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

Employing Ray-tracing and Least-Squares Support Vector Machines for Localisation

This article evaluates the use of least-squares support vector machines, with ray-traced data, to solve the problem of localisation in multipath environments. The schemes discussed concern 2-D localisation, but could easily be extended to 3-D. It does not require NLOS identification and mitigation, hence, it can be applied in any environment. Some background details and a detailed experimental setup is provided. Comparisons with schemes that require NLOS identification and mitigation, from earlier work, are also presented. The results demonstrate that the direct localisation scheme using least-squares support vector machine (the Direct method) achieves superior outage to TDOA and TOA/AOA for NLOS environments. TDOA has better outage in LOS environments. TOA/AOA performs better for an accepted outage probability of 20 percent or greater but as the outage probability lowers, the Direct method becomes better

Feature-Based Generalized Gaussian Distribution Method for NLoS Detection in Ultra-Wideband (UWB) Indoor Positioning System

Nonline-of-sight (NLoS) propagation condition is a crucial factor affecting the precision of the localization in the ultra-wideband (UWB) indoor positioning system (IPS). Numerous supervised machine learning (ML) approaches have been applied for the NLoS identification to improve the accuracy of the IPS. However, it is difficult for existing ML approaches to maintain a high classification accuracy when the database contains a small number of NLoS signals and a large number of line-of-sight (LoS) signals. The inaccurate localization of the target node caused by this small number of NLoS signals can still be problematic. To solve this issue, we propose feature-based Gaussian distribution (GD) and generalized GD (GGD) NLoS detection algorithms. By employing our detection algorithm for the imbalanced dataset, a classification accuracy of 96.7% and 98.0% can be achieved. We also compared the proposed algorithm with the existing cutting edge, such as support vector machine (SVM), decision tree (DT), naive Bayes (NB), and neural network (NN), which can achieve an accuracy of 92.6%, 92.8%, 93.2%, and 95.5%, respectively. The results demonstrate that the GGD algorithm can achieve high classification accuracy with the imbalanced dataset. Finally, the proposed algorithm can also achieve a higher classification accuracy for different ratios of LoS and NLoS signals, which proves the robustness and effectiveness of the proposed method

Two Novel Methods for Accurate NLOS Detection Based on Channel Statistics

ABSTRACT Time-of-arrival (TOA) estimation is the first step of the most positioning algorithms. However in various environments especially when ultra wideband (UWB) pulses are used, TOA extraction from the received signal is challenging. UWB radio propagation bears multipath phenomenon, therefore correct identification of the first path TOA highly depends on the statistical characteristics of the environment and apprehension that the signal has been passed through the line-of-sight (LOS) channel or the non-line-of-sight (NLOS) one. In this paper, two novel NLOS identification techniques based on the multipath channel statistics are proposed. Simulations show that the first technique using the fitness equations of mean and variance of the received signal is suitable for residential and outdoor environments. The other one that compares the relative energy of two different periods of the received signal is more accurate in office and industrial environments. IEEE 802.15.4a channel models are used and two hypothesis tests are applied to distinguish between LOS and NLOS. The high accuracy identification of channel type is achieved for all mentioned environments

UWB sensor based indoor LOS/NLOS localization with support vector machine learning

Ultra-wideband (UWB) sensor technology is known to achieve high-precision indoor localization accuracy in line-of-sight (LOS) environments, but its localization accuracy and stability suffer detrimentally in non-line-of-sight (NLOS) conditions. Current NLOS/LOS identification based on channel impulse response’s (CIR) characteristic parameters (CCP) improves location accuracy, but most CIR-based identification approaches did not sufficiently exploit the CIR information and are environment specific. This paper derives three new CCPs and proposes a novel two-step identification/classification methodology with dynamic threshold comparison (DTC) and the fuzzy credibility-based support vector machine (FC-SVM). The proposed SVM based classification methodology leverages on the derived CCPs obtained from the waveform and its channel analysis, which are more robust to environment and obstacles dynamic. This is achieved in two-step with a coarse-grained NLOS/LOS identification with the DTC strategy followed by FC-SVM to give the fine-grained result. Finally, based on the obtained identification results, a real-time ranging error mitigation strategy is then designed to improve the ranging and localization accuracy. Extensive experimental campaigns are conducted in different LOS/NLOS scenarios to evaluate the proposed methodology. The results show that the mean LOS/NLOS identification accuracy in various testing scenarios is 93.27 %, and the LOS and NLOS recalls are 94.27 % and 92.57 %, respectively. The ranging errors in LOS(NLOS) conditions are reduced from 0.106 m(1.442 m) to 0.065 m(0.739 m), demonstrating an improvement of 38.85 %(48.74 %) with 0.041 m(0.703 m) error reduction. On the other hand, the average positioning accuracy is also reduced from 0.250 m to 0.091 m with an improvement of 63.49 %(0.159 m), which outperforms the state-of-the-art approaches of the Least-squares support vector machine (LS-SVM) and K-Nearest Neighbor (KNN) algorithms