A survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

Statistical Characterization and Mitigation of NLOS Errors in UWB Localization Systems

In this paper some new experimental results about the statistical characterization of the non-line-of-sight (NLOS) bias affecting time-of-arrival (TOA) estimation in ultrawideband (UWB) wireless localization systems are illustrated. Then, these results are exploited to assess the performance of various maximum-likelihood (ML) based algorithms for joint TOA localization and NLOS bias mitigation. Our numerical results evidence that the accuracy of all the considered algorithms is appreciably influenced by the LOS/NLOS conditions of the propagation environment

CIR Parametric Rules Precocity For Ranging Error Mitigation In IR-UWB

The cutting-edge technology to support high ranging accuracy within the indoor environment is Impulse Radio Ultra Wide Band (IR-UWB) standard. Besides accuracy, IR-UWB’s low-complex architecture and low power consumption align well with mobile devices. A prime challenge in indoor IR-UWB based localization is to achieve a position accuracy under non-line-of-sight (NLOS) and multipath propagation (MPP) conditions. Another challenge is to achieve acceptable accuracy in the conditions mentioned above without any significant increase in latency and computational burden. This dissertation proposes a solution for addressing the accuracy and reliability problem of indoor localization system satisfying acceptable delay or computational complexity overhead. The proposed methodology is based on rules for identification of line-of-sight (LOS) and NLOS and the range error bias estimation and correction due to NLOS and MPP conditions. The proposed methodology provides accuracy for two major application domains, namely, wireless sensor networks (WSNs) and indoor tracking and navigation (ITN). This dissertation offers two different solutions for the localization problem. The first solution is a rules-based classification of LOS / NLOS and geometric-based range correction for WSN. In the first solution, the Boolean logic based classification is designed for identification of LOS/NLOS. The logic is based on channel impulse response (CIR) parameters. The second solution is based on fuzzy logic. The fuzzy based solution is appealing well for the stringent precision requirements in ITN. In this solution, the parametric Boolean logic from the first solution is converted and expanded into rules. These rules are implemented into a fuzzy logic based mechanism for designing a fuzzy inference system. The system estimates the ranging errors and correcting unmitigated ranges. The expanded rules and designed methodology are based on theoretical analysis and empirical observations of the parameters. The rules accommodate the parameters uncertainties for estimating the ranging error through the relationship between the input parameters uncertainties and ranging error using fuzzy inference mechanism. The proposed solutions are evaluated using real-world measurements in different indoor environments. The performance of the proposed solutions is also evaluated in terms of true classification rate, residual ranging errors’ cumulative distributions and probability density distributions, as well as outage probabilities. Evaluation results show that the true classification rate is more than 95%. Moreover, using the proposed fuzzy logic based solution, the residual errors convergence of 90% is attained for error threshold of 10 cm, and the reliability of the localization system is also more than 90% for error threshold of 15 cm

Map-Aware Models for Indoor Wireless Localization Systems: An Experimental Study

The accuracy of indoor wireless localization systems can be substantially enhanced by map-awareness, i.e., by the knowledge of the map of the environment in which localization signals are acquired. In fact, this knowledge can be exploited to cancel out, at least to some extent, the signal degradation due to propagation through physical obstructions, i.e., to the so called non-line-of-sight bias. This result can be achieved by developing novel localization techniques that rely on proper map-aware statistical modelling of the measurements they process. In this manuscript a unified statistical model for the measurements acquired in map-aware localization systems based on time-of-arrival and received signal strength techniques is developed and its experimental validation is illustrated. Finally, the accuracy of the proposed map-aware model is assessed and compared with that offered by its map-unaware counterparts. Our numerical results show that, when the quality of acquired measurements is poor, map-aware modelling can enhance localization accuracy by up to 110% in certain scenarios.Comment: 13 pages, 11 figures, 1 table. IEEE Transactions on Wireless Communications, 201

RFI Identification and Mitigation Using Simultaneous Dual Station Observations

RFI mitigation is a critically important issue in radio astronomy using existing instruments as well as in the development of next-generation radio telescopes, such as the Square Kilometer Array (SKA). Most designs for the SKA involve multiple stations with spacings of up to a few thousands of kilometers and thus can exploit the drastically different RFI environments at different stations. As demonstrator observations and analysis for SKA-like instruments, and to develop RFI mitigation schemes that will be useful in the near term, we recently conducted simultaneous observations with Arecibo Observatory and the Green Bank Telescope (GBT). The observations were aimed at diagnosing RFI and using the mostly uncorrelated RFI between the two sites to excise RFI from several generic kinds of measurements such as giant pulses from Crab-like pulsars and weak HI emission from galaxies in bands heavily contaminated by RFI. This paper presents observations, analysis, and RFI identification and excision procedures that are effective for both time series and spectroscopy applications using multi-station data.Comment: 12 pages, 9 figures (4 in ps and 5 in jpg formats), Accepted for publication in Radio Scienc

Distributed AOA-based source positioning in NLOS with sensor networks

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper focuses on the problem of positioning a source using angle-of-arrival measurements taken by a wireless sensor network in which some of the nodes experience non lineof-sight (LOS) propagation conditions. In order to mitigate the errors induced by the nodes in NLOS, we derive an algorithm that combines the expectation-maximization algorithm with a weighted least-squares estimation of the source position so that the nodes in NLOS are eventually identified and discarded. Moreover, a distributed version of this algorithm based on a diffusion strategy that iteratively refines the position estimate while driving the network to a consensus is presented.Peer ReviewedPostprint (author's final draft

CIRNN: An Ultra-Wideband Non-Line-of-Sight Signal Classifier Based on Deep-Learning

Non-line-of-sight (NLOS) error is the main factor that reduces indoor positioning accuracy. Identifying NLOS signals and eliminating NLOS errors are the keys to improving indoor positioning accuracy. To better identify NLOS signals, a multi-stream model channel-impulse-response-neural-network (CIRNN) was proposed. The inputs of CIRNN include the channel impulse response (CIR) and a small number of channel parameters. To make a more obvious comparison between NLOS signals and line-of-sight (LOS) signals, a new energy normalization method is proposed. Fusing multi-dimensional features, the CIRNN network has a good convergence performance and shows stronger sensitivity to NLOS signals. Experimental results show that the CIRNN achieves the best accuracy on the open-source data set, the F1 score is 89.3%. At the same time, the working efficiency of CIRNN meets industry needs, CIRNN can refresh the target position at about 92.6 Hz per second