904 research outputs found

    AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

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    With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

    Localization and tracking of electronic devices with their unintended emissions

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    The precise localization and tracking of electronic devices via their unintended emissions has a broad range of commercial and security applications. Active stimulation of the receivers of such devices with a known signal generates very low power unintended emissions. This dissertation presents localization and tracking of multiple devices using both simulation and experimental data in the form of five papers. First the localization of multiple emitting devices through active stimulation under multipath fading with a Smooth MUSIC based scheme in the near field region is presented. Spatial smoothing helps to separate the correlated sources and the multipath fading and results confirm improved accuracy. A cost effective near-field localization method is proposed next to locate multiple correlated unintended emitting devices under colored noise conditions using two well separated antenna arrays since colored noise in the environment degrades the subspace-based localization techniques. Subsequently, in order to track moving sources, a near-field scheme by using array output is introduced to monitor direction of arrival (DOA) and the distance between the antenna array and the moving source. The array output, which is a nonlinear function of DOA and distance information, is employed in the Extended Kalman Filter (EKF). In order to show the near- and far-field effect on estimation accuracy, computer simulation results are included for localization and tracking techniques. Finally, an L-shaped array is constructed and a suite of schemes are introduced for localization and tracking of such devices in the three-dimensional environment. Experimental results for localization and tracking of unintended emissions from single and multiple devices in the near-field environment of an antenna array are demonstrated --Abstract, page iv

    Bearing estimation techniques for improved performance spread spectrum receivers

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    The main topic of this thesis is the use of bearing estimation techniques combined with multiple antenna elements for spread spectrum receivers. The motivation behind this work is twofold: firstly, this type of receiver structure may offer the ability to locate the position of a mobile radio in an urban environment. Secondly, these algorithms permit the application of space division multiple access (SDMA) to cellular mobile radio, which can offer large system capacity increases. The structure of these receivers may naturally be divided into two parts: signal detection and spatial filtering blocks. The signal detection problem involves locating the bearings of the multipath components which arise from the transmission of the desired user’s signal. There are a number of approaches to this problem, but here the MUSIC algorithm will be adopted. This algorithm requires an initial estimate of the number of signals impinging on the receiver, a task which can be performed by model order determination techniques. A major deficiency of MUSIC is its inability to resolve the highly–correlated and coherent multipath signals which frequently occur in a spread spectrum system. One of the simplest ways to overcome this problem is to employ spatial smoothing techniques, which trade the size of the antenna array for the ability to resolve coherent signals. The minimum description length (MDL) is one method for determining the signal model order and it can easily be extended to calculating the required degree of spatial smoothing. In this thesis, an approach to analysing the probability of correct model order determination for the MDL with spatial smoothing is presented. The performance of MUSIC, combined with spatial smoothing, is also of great significance. Two smoothing algorithms, spatial smoothing and forward–backward spatial smoothing, are analysed to compare their performance. If SDMA techniques are to be deployed in cellular systems, it is important to first estimate the performance improvements available from applying antenna array spatial filters. Initially, an additive white Gaussian noise channel is used for estimating the capacity of a perfect power–controlled code division multiple access system with SDMA techniques. Results suggest that the mean interference levels are almost halved as the antenna array size doubles, permitting large capacity increases. More realistic multipath models for urban cellular radio channels are also considered. If the transmitter gives rise to a number of point source multipath components, the bearing estimation receiver is able to capture the signal energy of each multipath. However, when a multipath component has significant angular spread, bearing estimation receivers need to combine separate directional components, at an increased cost in complexity, to obtain similar results to a matched filter. Finally, a source location algorithm for urban environments is presented, based on bearing estimation of multipath components. This algorithm requires accurate knowledge of the positions of the major multipath reflectors present in the environment. With this knowledge it is possible to determine the position of a transmitting mobile unit. Simulation results suggest that the algorithm is very sensitive to angular separation of the multipath components used for the source location technique

    Enhancing polynomial MUSIC algorithm for coherent broadband sources through spatial smoothing

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    Direction of arrival algorithms which exploit the eigenstructure of the spatial covariance matrix (such as MUSIC) encounter difficulties in the presence of strongly correlated sources. Since the broadband polynomial MUSIC is an extension of the narrowband version, it is unsurprising that the same issues arise. In this paper, we extend the spatial smoothing technique to broadband scenarios via spatially averaging polynomial spacetime covariance matrices. This is shown to restore the rank of the polynomial source covariance matrix. In the application of the polynomial MUSIC algorithm, the spatially smoothed spacetime covariance matrix greatly enhances the direction of arrival estimate in the presence of strongly correlated sources. Simulation results are described shows the performance improvement gained using the new approach compared to the conventional non-smoothed method

    3-D Beamspace ML Based Bearing Estimator Incorporating Frequency Diversity and Interference Cancellation

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    The problem of low-angle radar tracking utilizing an array of antennas is considered. In the low-angle environment, echoes return from a low flying target via a specular path as well as a direct path. The problem is compounded by the fact that the two signals arrive within a beamwidth of each other and are usually fully correlated, or coherent. In addition, the SNR at each antenna element is typically low and only a small number of data samples, or snapshots, is available for processing due to the rapid movement of the target. Theoretical studies indicates that the Maximum Likelihood (ML) method is the only reliable estimation procedure in this type of scenario. However, the classical ML estimator involves a multi-dimensional search over a multi-modal surface and is consequently computationally burdensome. In order to facilitate real time processing, we here propose the idea of beamspace domain processing in which the element space snapshot vectors are first operated on by a reduced Butler matrix composed of three orthogonal beamforming weight vectors facilitating a simple, closed-form Beamspace Domain ML (BDML) estimator for the direct and specular path angles. The computational simplicity of the method arises from the fact that the respective beams associated with the three columns of the reduced Butler matrix have all but three nulls in common. The performance of the BDML estimator is enhanced by incorporating the estimation of the complex reflection coefficient and the bisector angle, respectively, for the symmetric and nonsymmetric multipath cases. To minimize the probability of track breaking, the use of frequency diversity is incorporated. The concept of coherent signal subspace processing is invoked as a means for retaining the computational simplicity of single frequency operation. With proper selection of the auxiliary frequencies, it is shown that perfect focusing may be achieved without iterating. In order to combat the effects of strong interfering sources, a novel scheme is presented for adaptively forming the three beams which retains the feature of common nulls

    Sensor array signal processing : two decades later

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    Caption title.Includes bibliographical references (p. 55-65).Supported by Army Research Office. DAAL03-92-G-115 Supported by the Air Force Office of Scientific Research. F49620-92-J-2002 Supported by the National Science Foundation. MIP-9015281 Supported by the ONR. N00014-91-J-1967 Supported by the AFOSR. F49620-93-1-0102Hamid Krim, Mats Viberg

    Simulation and analysis of differential global positioning system for civil helicopter operations

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    A Differential Global Positioning System (DGPS) computer simulation was developed, to provide a versatile tool for assessing DGPS referenced civil helicopter navigation. The civil helicopter community will probably be an early user of the GPS capability because of the unique mission requirements which include offshore exploration and low altitude transport into remote areas not currently served by ground based Navaids. The Monte Carlo simulation provided a sufficiently high fidelity dynamic motion and propagation environment to enable accurate comparisons of alternative differential GPS implementations and navigation filter tradeoffs. The analyst has provided the capability to adjust most aspects of the system, the helicopter flight profile, the receiver Kalman filter, and the signal propagation environment to assess differential GPS performance and parameter sensitivities. Preliminary analysis was conducted to evaluate alternative implementations of the differential navigation algorithm in both the position and measurement domain. Results are presented to show that significant performance gains are achieved when compared with conventional GPS but that differences due to DGPS implementation techniques were small. System performance was relatively insensitive to the update rates of the error correction information

    Sturdy Positioning with High Sensitivity GPS Sensors Under Adverse Conditions

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    High sensitivity GPS receivers have extended the use of GNSS navigation to environments which were previously deemed unsuitable for satellite signal reception. Under adverse conditions the signals become attenuated and reflected. High sensitivity receivers achieve signal reception by using a large number of correlators and an extended integration time. Processing the observation data in dynamic and rapidly changing conditions requires a careful and consistent treatment. Code-based autonomous solutions can cause major errors in the estimated position, due primarily to multipath effects. A custom procedure of autonomous GPS positioning has been developed, boosting the positioning performance through appropriate processing of code and Doppler observations. Besides the common positioning procedures, robust estimation methods have been used to minimise the effects of gross observation errors. In normal conditions, differential GNSS yields good results, however, under adverse conditions, it fails to improve significantly the receiver’s position. Therefore, a so-called conditional DGPS has been developed which determines the position differentially by using data from the strong signals only. These custom-developed procedures have been tested in different conditions in static and kinematic cases and the results have been compared to those processed by the receiver
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