Workshop on Positioning Navigation and Communication 

About: Workshop on Positioning Navigation and Communication is an academic conference. The conference publishes majorly in the area(s): Ranging & Wireless sensor network. Over the lifetime, 442 publications have been published by the conference receiving 7493 citations.

A comparative survey of WLAN location fingerprinting methods

Abstract: The term “location fingerprinting” covers a wide variety of methods for determining receiver position using databases of radio signal strength measurements from different sources. In this work we present a survey of location fingerprinting methods, including deterministic and probabilistic methods for static estimation, as well as filtering methods based on Bayesian filter and Kalman filter. We present a unified mathematical formulation of radio map database and location estimation, point out the equivalence of some methods from the literature, and present some new variants. A set of tests in an indoor positioning scenario using WLAN signal strengths is performed to determine the influence of different calibration and location method parameters. In the tests, the probabilistic method with the kernel function approximation of signal strength histograms was the best static positioning method. Moreover, all filters improved the results significantly over the static methods.

Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU

Abstract: The estimation of the position of a person in a building is a must for creating Intelligent Spaces. State-of-the-art Local Positioning Systems (LPS) require a complex sensornetwork infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we describe and implement a Kalman-based framework, called INS-EKF-ZUPT (IEZ), to estimate the position and attitude of a person while walking. IEZ makes use of an Extended Kalman filter (EKF), an INS mechanization algorithm, a Zero Velocity Update (ZUPT) methodology, as well as, a stance detection algorithm. As the IEZ methodology is not able to estimate the heading and its drift (non-observable variables), then several methods are used for heading drift reduction: ZARU, HDR and Compass. The main contribution of the paper is the integration of the heading drift reduction algorithms into a Kalman-based IEZ platform, which represents an extended PDR methodology (IEZ+) valid for operation in indoor spaces with local magnetic disturbances. The IEZ+ PDR methodology was tested in several simulated and real indoor scenarios with a low-performance IMU mounted on the foot. The positioning errors were about 1% of the total travelled distance, which are good figures if compared with other works using IMUs of higher performance.

Performance comparison of TOA and TDOA based location estimation algorithms in LOS environment

Abstract: In this paper, various positioning algorithms for range-based TOA and TDOA localization have been analyzed, which include the analytical method, least square method, approximate maximum likelihood method, Taylor series method, two-stage maximum likelihood method and genetic algorithm. The assumed scenario is an overdetermined system in a 3D space under line of sight (LOS) situation and a number of sensor nodes placed arbitrarily across this area. The performance of the algorithms has been compared in the assumed scenario. Both the average error and the failure rate have been investigated in terms of the number of reference nodes and the root mean squared error (RMSE) of the range estimation.

WLAN-Based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors

Abstract: Indoor positioning systems based on wireless LAN (WLAN) are being widely investigated in academia and industry. Meanwhile, the emerging low-cost MEMS sensors can also be used as another independent positioning source. In this paper, we propose a pedestrian tracking framework based on particle filters, which extends the typical WLAN-based indoor positioning systems by integrating low-cost MEMS accelerometer and map information. Our simulation and real world experiments indicate a remarkable performance improvement by using this fusion framework.

Omnidirectional Pedestrian Navigation for First Responders

Abstract: It might be assumed that dead reckoning approaches to pedestrian navigation could address the needs of first responders, including fire fighters. However, conventional PDR approaches with body-mounted motion sensors can fail when used with the irregular walking patterns typical of urban search and rescue missions. In this paper, a technique using shoe-mounted sensors and inertial mechanization equations to directly calculate the displacement of the feet between footfalls is described. Zero-velocity updates (ZUPTs) at foot standstills limit the drift that would otherwise occur with inexpensive IMUs. We show that the technique is fairly accurate in terms of distance travelled and can handle many arbitrary manoevers, such as tight turns, side/back stepping and stair climbing.