Robust localization in cluttered environments with NLOS propagation

Nawaz, Syed Junaid; Trigoni, Niki

doi:10.1109/mass.2010.5663983

Cited by 23 publications

(16 citation statements)

References 15 publications

(14 reference statements)

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“…To initialize the state vector of the nonlinear filters (UKF and DDKF ), the initial positions are calculated using a multilateration approach, which provides estimates of the x and y positions, from distances obtained using the propagation model [17]. The velocities v x and v y are initialized to zero assuming no prior knowledge about the movement speed of the mobile node.…”

Section: Simulation Resultsmentioning

confidence: 99%

Mobile Localization And Tracking With Los And Nlos Identification In Wireless Sensor Networks

Benkouider

Keche

2016

International Journal on Smart Sensing and Intelligent Systems

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Section: Simulation Resultsmentioning

confidence: 99%

Mobile Localization And Tracking With Los And Nlos Identification In Wireless Sensor Networks

Benkouider

Keche

2016

International Journal on Smart Sensing and Intelligent Systems

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“…Some researchers begin to explore new methods without NLOS identification. Typical algorithms include least square multi-lateration [30], optimized residual weighting [31], support vector machine classifier [11], and machine learning [32]. These methods often require ideal synchronization or massive experimental data.…”

Section: Related Workmentioning

confidence: 99%

Moving target localization in indoor wireless sensor networks mixed with LOS/NLOS situations

Zhang

Wei

et al. 2013

J Wireless Com Network

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Indoor target localization is an essential and fundamental issue for wireless sensor networks (WSN). However, it is rather difficult for WSN to maintain the localization accuracy in line-of-sight (LOS) and non-line-of-sight (NLOS) mixed environment. NLOS propagation always leads to larger ranging error than LOS does. When the target moves in the rooms and corridors, the signal transmission state will switch frequently between LOS and NLOS. It is a challenging task to deal with this situation because the ranging error characteristics under LOS and NLOS conditions are quite different. In this paper, we propose an interacting multiple model-extended Kalman filter (IMM-EKF) algorithm to improve the localization accuracy for moving target in indoor environment. In the IMM structure, two Kalman filters (KF) are adopted in parallel to accurately smoothen the distance measurement. The proposed algorithm can adapt to the dynamically changing condition between LOS and NLOS due to the two KFs' interaction so that large NLOS ranging errors are further reduced. Once the estimated ranges are obtained, the EKF is employed to estimate the target's location. Empirical measurement results are obtained from typical office environment to verify the effectiveness of the proposed algorithm. Experimental results illustrate that the IMM smoother can efficiently mitigate the NLOS effects on ranging errors and achieve high localization accuracy.

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“…Non Line of Sight (NLoS) errors with different size of object and position can be between 10% and 100% [12]. It is also known that the traditional least squares multilateration algorithm delivers inaccurate results if there is a single error in measured distances [13]. For example [12] used the least median of squares (LMedS) which delivers improved performance in case of NLOS signals.…”

Section: Related Workmentioning

confidence: 99%

A Self-Contained and Self-Checking LPS with High Accuracy

Walter

Syafrudin

Schweinzer

2013

IJGI

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There are many situations in which it is desirable to use a Local Positioning System (LPS), which constitutes a complete and independent unit, offers high accuracy and in addition is economical to realize. This paper describes the LPS LOSNUS (Localization of Sensor Nodes by Ultra Sound). LOSNUS is a complete and independent LPS where the same system can be used for localization and calibration. Primarily designed for locating numerous quasi-static devices, special care of system construction has taken on costly factors, especially in the construction of the infrastructure and of sensor nodes where locating can be realized with minimal additional hardware costs. LOSNUS enables a calibration process without the need of additional expensive tools and/or laborious time in order to get accurate positions of transmitters. As a result, LOSNUS delivers high locating accuracy at medium update rates, and in case of sufficient number of transmitters can also tolerate single failures in the Time of Arrival (ToA) measurement, allowing arbitrary failure modes. In this article, the system is presented starting from design, realization and algorithms of localization and calibration. Finally, new measurement results are showing the high accuracy of localization based on a discussion of the applied uncertainty description.

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Robust localization in cluttered environments with NLOS propagation

Cited by 23 publications

References 15 publications

Mobile Localization And Tracking With Los And Nlos Identification In Wireless Sensor Networks

Mobile Localization And Tracking With Los And Nlos Identification In Wireless Sensor Networks

Moving target localization in indoor wireless sensor networks mixed with LOS/NLOS situations

A Self-Contained and Self-Checking LPS with High Accuracy

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