Hybrid cargo-level tracking system for logistics

In this paper, we propose a hybrid cargo-level tracking system for logistics. We highlight the special system requirements, discuss the design issues and identify the design principles. Then we propose an innovative hybrid system. As far as we know, this is the first system that exploits both infrastructure-based and infrastructure-less positioning schemes for practical cargo-level tracking. Compared with existing systems, the proposed system provides a ubiquitous cargo-level tracking solution with enhanced availability, reliability, and lower total costs. © 2010 IEEE.published_or_final_versionThe 71st IEEE Vehicular Technology Conference (VTC2010-Spring), Taipei, Taiwan, 16-19 May 2010. In Proceedings of 71st IEEE-VTC (Spring), 2010, p. 1-

Cooperatively Extending the Range of Indoor Localisation

̶Whilst access to location based information has been mostly possible in the\ud outdoor arena through the use of GPS, the provision of accurate positioning estimations and\ud broad coverage in the indoor environment has proven somewhat problematic to deliver.\ud Considering more time is spent in the indoor environment, the requirement for a solution is\ud obvious. The topography of an indoor location with its many walls, doors, pillars, ceilings\ud and floors etc. muffling the signals to \from mobile devices and their tracking devices, is one\ud of the many barriers to implementation. Moreover the cha racteristically noisy behaviour of\ud wireless devices such as Bluetooth headsets, cordless phones and microwaves can cause\ud interference as they all operate in the same band as Wi -Fi devices. The limited range of\ud tracking devices such as Wireless Access Point s (AP), and the restrictions surrounding their\ud positioning within a buildings’ infrastructure further exacerbate this issue, these difficulties\ud provide a fertile research area at present.\ud The genesis for this research is the inability of an indoor location based system (LBS) to\ud locate devices beyond the range of the fixed tracking devices. The hypothesis advocates a\ud solution that extends the range of Indoor LBS using Mobile Devices at the extremities of\ud Cells that have a priori knowledge of their location, and utilizing these devices to ascertain\ud the location of devices beyond the range of the fixed tracking device. This results in a\ud cooperative localisation technique where participating devices come together to aid in the\ud determination of location of device s which otherwise would be out of scope

RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained

Cooperatively extending the range of indoor localisation

Whilst access to location based information has been mostly possible in the outdoor arena through the use of GPS, the provision of accurate positioning estimations and broad coverage in the indoor environment has proven somewhat problematic to deliver. Considering more time is spent in the indoor environment, the requirement for a solution is obvious. The topography of an indoor location with its many walls, doors, pillars, ceilings and floors etc. muffling the signals to from mobile devices and their tracking devices, is one of the many barriers to implementation. Moreover the characteristically noisy behaviour of wireless devices such as Bluetooth headsets, cordless phones and microwaves can cause interference as they all operate in the same band as Wi-Fi devices. The limited range of tracking devices such as Wireless Access Points (AP), and the restrictions surrounding their positioning within a buildings' infrastructure further exacerbate this issue, these difficulties provide a fertile research area at present. The genesis for this research is the inability of an indoor location based system (LBS) to locate devices beyond the range of the fixed tracking devices. The hypothesis advocates a solution that extends the range of Indoor LBS using Mobile Devices at the extremities of Cells that have a priori knowledge of their location, and utilizing these devices to ascertain the location of devices beyond the range of the fixed tracking device. This results in a cooperative localisation technique where participating devices come together to aid in the determination of location of devices which otherwise would be out of scope

WiFiPoz -- an accurate indoor positioning system

Location based services are becoming an important part of life. Wide adoption of GPS in mobile devices combined with cellular networks has practically solved the problem of outdoor localization needs. The problem of locating an indoor user has being studied only recently. Much research contributed to the innovative concept of an indoor positioning system. By analyzing different technologies and algorithms, this thesis concluded that, considering a trade-off between accuracy and cost, a Wi-Fi based Fingerprint method is proved to be the most promising approach to determine the location of a mobile device. However, the Fingerprint method works in two phases-an offline training phase (collection of Received Signal Strength signatures) and an online phase in which data from the first phase is used to determine the current position of a mobile user. The number of training points in a certain area has a direct impact on the accuracy of the system. As a result, the offline phase is a tedious and cumbersome process and the positioning systems are only as accurate as the offline training phase has been detailed. Moreover, the offline phase must be repeated every time a change in the environment occurs. To avoid these limitations, we focus on improving the accuracy of the indoor positioning system, without increasing the number of training points. This thesis presents a Wi-Fi based system for locating a user inside a building. The system is named WiFiPoz, which means Wi-Fi positioning system based on the zoning method. WiFiPoz has a novel approach to Fingerprint method that incorporates Propagation and zoning methods. Experimental results show that WiFiPoz is highly efficient both in accuracy and costs. Compared to traditional Fingerprint methods, with the optimization of the accuracy of the location estimation, WiFiPoz reduces the number of training points. This feature makes it possible to quickly adapt to changes in the environment. In order to explore another possible solution, this thesis also developed, implemented and tested an indoor positioning system named GIS (Geometric Information based positioning System), which is based on a model proposed by another researcher. Several experiments were run in the offline phase and results were compared between the traditional Fingerprint method, GIS and proposed WiFiPoz. We concluded that WiFiPoz is a more efficient and simple way to increase the accuracy of the location determination with fewer training points --Document

Source localization via time difference of arrival

Accurate localization of a signal source, based on the signals collected by a number of receiving sensors deployed in the source surrounding area is a problem of interest in various fields. This dissertation aims at exploring different techniques to improve the localization accuracy of non-cooperative sources, i.e., sources for which the specific transmitted symbols and the time of the transmitted signal are unknown to the receiving sensors. With the localization of non-cooperative sources, time difference of arrival (TDOA) of the signals received at pairs of sensors is typically employed. A two-stage localization method in multipath environments is proposed. During the first stage, TDOA of the signals received at pairs of sensors is estimated. In the second stage, the actual location is computed from the TDOA estimates. This later stage is referred to as hyperbolic localization and it generally involves a non-convex optimization. For the first stage, a TDOA estimation method that exploits the sparsity of multipath channels is proposed. This is formulated as an f1-regularization problem, where the f1-norm is used as channel sparsity constraint. For the second stage, three methods are proposed to offer high accuracy at different computational costs. The first method takes a semi-definite relaxation (SDR) approach to relax the hyperbolic localization to a convex optimization. The second method follows a linearized formulation of the problem and seeks a biased estimate of improved accuracy. A third method is proposed to exploit the source sparsity. With this, the hyperbolic localization is formulated as an an f1-regularization problem, where the f1-norm is used as source sparsity constraint. The proposed methods compare favorably to other existing methods, each of them having its own advantages. The SDR method has the advantage of simplicity and low computational cost. The second method may perform better than the SDR approach in some situations, but at the price of higher computational cost. The l1-regularization may outperform the first two methods, but is sensitive to the choice of a regularization parameter. The proposed two-stage localization approach is shown to deliver higher accuracy and robustness to noise, compared to existing TDOA localization methods. A single-stage source localization method is explored. The approach is coherent in the sense that, in addition to the TDOA information, it utilizes the relative carrier phases of the received signals among pairs of sensors. A location estimator is constructed based on a maximum likelihood metric. The potential of accuracy improvement by the coherent approach is shown through the Cramer Rao lower bound (CRB). However, the technique has to contend with high peak sidelobes in the localization metric, especially at low signal-to-noise ratio (SNR). Employing a small antenna array at each sensor is shown to lower the sidelobes level in the localization metric. Finally, the performance of time delay and amplitude estimation from samples of the received signal taken at rates lower than the conventional Nyquist rate is evaluated. To this end, a CRB is developed and its variation with system parameters is analyzed. It is shown that while with noiseless low rate sampling there is no estimation accuracy loss compared to Nyquist sampling, in the presence of additive noise the performance degrades significantly. However, increasing the low sampling rate by a small factor leads to significant performance improvement, especially for time delay estimation

Wireless Positioning Applications in Multipath Environments

Funklokalisierung in der Umgebung mit der Mehrwegeausbreitung In den vergangenen Jahren wurde zunehmend Forschung im Bereich drahtlose Sensornetzwerk (engl. „Wireless Sensor Network“) betrieben. Lokalisierung im Innenraum ist ein vielversprechendes Forschungsthema, das in den Literaturen vielfältig diskutiert wird. Jedoch berücksichtigen die meisten Arbeiten einen wichtigen Faktor nicht, nämlich die Mehrwegeausbreitung, welche die Genauigkeit der Lokalisierung beeinflusst. Diese Arbeit bezieht sich auf Lokalisierungsanwendungen in UWB (Ultra-Breitband-Technologie)- und WLAN (drahtloses lokales Netzwerk)- Systemen im Fall von Mehrwegeausbreitung. Zur Steigerung der Robustheit der Lokalisierungsanwendungen bei Mehrwegeausbreitung wurden neuartige Lokalisierungsalgorithmen, die auf der Auswertung der Ankunftszeit (engl. „Time of Arrival“, ToA), der empfangenen Signalstärke (engl. „Received Signal Strength“, RSS) und dem Einfallswinkel (engl. „Angle of Arrival“, AoA) basieren, vorgestellt und untersucht. Bei Mehrwegeausbreitung ist die Fragen den direkten Pfad zu lösen, da der direkte Pfad (engl. „Direct Path“, DP) schwächer als anderer Pfad sein kann. In dieser Arbeit werden daher neuartige Algorithmen zur Flankendetektion der empfangenen Signale für UWB Systeme entwickelt, um die Positionsbestimmung zu verbessern: Es gibt die kooperative Flankendetektion (engl. „Joint Leading Edge Detection“, JLED), die erweiterte maximalwahrscheinlichkeitbasierte Kanalschätzung (engl. „Improved Maximum Likelihood Channel Estimation“, IMLCE) und die Flankendetektion mit untervektorraumbasiertem Verfahren (engl. „Subspace based Approaches“, SbA). Bei der kooperativen Flankendetektion werden zwei Kriterien herangezogen nämlich die minimale Fläche und das minimale mittlere Quadrat des Schätzfehlers (engl. „Minimum Mean Squared Error“, MMSE). Weiterhin wird ein monopulsbasierter Kanalschätzer (engl. „Monopulse based Channel Estimator“, MCE) entwickelt, um die möglicherweise falsche Kombinationen der Flanken (engl. „Leading Edge Combination“, LEC) auszuschließen. Zudem wird in der Arbeit der erweiterte MLCE vorgestellt, der aus einem groben und einem genauen Schätzungsschritt besteht. Bei dem neuartigen untervektorraumbasierten Verfahren werden ein statischer und ein Schwundkanal untersucht. Im ersten Fall wird die Kombination der Rückwärtssuchalgorithmus mit untervektorraumbasierten Verfahren untersucht. Zudem wird im zweiten Fall ein untervektorraumbasierte Verfahren im Frequenzbereich vorgestellt. Für die RSS-basierte Lokalisierung wird ein Fingerabdruckverfahren (engl. „Fingerprint Approach“) und ein neuartiger Entfernungsschätzer basierend auf der Kanalenergie entwickelt und implementiert. Schließlich wird in der Arbeit ein Lokalisierungssystem mit Winkelschätzern inklusive einer entsprechenden Kalibrierung auf einer 802.11a/g Hardwareplattform vorgestellt. Dazu wird ein neuartiger Trägerschätzer und Kanalschätzer entwickelt.In the past several years there has been more growing research on Wireless Sensor Network (WSN). The indoor localization is a promising research topic, which is discussed variously in some literatures. However, the most work does not consider an important factor, i.e. the multi-path propagation, which affects the accuracy of the indoor localization. This work dealt with the indoor localization applied in UWB (Ultra Wide Band) and WLAN (Wireless Local Area Network) systems in the case of multi-path propagation. To improve the robustness of the applications of localization in the case of multi-path propagation, novel localization algorithms based on the evaluation of the Time of Arrival (ToA), the Received Signal Strength (RSS) and the Angle of Arrival (AoA) were proposed and investigated. In the ToA based localization systems, the detection of shortest signal propagation time plays a critical role. In the case of multi-path propagation, the Direct Path (DP) needs to be resolved because the DP may be weaker than Multi Path Components (MPC). Thus the novel algorithms for leading edge detection were developed in this work in order to improve the accuracy of localization, namely Joint Leading Edge Detection (JLED), Improved Maximum Likelihood Channel Estimation (IMLCE) and the leading edge detection with Subspace based Approaches (SbA). Two criteria were proposed and referenced for the JLED, namely Minimum Area (MA) and Minimum Mean Squared Error (MMSE). Furthermore, a monocycle-based channel estimator was developed to mitigate the fake LECs (Leading Edge Combination). The estimation error of JLED was theoretically analyzed and simulated for evaluation of the estimator. IMLCE consists of a coarse and a fine estimation step. The coarse position of the first correlation peak shall be found with the Search Back Algorithms (SBA), which is followed by MLCE-algorithms. The novel SbA was investigated in a static and a fading channel. In the former case, the iterative algorithm, which combines SbA with SBA, was investigated. In the latter case, the FD-SbA (Frequency Domain - SbA) was proposed, which requires to calculate the covariance matrix in the FD. For the RSS based localization, fingerprint approach and the novel channel energy based distance estimator were investigated and developed in this dissertation. Finally, a localization system using AoA estimation and the initial calibration was presented on an 802.11a/g hardware platform. A novel Carrier Frequency Offset (CFO) estimator and channel estimator were investigated and developed. The measurement campaigns were made for one, two and four fixed stations, respectivel