A Centralized Processing Framework for Foliage Penetration Human Tracking in Multistatic Radar

A complete centralized processing framework is proposed for human tracking using multistatic radar in the foliage-penetration environment. The configuration of the multistatic radar system is described. Primary attention is devoted to time of arrival (TOA) estimation and target localization. An improved approach that takes the geometrical center as the TOA estimation of the human target is given. The minimum mean square error paring (MMSEP) approach is introduced for multi-target localization in the multistatic radar system. An improved MMSEP algorithm is proposed using the maximum velocity limitation and the global nearest neighbor criterion, efficiently decreasing the computational cost of MMSEP. The experimental results verify the effectiveness of the centralized processing framework

Efficient closed-form estimators in multistatic target localization and motion analysis

Object localization is fast becoming an important research topic because of its wide applications. Often of the time, object localization is accomplished in two steps. The first step exploits the characteristics of the received signals and extracts certain localization information i.e. measurements. Some typical measurements include timeof-arrival (TOA), time-difference-of-arrival (TDOA), received signal strength (RSS) and angle-of-arrival (AOA). Together with the known receiver position information, the object location is then estimated in the second step from the obtained measurements. The localization of an object using a number of sensors is often challenged due to the highly nonlinear relationship between the measurements and the object location. This thesis focuses on the second step and considers designing novel and efficient localization algorithms to solve such a problem. This thesis first derives a new algebraic positioning solution using a minimum number of measurements, and from which to develop an object location estimator. Two measurements are sufficient in 2-D and three in 3-D to yield a solution if they are consistent. The derived minimum measurement solution is exact and reduces the computation to the roots of a quadratic equation. The solution derivation also leads to simple criteria to ascertain if the line of positions from two measurements intersects. By partitioning the overdetermined set of measurements first to obtain the individual minimum measurement solutions, we propose a best linear unbiased estimator to form the final location estimate. The analysis supports the proposed estimator in reaching the Cramer-Rao Lower Bound (CRLB) accuracy under Gaussian noise. A measurement partitioning scheme is developed to improve performance when the noise level becomes large. We mainly use elliptic time delay measurements for presentation, and the derived results apply to the hyperbolic time difference measurements as well. Both the 2-D and 3-D scenarios are considered. A multistatic system uses a transmitter to illuminate the object of interest and collects the reflected signal by several receivers to determine its location. In some scenarios such as passive coherent localization or for gaining flexibility, the position of the transmitter is not known. In this thesis, we investigate the use of the indirect path measurements reflected off the object alone, or together with the direct path measurements from the transmitter to receiver for locating the object in the absence of the transmitter position. We show that joint estimation of the object and transmitter positions from both the indirect and direct measurements can yield better object location estimate than using the indirect measurements only by eliminating the dependency of the transmitter position. An algebraic closed-form solution is developed for the nonlinear problem of joint estimation and is shown analytically to achieve the CRLB performance under Gaussian noise over the small error region. To complete the study and gain insight, the optimum receiver placement in the absence of transmitter position is derived, by minimizing the estimation confidence region or the estimation variance for the object location. The performance lost due to unknown transmitter position under the optimum geometries is quantified. Simulations confirm well with the theoretical developments. In practice, a more realistic localization scenario with the unknown transmitter is that the transmitter works non-cooperatively. In this situation, no timestamp is available in the transmitted signal so that the signal sent time is often not known. This thesis next considers the extension of the localization scenario to such a case. More generally, the motion potential of the unknown object and transmitter is considered in the analysis. When the transmitted signal has a well-defined pattern such as some standard synchronization or pilot sequence, it would still be able to estimate the indirect and direct time delays and Doppler frequency shifts but with unknown constant time delay and frequency offset added. In this thesis, we would like to estimate the object and transmitter positions and velocities, and the time and frequency offsets jointly. Both dynamic and partial dynamic localization scenarios based on the motion status of the object and the transmitter are considered in this thesis. By investigating the CRLB of the object location estimate, the improvement in position and velocity estimate accuracy through joint estimation comparing with the differencing approach using TDOA/FDOA measurements is evaluated. The degradation due to time and frequency offsets is also analyzed. Algebraic closed-form solutions to solve the highly nonlinear joint estimation problems are then proposed in this thesis, followed by the analysis showing that the CRLB performance can be achieved under Gaussian noise over the small error region. When the transmitted signal is not time-stamped and does not have a well-defined pattern such as some standard synchronization or pilot sequence, it is often impossible to obtain the indirect and direct measurements separately. Instead, a self-calculated TDOA between the indirect- and direct-path TOAs shall be considered which does not require any synchronization between the transmitter and a receiver, or among the receivers. A refinement method is developed to locate the object in the presence of the unknown transmitter position, where a hypothesized solution is needed for initialization. Analysis shows that the refinement method is able to achieve the CRLB performance under Gaussian noise. Three realizations of the hypothesized solution applying multistage processing to simplify the nonlinear estimation problem are derived. Simulations validate the effectiveness in initializing the refinement estimator

Mathematical optimization techniques for cognitive radar networks

This thesis discusses mathematical optimization techniques for waveform design in cognitive radars. These techniques have been designed with an increasing level of sophistication, starting from a bistatic model (i.e. two transmitters and a single receiver) and ending with a cognitive network (i.e. multiple transmitting and multiple receiving radars). The environment under investigation always features strong signal-dependent clutter and noise. All algorithms are based on an iterative waveform-filter optimization. The waveform optimization is based on convex optimization techniques and the exploitation of initial radar waveforms characterized by desired auto and cross-correlation properties. Finally, robust optimization techniques are introduced to account for the assumptions made by cognitive radars on certain second order statistics such as the covariance matrix of the clutter. More specifically, initial optimization techniques were proposed for the case of bistatic radars. By maximizing the signal to interference and noise ratio (SINR) under certain constraints on the transmitted signals, it was possible to iteratively optimize both the orthogonal transmission waveforms and the receiver filter. Subsequently, the above work was extended to a convex optimization framework for a waveform design technique for bistatic radars where both radars transmit and receive to detect targets. The method exploited prior knowledge of the environment to maximize the accumulated target return signal power while keeping the disturbance power to unity at both radar receivers. The thesis further proposes convex optimization based waveform designs for multiple input multiple output (MIMO) based cognitive radars. All radars within the system are able to both transmit and receive signals for detecting targets. The proposed model investigated two complementary optimization techniques. The first one aims at optimizing the signal to interference and noise ratio (SINR) of a specific radar while keeping the SINR of the remaining radars at desired levels. The second approach optimizes the SINR of all radars using a max-min optimization criterion. To account for possible mismatches between actual parameters and estimated ones, this thesis includes robust optimization techniques. Initially, the multistatic, signal-dependent model was tested against existing worst-case and probabilistic methods. These methods appeared to be over conservative and generic for the considered signal-dependent clutter scenario. Therefore a new approach was derived where uncertainty was assumed directly on the radar cross-section and Doppler parameters of the clutters. Approximations based on Taylor series were invoked to make the optimization problem convex and {subsequently} determine robust waveforms with specific SINR outage constraints. Finally, this thesis introduces robust optimization techniques for through-the-wall radars. These are also cognitive but rely on different optimization techniques than the ones previously discussed. By noticing the similarities between the minimum variance distortionless response (MVDR) problem and the matched-illumination one, this thesis introduces robust optimization techniques that consider uncertainty on environment-related parameters. Various performance analyses demonstrate the effectiveness of all the above algorithms in providing a significant increase in SINR in an environment affected by very strong clutter and noise

Design of Miniaturized Antipodal Vivaldi Antennas and a Microwave Head Imaging System for the Detection of Blood Clots in the Brain

Traditional brain imaging modalities, for example, MRI, CT scan, X-ray, etc. can provide precise and high-resolution images of the brain for diagnosing lesions, tumors or clots inside the brain. However, these modalities require bulky and expensive test setups accessible only at specialized diagnostic centers, and hence may not be suitable or affordable to many patients. Furthermore, the inherent health risks limit the usability of these modalities for frequent monitoring. Microwave imaging is deemed a promising alternative due to its being cost-effective, portable, non-ionizing, non-intrusive. Therefore, this work aims to design an effective microwave head imaging system for the detection of blood clots inside the brain. Two miniaturized antipodal Vivaldi antenna designs are proposed which can provide wideband operation covering the low microwave frequency range (within 1 - 6 GHz) while having electrically small dimensions, directional radiation pattern with reasonable gain, and without requiring immersion in any matching/ coupling liquid. A head imaging system is presented which utilizes a quarter-head scanning approach, to reconstruct four images of the brain by scanning four quarters of the head, using the designed antipodal wideband Vivaldi antenna. A numerical brain model, with and without the presence of blood clot, is simulated using the proposed head-imaging system. At each quarter, the antenna is placed at nine different positions for scanning. The reflected signal at each position is processed and using confocal microwave imaging technique four images of the brain are reconstructed. A comparison is made among the four images in terms of their intensities, for the detection and approximate location of the blood clot inside the brain. The presence of higher intensity regions in any specific quarter of the head demonstrates the presence of a clot and its location and validates the feasibility of the proposed head imaging system using the low frequency wideband Vivaldi antenna

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

Location-based augmented reality visualization of 3D models using a mobile application – izanagiXR

Introduction: The increasing use of AR in practical applications such as education, design, manufacturing, and construction shows enormous promise for upgrading existing technology and improving quality of life. Meanwhile, the construction industry is well known for falling behind in the implementation of IT even though the digitalization is already transforming the industry across the entire lifecycle. A peculiarity in Switzerland is that in most cantons, projects still must be marked with physical metal poles called construction spans. Methodology: The aim of this research is to investigate an effective method for visualizing 3D models using location-based Augmented Reality. This paper seeks to identify the necessary steps for developing an application to accurately visualize 3D buildings using location-based AR for replacing construction spans and to find out what the benefits and challenges are. For this purpose, an AR artifact is being developed and five interviews with industry experts are being conducted to gain a better understanding of the current practice. Findings: The costs of erecting and maintaining such construction spans can amount to 0.1% or more of each construction project depending on its height and the complexity of the terrain. AR represents an enabling technology for customer engagement. It can be reasoned that by externalizing the visualization of construction projects, AR can reduce the mental effort customers need to participate in a productive discourse. AR technology has been improving dramatically and together with hybrid localization methods it is able to display information accurately and reliably in the physical world. Recommendations: In light of the findings of this research, developers, and public bodies such as municipalities alike should evaluate the use of AR applications for replacing construction spans and digitize the construction span industry to save significant investment amounts and opportunity losses. This paper recommends the above-mentioned stakeholders to explore the potential of AR applications additionally to using construction spans to gain important experience and to get an idea how their industry could look like once construction spans are not legally required anymore and AR hardware has overcome current limitations

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion