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

EXPERIMENTAL EVALUATION OF MACHINE LEARNING ALGORITHMS FOR FINGERPRINTING INDOOR LOCALIZATION

One of the most preferred range-free indoor localization methods is the location fingerprinting. In the fingerprinting localization phase machine learning algorithms have widespread usage in estimating positions of the target node. The real challenge in indoor localization systems is to find out the proper machine learning algorithm. In this paper, three different machine learning algorithms for training the fingerprint database were used. We analysed the localization accuracy depending on a fingerprint density and number of line-of-sight (LOS) anchors. Experiments confirmed that Gaussian processes algorithm is superior to all other machine learning algorithms. The results prove that the localization accuracy can be achieved with a sub-decimetre resolution under typical real-world conditions

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

Bidirectional UWB Localization: A Review on an Elastic Positioning Scheme for GNSS-deprived Zones

A bidirectional Ultra-Wideband (UWB) localization scheme is one of the three widely deployed design integration processes ordinarily destined for time-based UWB positioning systems. The key property of the bidirectional UWB localization is its ability to serve both the navigation and tracking assignments on-demand within a single localization scheme. Conventionally, the perspective of navigation and tracking in wireless localization systems is viewed distinctly as an individual system because different methodologies were required for the implementation process. The ability to flexibly or elastically combine two unique positioning perspectives (i.e., navigation and tracking) within a single scheme is a paradigm shift in the way location-based services are observed. Thus, this article addresses and pinpoints the potential of a bidirectional UWB localization scheme. Regarding this, the complete system model of the bidirectional UWB localization scheme was comprehensively described based on modular processes in this article. The demonstrative evaluation results based on two system integration processes as well as a SWOT (strengths, weaknesses, opportunities, and threats) analysis of the scheme were also discussed. Moreover, we argued that the presented bidirectional scheme can also be used as a prospective topology for the realization of precise location estimation processes in 5G/6G wireless mobile networks, as well as Wi-Fi fine-time measurement-based positioning systems in this article.Comment: 30 pages, 12 figure

Identification and Mitigation of NLOS based on Channel Information Rules for Indoor UWB Localization

Indoor localization is an emerging technology that can be utilized for developing products and services for commercial usage, public safety, military applications and so forth. Commercially it can be applied to track children, people with special needs, help navigate blind people, locate equipment, mobile robots, etc. The objective of this thesis is to enable an indoor mobile vehicle to determine its location and thereby making it capable of autonomous localization under Non-light of sight (NLOS) conditions. The solution developed is based on Ultra Wideband (UWB) based Indoor Positioning System (IPS) in the building. The proposed method increases robustness, scalability, and accuracy of location. The out of the box system of DecaWave TREK1000 provides tag tracking features but has no method to detect and mitigate location inaccuracies due to the multipath effect from physical obstacles found in an indoor environment. This NLOS condition causes ranges to be positively biased, hence the wrong location is reported. Our approach to deal with the NLOS problem is based on the use of Rules Classifier, which is based on channel information. Once better range readings are achieved, approximate location is calculated based on Time of Flight (TOF). Moreover, the proposed rule based IPS can be easily implemented on hardware due to the low complexity. The measurement results, which was obtained using the proposed mitigation algorithm, show considerable improvements in the accuracy of the location estimation which can be used in different IPS applications requiring centimeter level precision. The performance of the proposed algorithm is evaluated experimentally using an indoor positioning platform in a laboratory environment, and is shown to be significantly better than conventional approaches. The maximum positioning error is reduced to 15 cm for NLOS using both an offline and real time tracking algorithm extended from the proposed approach

Indoor Cooperative Localization for Ultra Wideband Wireless Sensor Networks

In recent years there has been growing interest in ad-hoc and wireless sensor networks (WSNs) for a variety of indoor applications. Localization information in these networks is an enabling technology and in some applications it is the main sought after parameter. The cooperative localization performance of WSNs is ultimately constrained by the behavior of the utilized ranging technology in dense cluttered indoor environments. Recently, ultra-wideband (UWB) Time-of-Arrival (TOA) based ranging has exhibited potential due to its large bandwidth and high time resolution. However, the performance of its ranging and cooperative localization capabilities in dense indoor multipath environments needs to be further investigated. Of main concern is the high probability of non-line of sight (NLOS) and Direct Path (DP) blockage between sensor nodes, which biases the TOA estimation and degrades the localization performance. In this dissertation, we first present the results of measurement and modeling of UWB TOA-based ranging in different indoor multipath environments. We provide detailed characterization of the spatial behavior of ranging, where we focus on the statistics of the ranging error in the presence and absence of the DP and evaluate the pathloss behavior in the former case which is important for indoor geolocation coverage characterization. Parameters of the ranging error probability distributions and pathloss models are provided for different environments: traditional office, modern office, residential and manufacturing floor; and different ranging scenarios: indoor-to-indoor (ITI), outdoor-to-indoor (OTI) and roof-to-indoor (RTI). Based on the developed empirical models of UWB TOA-based OTI and ITI ranging, we derive and analyze cooperative localization bounds for WSNs in the different indoor multipath environments. First, we highlight the need for cooperative localization in indoor applications. Then we provide comprehensive analysis of the factors affecting localization accuracy such as network and ranging model parameters. Finally we introduce a novel distributed cooperative localization algorithm for indoor WSNs. The Cooperative LOcalization with Quality of estimation (CLOQ) algorithm integrates and disseminates the quality of the TOA ranging and position information in order to improve the localization performance for the entire WSN. The algorithm has the ability to reduce the effects of the cluttered indoor environments by identifying and mitigating the associated ranging errors. In addition the information regarding the integrity of the position estimate is further incorporated in the iterative distributed localization process which further reduces error escalation in the network. The simulation results of CLOQ algorithm are then compared against the derived G-CRLB, which shows substantial improvements in the localization performance

Mobile Location with NLOS Identification and Mitigation Based on Modified Kalman Filtering

In order to enhance accuracy and reliability of wireless location in the mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a robust mobile location algorithm is presented to track the position of a mobile node (MN). An extended Kalman filter (EKF) modified in the updating phase is utilized to reduce the NLOS error in rough wireless environments, in which the NLOS bias contained in each measurement range is estimated directly by the constrained optimization method. To identify the change of channel situation between NLOS and LOS, a low complexity identification method based on innovation vectors is proposed. Numerical results illustrate that the location errors of the proposed algorithm are all significantly smaller than those of the iterated NLOS EKF algorithm and the conventional EKF algorithm in different LOS/NLOS conditions. Moreover, this location method does not require any statistical distribution knowledge of the NLOS error. In addition, complexity experiments suggest that this algorithm supports real-time applications