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

A Markov Model for Dynamic Behavior of Toa-Based Ranging in Indoor Localization

The existence of undetected direct path ( UDP) conditions causes occurrence of unexpected large random ranging errors which pose a serious challenge to precise indoor localization using time of arrival ( ToA). Therefore, analysis of the behavior of the ranging error is essential for the design of precise ToA-based indoor localization systems. In this paper, we propose a novel analytical framework for the analysis of the dynamic spatial variations of ranging error observed by a mobile user based on an application of Markov chain. the model relegates the behavior of ranging error into four main categories associated with four states of the Markov process. the parameters of distributions of ranging error in each Markov state are extracted from empirical data collected from a measurement calibrated ray tracing ( RT) algorithm simulating a typical office environment. the analytical derivation of parameters of the Markov model employs the existing path loss models for the first detected path and total multipath received power in the same office environment. Results of simulated errors from the Markov model and actual errors from empirical data show close agreement

Impacto del modelo de error en distancia en la simulación de sistemas de localización

Las redes inalámbricas han favorecido enormemente el interés de los usuarios, proveedores de servicio y operadores de red en el posicionamiento geográfico. Como consecuencia, se han propuesto mecanismos en la mayor parte de tecnologías de red inalámbrica con los que soportar la localización de usuarios. La evaluación de calidad ofrecida por dichas técnicas de localización, normalmente en términos de precisión, latencia y escalabilidad, recae en herramientas de simulación. Es esencial por tanto, que los modelos de error empleados en estas herramientas estén acordes a la realidad. Este hecho es si cabe más importante en el caso de emplear técnicas de localización basadas en medida de la distancia a partir de métricas temporales, como son el tiempo de llegada (TOA) o la diferencia entre tiempos de llegada (TDOA). Estas técnicas son especialmente sensibles a no disponer de visión directa entre los distintos elementos involucrados en la localización, por lo que la evaluación de sus capacidades suele hacerse bajo esas condiciones. El presente artículo compara bajo un mismo escenario, diversos modelos de error para las métricas empleadas en técnicas como TOA o TDOA. Los resultados concluyen que los modelos que no tienen en cuenta las distancias reales (que son los más habituales) tienden a proporcionar una estimación optimista el error de posicionamiento, cosa que no ocurre en el caso de modelos más complejos que sí tienen en cuenta esa información.Postprint (published version

Multi-node TOA-DOA cooperative LOS-NLOS localization : enabling high accuracy and reliability

This dissertation investigates high performance cooperative localization in wireless environments based on multi-node time-of-arrival (TOA) and direction-of-arrival (DOA) estimations in line-of-sight (LOS) and non-LOS (NLOS) scenarios. Here, two categories of nodes are assumed: base nodes (BNs) and target nodes (TNs). BNs are equipped with antenna arrays and capable of estimating TOA (range) and DOA (angle). TNs are equipped with Omni-directional antennas and communicate with BNs to allow BNs to localize TNs; thus, the proposed localization is maintained by BNs and TNs cooperation. First, a LOS localization method is proposed, which is based on semi-distributed multi-node TOA-DOA fusion. The proposed technique is applicable to mobile ad-hoc networks (MANETs). We assume LOS is available between BNs and TNs. One BN is selected as the reference BN, and other nodes are localized in the coordinates of the reference BN. Each BN can localize TNs located in its coverage area independently. In addition, a TN might be localized by multiple BNs. High performance localization is attainable via multi-node TOA-DOA fusion. The complexity of the semi-distributed multi-node TOA-DOA fusion is low because the total computational load is distributed across all BNs. To evaluate the localization accuracy of the proposed method, we compare the proposed method with global positioning system (GPS) aided TOA (DOA) fusion, which are applicable to MANETs. The comparison criterion is the localization circular error probability (CEP). The results confirm that the proposed method is suitable for moderate scale MANETs, while GPS-aided TOA fusion is suitable for large scale MANETs. Usually, TOA and DOA of TNs are periodically estimated by BNs. Thus, Kalman filter (KF) is integrated with multi-node TOA-DOA fusion to further improve its performance. The integration of KF and multi-node TOA-DOA fusion is compared with extended-KF (EKF) when it is applied to multiple TOA-DOA estimations made by multiple BNs. The comparison depicts that it is stable (no divergence takes place) and its accuracy is slightly lower than that of the EKF, if the EKF converges. However, the EKF may diverge while the integration of KF and multi-node TOA-DOA fusion does not; thus, the reliability of the proposed method is higher. In addition, the computational complexity of the integration of KF and multi-node TOA-DOA fusion is much lower than that of EKF. In wireless environments, LOS might be obstructed. This degrades the localization reliability. Antenna arrays installed at each BN is incorporated to allow each BN to identify NLOS scenarios independently. Here, a single BN measures the phase difference across two antenna elements using a synchronized bi-receiver system, and maps it into wireless channel’s K-factor. The larger K is, the more likely the channel would be a LOS one. Next, the K-factor is incorporated to identify NLOS scenarios. The performance of this system is characterized in terms of probability of LOS and NLOS identification. The latency of the method is small. Finally, a multi-node NLOS identification and localization method is proposed to improve localization reliability. In this case, multiple BNs engage in the process of NLOS identification, shared reflectors determination and localization, and NLOS TN localization. In NLOS scenarios, when there are three or more shared reflectors, those reflectors are localized via DOA fusion, and then a TN is localized via TOA fusion based on the localization of shared reflectors

Resource-efficient strategies for mobile ad-hoc networking

The ubiquity and widespread availability of wireless mobile devices with ever increasing inter-connectivity (e. g. by means of Bluetooth, WiFi or UWB) have led to new and emerging next generation mobile communication paradigms, such as the Mobile Ad-hoc NETworks (MANETs). MANETs are differentiated from traditional mobile systems by their unique properties, e. g. unpredictable nodal location, unstable topology and multi-hop packet relay. The success of on-going research in communications involving MANETs has encouraged their applications in areas with stringent performance requirements such as the e-healthcare, e. g. to connect them with existing systems to deliver e-healthcare services anytime anywhere. However, given that the capacity of mobile devices is restricted by their resource constraints (e. g. computing power, energy supply and bandwidth), a fundamental challenge in MANETs is how to realize the crucial performance/Quality of Service (QoS) expectations of communications in a network of high dynamism without overusing the limited resources. A variety of networking technologies (e. g. routing, mobility estimation and connectivity prediction) have been developed to overcome the topological instability and unpredictability and to enable communications in MANETs with satisfactory performance or QoS. However, these technologies often feature a high consumption of power and/or bandwidth, which makes them unsuitable for resource constrained handheld or embedded mobile devices. In particular, existing strategies of routing and mobility characterization are shown to achieve fairly good performance but at the expense of excessive traffic overhead or energy consumption. For instance, existing hybrid routing protocols in dense MANETs are based in two-dimensional organizations that produce heavy proactive traffic. In sparse MANETs, existing packet delivery strategy often replicates too many copies of a packet for a QoS target. In addition, existing tools for measuring nodal mobility are based on either the GPS or GPS-free positioning systems, which incur intensive communications/computations that are costly for battery-powered terminals. There is a need to develop economical networking strategies (in terms of resource utilization) in delivering the desired performance/soft QoS targets. The main goal of this project is to develop new networking strategies (in particular, for routing and mobility characterization) that are efficient in terms of resource consumptions while being effective in realizing performance expectations for communication services (e. g. in the scenario of e-healthcare emergency) with critical QoS requirements in resource-constrained MANETs. The main contributions of the thesis are threefold: (1) In order to tackle the inefficient bandwidth utilization of hybrid service/routing discovery in dense MANETs, a novel "track-based" scheme is developed. The scheme deploys a one-dimensional track-like structure for hybrid routing and service discovery. In comparison with existing hybrid routing/service discovery protocols that are based on two-dimensional structures, the track-based scheme is more efficient in terms of traffic overhead (e. g. about 60% less in low mobility scenarios as shown in Fig. 3.4). Due to the way "provocative tracks" are established, the scheme has also the capability to adapt to the network traffic and mobility for a better performance. (2) To minimize the resource utilization of packet delivery in sparse MANETs where wireless links are intermittently connected, a store-and-forward based scheme, "adaptive multicopy routing", was developed for packet delivery in sparse mobile ad-hoc networks. Instead of relying on the source to control the delivery overhead as in the conventional multi-copy protocols, the scheme allows each intermediate node to independently decide whether to forward a packet according to the soft QoS target and local network conditions. Therefore, the scheme can adapt to varying networking situations that cannot be anticipated in conventional source-defined strategies and deliver packets for a specific QoS targets using minimum traffic overhead. ii (3) The important issue of mobility measurement that imposes heavy communication/computation burdens on a mobile is addressed with a set of resource-efficient "GPS-free" soluti ons, which provide mobility characterization with minimal resource utilization for ranging and signalling by making use of the information of the time-varying ranges between neighbouring mobile nodes (or groups of mobile nodes). The range-based solutions for mobility characterization consist of a new mobility metric for network-wide performance measurement, two velocity estimators for approximating the inter-node relative speeds, and a new scheme for characterizing the nodal mobility. The new metric and its variants are capable of capturing the mobility of a network as well as predicting the performance. The velocity estimators are used to measure the speed and orientation of a mobile relative to its neighbours, given the presence of a departing node. Based on the velocity estimators, the new scheme for mobility characterization is capable of characterizing the mobility of a node that are associated with topological stability, i. e. the node's speeds, orientations relative to its neighbouring nodes and its past epoch time. iiiBIOPATTERN EU Network of Excellence (EU Contract 508803

Impacto del modelo de ranging en sistemas TOA/TDOA. Propuestas de mejora

Castellano: Debido a la creciente popularidad de las redes ad hoc, ha habido un incremento en el interés por sus problemas asociados, especialmente el de posicionamiento de sus nodos. Por eso surgen en los últimos tiempos numerosas técnicas de posicionamiento. Dado que la simulación es la forma más común de evaluar el funcionamiento de las técnicas de posicionamiento, es vital la utilización de un modelo apropiado del entorno, con el fin de obtener evaluaciones realistas. Una característica de los entornos wireless de interior es el efecto provocado por la falta de visibilidad directa entre terminales (Non- Line-of-Sight), el cual deteriora la estimación de la posición en esquemas basados en estimaciones de la distancia (ranging) a través de medidas del tiempo de llegada (Timeof- Arrival). En este estudio se comparan mediante simulaciones de Monte Carlo varios modelos del error de ranging para entornos de interior y se investiga su impacto en los resultados de dos técnicas de localización. Los resultados muestran que el valor eficaz del error de posicionamiento sufre un incremento de hasta el 18% del valor eficaz del error de ranging cuando se usan modelos dependientes de la distancia en vez de sus homólogos independientes. Esto indica que la utilización de modelos dependientes de la distancia puede ser útil para evitar evaluaciones demasiado optimistas. El passive-TDOA (passive Time Difference Of Arrival) es una técnica de posicionamiento recientemente publicada, la cual tiene la interesante propiedad de no inyectar tráfico en la red. En este estudio se ha propuesto una serie de modificaciones sobre la técnica original, al objeto de mejorar la precisión obtenida. Dichas modificaciones consisten en la ponderación de las ecuaciones del sistema resultante de aplicar la técnica passive-TDOA, con el objetivo de penalizar aquellas ecuaciones que se suponen menos fiables. Para ello se han tenido en cuenta dos aspectos. Por un lado, se ha considerado que aquellas ecuaciones provenientes de puntos de acceso lejanos incurren en una mayor imprecisión. Por otro lado, se ha asumido que puntos de acceso cercanos entre si se verán afectados por errores de magnitud similar, puesto que las trayectorias seguidas por la señal se vean salpicadas por las mismas fuentes de error. En contra de lo esperado, los resultados obtenidos muestran que no se ha producido una mejora significativa

Impacto del modelo de ranging en sistemas TOA/TDOA. Propuestas de mejora

Castellano: Debido a la creciente popularidad de las redes ad hoc, ha habido un incremento en el interés por sus problemas asociados, especialmente el de posicionamiento de sus nodos. Por eso surgen en los últimos tiempos numerosas técnicas de posicionamiento. Dado que la simulación es la forma más común de evaluar el funcionamiento de las técnicas de posicionamiento, es vital la utilización de un modelo apropiado del entorno, con el fin de obtener evaluaciones realistas. Una característica de los entornos wireless de\ud interior es el efecto provocado por la falta de visibilidad directa entre terminales (Non- Line-of-Sight), el cual deteriora la estimación de la posición en esquemas basados en estimaciones de la distancia (ranging) a través de medidas del tiempo de llegada (Timeof- Arrival). En este estudio se comparan mediante simulaciones de Monte Carlo varios modelos del error de ranging para entornos de interior y se investiga su impacto en los resultados de dos técnicas de localización. Los resultados muestran que el valor eficaz del error de posicionamiento sufre un incremento de hasta el 18% del valor eficaz del error de ranging cuando se usan modelos dependientes de la distancia en vez de sus homólogos independientes. Esto indica que la utilización de modelos dependientes de la distancia puede ser útil para evitar evaluaciones demasiado optimistas. El passive-TDOA (passive Time Difference Of Arrival) es una técnica de posicionamiento recientemente publicada, la cual tiene la interesante propiedad de no inyectar tráfico en la red. En este estudio se ha propuesto una serie de modificaciones sobre la técnica original, al objeto de mejorar la precisión obtenida. Dichas modificaciones consisten en la ponderación de las ecuaciones del sistema resultante de aplicar la técnica passive-TDOA, con el objetivo de penalizar aquellas ecuaciones que se suponen menos fiables. Para ello se han tenido en cuenta dos aspectos. Por un lado, se ha considerado que aquellas ecuaciones provenientes de puntos de acceso lejanos incurren en una mayor imprecisión. Por otro lado, se ha asumido que puntos de acceso cercanos entre si se verán afectados por errores de magnitud similar, puesto que las trayectorias seguidas por la señal se vean salpicadas por las mismas fuentes de error. En contra de lo esperado, los resultados obtenidos muestran que no se ha producido una mejora significativa

Ad-Hoc Personenlokalisierung in Drahtlosen Sensornetzwerken

In der Arbeit wird ein neues Konzept zur ad-hoc Personenlokalisierung entwickelt und untersucht. Ansätze aus dem Bereich der Lokalisierung in selbstkonfigurierenden, drahtlosen Sensornetzwerken sowie aus dem Bereich der inertialsensorbasierten Personennavigation werden verwendet und zu einem hybriden Lokalisierungsansatz kombiniert. Eine umfangreiche, experimentelle Studie wird durchgeführt. Im Ergebnis wird ein Ansatz aufgezeigt, wie sich Personen in ad-hoc Szenarien lokalisieren lassen

Algorithms for Positioning with Nonlinear Measurement Models and Heavy-tailed and Asymmetric Distributed Additive Noise

Determining the unknown position of a user equipment using measurements obtained from transmitters with known locations generally results in a nonlinear measurement function. The measurement errors can have a heavy-tailed and/ or skewed distribution, and the likelihood function can be multimodal.A positioning problem with a nonlinear measurement function is often solved by a nonlinear least squares (NLS) method or, when filtering is desired, by an extended Kalman filter (EKF). However, these methods are unable to capture multiple peaks of the likelihood function and do not address heavy-tailedness or skewness. Approximating the likelihood by a Gaussian mixture (GM) and using a GM filter (GMF) solves the problem. The drawback is that the approximation requires a large number of components in the GM for a precise approximation, which makes it unsuitable for real-time positioning on small mobile devices.This thesis studies a generalised version of Gaussian mixtures, which is called GGM, to capture multiple peaks. It relaxes the GM’s restriction to non-negative component weights. The analysis shows that the GGM allows a significant reduction of the number of required Gaussian components when applied for approximating the measurement likelihood of a transmitter with an isotropic antenna, compared with the GM. Therefore, the GGM facilitates real-time positioning in small mobile devices. In tests for a cellular telephone network and for an ultra-wideband network the GGM and its filter provide significantly better positioning accuracy than the NLS and the EKF.For positioning with nonlinear measurement models, and heavytailed and skewed distributed measurement errors, an Expectation Maximisation (EM) algorithm is studied. The EM algorithm is compared with a standard NLS algorithm in simulations and tests with realistic emulated data from a long term evolution network. The EM algorithm is more robust to measurement outliers. If the errors in training and positioning data are similar distributed, then the EM algorithm yields significantly better position estimates than the NLS method. The improvement in accuracy and precision comes at the cost of moderately higher computational demand and higher vulnerability to changing patterns in the error distribution (of training and positioning data). This vulnerability is caused by the fact that the skew-t distribution (used in EM) has 4 parameters while the normal distribution (used in NLS) has only 2. Hence the skew-t yields a closer fit than the normal distribution of the pattern in the training data. However, on the downside if patterns in training and positioning data vary than the skew-t fit is not necessarily a better fit than the normal fit, which weakens the EM algorithm’s positioning accuracy and precision. This concept of reduced generalisability due to overfitting is a basic rule of machine learning.This thesis additionally shows how parameters of heavy-tailed and skewed error distributions can be fitted to training data. It furthermore gives an overview on other parametric methods for solving the positioning method, how training data is handled and summarised for them, how positioning is done by them, and how they compare with nonparametric methods. These methods are analysed by extensive tests in a wireless area network, which shows the strength and weaknesses of each method