33 research outputs found

    Adaptive Illumination Patterns for Radar Applications

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    The fundamental goal of Fully Adaptive Radar (FAR) involves full exploitation of the joint, synergistic adaptivity of the radar\u27s transmitter and receiver. Little work has been done to exploit the joint space time Degrees-of-Freedom (DOF) available via an Active Electronically Steered Array (AESA) during the radar\u27s transmit illumination cycle. This research introduces Adaptive Illumination Patterns (AIP) as a means for exploiting this previously untapped transmit DOF. This research investigates ways to mitigate clutter interference effects by adapting the illumination pattern on transmit. Two types of illumination pattern adaptivity were explored, termed Space Time Illumination Patterns (STIP) and Scene Adaptive Illumination Patterns (SAIP). Using clairvoyant knowledge, STIP demonstrates the ability to remove sidelobe clutter at user specified Doppler frequencies, resulting in optimum receiver performance using a non-adaptive receive processor. Using available database knowledge, SAIP demonstrated the ability to reduce training data heterogeneity in dense target environments, thereby greatly improving the minimum discernable velocity achieved through STAP processing

    Electrical and Computer Engineering Annual Report 2017

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    Early Career Awards Faculty Directory Faculty Highlights Special Report: Mobility at Michigan Tech Faculty Publications Staff Profile & Directory Graduate Student Research Accelerated Master\u27s Degree Graduate Student Awards & Degrees Undergraduate Highlights Senior Design Enterprise Undergraduate Student Awards & Advisory Grants & Contracts Departmental Statistics A Pioneer\u27s Storyhttps://digitalcommons.mtu.edu/ece-annualreports/1001/thumbnail.jp

    Target Detection Architecture for Resource Constrained Wireless Sensor Networks within Internet of Things

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    Wireless sensor networks (WSN) within Internet of Things (IoT) have the potential to address the growing detection and classi�cation requirements among many surveillance applications. RF sensing techniques are the next generation technologies which o�er distinct advantages over traditional passive means of sensing such as acoustic and seismic which are used for surveillance and target detection applications of WSN. RF sensing based WSN within IoT detect the presence of designated targets by transmitting RF signals into the sensing environment and observing the re ected echoes. In this thesis, an RF sensing based target detection architecture for surveillance applications of WSN has been proposed to detect the presence of stationary targets within the sensing environment. With multiple sensing nodes operating simultaneously within the sensing region, diversity among the sensing nodes in the choice of transmit waveforms is required. Existing multiple access techniques to accommodate multiple sensing nodes within the sensing environment are not suitable for RF sensing based WSN. In this thesis, a diversity in the choice of the transmit waveforms has been proposed and transmit waveforms which are suitable for RF sensing based WSN have been discussed. A criterion have been de�ned to quantify the ease of detecting the signal and energy e�ciency of the signal based on which ease of detection index and energy e�ciency index respectively have been generated. The waveform selection criterion proposed in this thesis takes the WSN sensing conditions into account and identi�es the optimum transmit waveform within the available choices of transmit waveforms based on their respective ease of detection and energy e�ciency indexes. A target detector analyses the received RF signals to make a decision regarding the existence or absence of targets within the sensing region. Existing target detectors which are discussed in the context of WSN do not take the factors such as interference and nature of the sensing environment into account. Depending on the nature of the sensing environment, in this thesis the sensing environments are classi�ed as homogeneous and heterogeneous sensing environments. Within homogeneous sensing environments the presence of interference from the neighbouring sensing nodes is assumed. A target detector has been proposed for WSN within homogeneous sensing environments which can reliably detect the presence of targets. Within heterogeneous sensing environments the presence of clutter and interfering waveforms is assumed. A target detector has been proposed for WSN within heterogeneous sensing environments to detect targets in the presence of clutter and interfering waveforms. A clutter estimation technique has been proposed to assist the proposed target detector to achieve increased target detection reliability in the presence of clutter. A combination of compressive and two-step target detection architectures has been proposed to reduce the transmission costs. Finally, a 2-stage target detection architecture has been proposed to reduce the computational complexity of the proposed target detection architecture

    Hidden Markov models for radio localization in mixed LOS/NLOS conditions

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    Abstract—This paper deals with the problem of radio localization of moving terminals (MTs) for indoor applications with mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions. To reduce false localizations, a grid-based Bayesian approach is proposed to jointly track the sequence of the positions and the sight conditions of the MT. This method is based on the assumption that both the MT position and the sight condition are Markov chains whose state is hidden in the received signals [hidden Markov model (HMM)]. The observations used for the HMM localization are obtained from the power-delay profile of the received signals. In ultrawideband (UWB) systems, the use of the whole power-delay profile, rather than the total power only, allows to reach higher localization accuracy, as the power-profile is a joint measurement of time of arrival and power. Numerical results show that the proposed HMM method improves the accuracy of localization with respect to conventional ranging methods, especially in mixed LOS/NLOS indoor environments. Index Terms—Bayesian estimation, hidden Markov models (HMM), mobile positioning, source localization, tracking algorithms

    Country-specific Ground-based Bistatic Radar Clutter Analysis of Rural Environments

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    This thesis presents a novel statistical analysis of bistatic radar rural ground clutter for different terrain types of German rural environments under low grazing angles. A country-specific clutter analysis for subgroups of rural environments rather than for the rural environment as a whole will be presented. Therefore, the rural environment is divided into four dominant subgroup terrain types, namely fields with low vegetation, fields with high vegetation, plantations of small trees and forest environments, representing a typical rural German or even Central European environment. The thesis will present the bistatic clutter characteristics for both the summer and the winter vegetation. Therefore, bistatic measurement campaigns have been carried out during the summer 2019 and the winter of 2019/20 in the aforementioned four different rural terrain types. The measurements were carried out according to a designed bistatic measurement methodology to obtain comparable results and to be used for different radar applications in the radar relevant X-band at a center frequency of 8.85 GHz and over a bandwidth of 100 MHz, according to available transmit permissions. The distinction of the rural terrain into different subgroups enables a more precise and accurate clutter analysis and modeling of the statistical properties as will be shown in the presented results. A clear separation of the different types of rural terrain and the influence of the seasons was worked out. Additionally, model functions for the relevant parameters, characterizing the the bistatic clutter, are presented for their analytical description. The statistical properties are derived from the clutter regions of processed range-Doppler domain data, using an improved range-Doppler processing approach, for each of the four terrain types and the corresponding seasons. The data basis for the clutter analysis are the processed range-Doppler maps from the bistatic radar measurements using a dual-channel measurement approach, with a separate reference and surveillance channel. According to the authors’ current knowledge, a similar investigation based on real bistatic radar measurement data with the division into terrain subgroups and additionally for different season has not yet been carried out and published for a German rural environment. The presented data and results therefore have a significant impact on the research field of bistatic ground clutter, in which there are currently only very few results in the frequency range discussed in this thesis

    Mismatched Processing for Radar Interference Cancellation

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    Matched processing is a fundamental filtering operation within radar signal processing to estimate scattering in the radar scene based on the transmit signal. Although matched processing maximizes the signal-to-noise ratio (SNR), the filtering operation is ineffective when interference is captured in the receive measurement. Adaptive interference mitigation combined with matched processing has proven to mitigate interference and estimate the radar scene. A known caveat of matched processing is the resulting sidelobes that may mask other scatterers. The sidelobes can be efficiently addressed by windowing but this approach also comes with limited suppression capabilities, loss in resolution, and loss in SNR. The recent emergence of mismatch processing has shown to optimally reduce sidelobes while maintaining nominal resolution and signal estimation performance. Throughout this work, re-iterative minimum-mean square error (RMMSE) adaptive and least-squares (LS) optimal mismatch processing are proposed for enhanced signal estimation in unison with adaptive interference mitigation for various radar applications including random pulse repetition interval (PRI) staggering pulse-Doppler radar, airborne ground moving target indication, and radar & communication spectrum sharing. Mismatch processing and adaptive interference cancellation each can be computationally complex for practical implementation. Sub-optimal RMMSE and LS approaches are also introduced to address computational limitations. The efficacy of these algorithms is presented using various high-fidelity Monte Carlo simulations and open-air experimental datasets
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