A reference system for indoor localization testbeds

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

2014

Self Cite

It is hard to obtain a general error model for range-based wireless indoor target tracking system due to the complicated hybrid LOS/NLOS environment. In this paper, we employ a dynamic Gaussian model (DGM) to describe the indoor ranging error. A general Gaussian distribution is constructed firstly. The instantaneous LOS or NLOS error at a typical time is considered as the drift from this general distribution dynamically. Based on this modeling method, we propose an adaptive likelihood method of particle filter. Our method is adaptable for dynamic environment and achieves accurate estimation. The simulation and real indoor experiment demonstrate that the estimation accuracy of our algorithm is greatly improved without imposing computational complexity Index Terms-indoor target tracking, adaptive likelihood, particle filter, dynamic Gaussian model.

Section: Real Indoor Target Tracking Resultsmentioning

confidence: 99%

An adaptive likelihood fusion method using dynamic Gaussian model for indoor target tracking

Zhao

Yang

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

2014

Self Cite

“…We previously presented a reference system for indoor localization testbeds [1]. A low-cost TurtleBot [5] robot carried the system under test while directed remotely through hallways in our office-like building.…”

Section: A Reference Systemmentioning

confidence: 99%

“…If appropriate hardware components are available, an analysis in realworld scenarios is possible but often very time consuming, since ground truth data is needed to properly evaluate the localization algorithm. We presented a reference system [1] which is based on a remote controlled mobile robot and capable of performing independent localization in a 2D indoor environment with an average error of 6.7 cm. We showed that it is easily feasible to record both, ground truth data and data from a system under test, which is carried by the reference system.…”

Section: Introductionmentioning

confidence: 99%

“…We present such a virtual testbed, based on data acquired by our previously presented reference system [1] and, as a system under test, a modified version of the Modular Sensor Board (MSB) A2 [3] node which is equipped with a Nanotron nanoPAN 5375 [4] transceiver. This hardware enables the sensor nodes to measure inter-node ranges using time-of-flight in the 2.4 GHz frequency band.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A virtual indoor localization testbed for Wireless Sensor Networks

Will

Hillebrandt

2013 IEEE International Conference on Sensing, Communications and Networking (SECON)

2013

Self Cite

We present a novel, easy to use virtual testbed enabling researchers to evaluate their localization algorithms based on distance measurements in indoor environments. We provide precise ground truth information collected by our previously presented reference system, based on a mobile robot, in combination with range measurements from a Wireless Sensor Network (WSN) in multiple buildings. The user can define a virtual experiment using our datasets over the web. This approach separates the process of designing a robust indoor localization algorithm from the need for testing it on actual hardware in different scenarios.

“…In this system, we deployed 17 wireless sensor nodes either along the corridor or in the offices of our research building. A robot carrying a sensor node as the mobile target moving along the corridor of the building with constant speed while recording its own positions [19]. The error of record positions is less than 15 cm, which can be seen as a the actual positions.…”

Section: The Real Indoor Experiments Test-bedmentioning

confidence: 99%

Likelihood adaptation of particle filter for target tracking using wireless sensor networks

Zhao

Yang

2013 IEEE Global Communications Conference (GLOBECOM)

2013

Self Cite

The accuracy of particle filtering estimation for the tracking system is prone to be influenced by the high measurement noise (or errors) in wireless sensor networks (WSNs). We first analyze the impact of instantaneous measurement noise which is introduced into the likelihood function and biases the particle filtering estimation. Based on our analysis, we propose a likelihood adaptation method considering the prior information of measurement and introduce a belief factor θ, which is a tuning parameter for adaptation. The optimal θ is attained by deriving the minimum Kullback-Leibler divergence. We integrate our adaptation method with bootstrap particle filter for timeof-arrival based target tracking. The simulation and experiment results of demonstrate that our likelihood adaptation method has greatly improved the estimation performance of particle filter in a high noise environment.Index Terms-Particle filter, wireless sensor network, likelihood adaptation, Kullback-Leibler divergence.