Accurate Distance Estimation between Things: A Self-correcting Approach

This paper suggests a method to measure the physical distance between an IoT device (a Thing) and a mobile device (also a Thing) using BLE (Bluetooth Low-Energy profile) interfaces with smaller distance errors. BLE is a well-known technology for the low-power connectivity and suitable for IoT devices as well as for the proximity with the range of several meters. Apple has already adopted the technique and enhanced it to provide subdivided proximity range levels. However, as it is also a variation of RSS-based distance estimation, Apple's iBeacon could only provide immediate, near or far status but not a real and accurate distance. To provide more accurate distance using BLE, this paper introduces additional self-correcting beacon to calibrate the reference distance and mitigate errors from environmental factors. By adopting self-correcting beacon for measuring the distance, the average distance error shows less than 10% within the range of 1.5 meters. Some considerations are presented to extend the range to be able to get more accurate distances

Geometry and Motion-Based Positioning Algorithms for Mobile Tracking in NLOS Environments

Abstract—This paper presents positioning algorithms for cellular network-based vehicle tracking in severe non-line-of-sight (NLOS) propagation scenarios. The aim of the algorithms is to enhance positional accuracy of network-based positioning systems when the GPS receiver does not perform well due to the complex propagation environment. A one-step position estimation method and another two-step method are proposed and developed. Constrained optimization is utilized to minimize the cost function which takes account of the NLOS error so that the NLOS effect is significantly reduced. Vehicle velocity and heading direction measurements are exploited in the algorithm development, which may be obtained using a speedometer and a heading sensor, respectively. The developed algorithms are practical so that they are suitable for implementation in practice for vehicle applications. It is observed through simulation that in severe NLOS propagation scenarios, the proposed positioning methods outperform the existing cellular networkbased positioning algorithms significantly. Further, when the distance measurement error is modeled as the sum of an exponential bias variable and a Gaussian noise variable, the exact expressions of the CRLB are derived to benchmark the performance of the positioning algorithms. Index Terms—NLOS mitigation, heading angle, constrained optimization, Cramer-Rao lower bound, mobile tracking.

Geometry and motion based positioning algorithms for mobile tracking in NLOS environments

This paper presents positioning algorithms for cellular network-based mobile tracking in severe non-line-of-sight (NLOS) propagation scenarios. The aim of the algorithms is to enhance positional accuracy of network-based positioning systems when the GPS receiver does not perform well due to the hostile environment. Two positioning methods with NLOS mitigation are proposed. Constrained optimization is utilized to minimize the cost function which takes account of the NLOS error. Mobile velocity and heading angle information is exploited to greatly enhance position accuracy. It is observed through simulation that the proposed methods significantly outperform other cellular network based positioning algorithms. Further, the exact expressions of the CRLB are derived when the distance measurement error is the sum of an exponential and a Gaussian variable. ©2010 IEEE