed into linguistic variables such as Low, Medium, High etc. by binning. The location of the node is then solved for using a nonlinear system in the fuzzy domain itself, which outputs the location of the node as a pair of fuzzy numbers. An included destination prediction system activates when only one anchor is heard; it localizes the node to an area. It accomplishes this using the theoretical construct of virtual anchors, which are calculated when a single anchor is in the node’s vicinity. The fuzzy logic system is trained during deployment itself so that it learns to associate an RSS with a distance, and a set of distances to a probability vector. We implement the method in a simulator and compare it against other methods like MCL, Centroid and Amorphous. Extensive evaluation is done based on a variety of metrics like anchor density, node density etc