Adaptive MIMO Radar for Target Detection, Estimation, and Tracking

We develop and analyze signal processing algorithms to detect, estimate, and track targets using multiple-input multiple-output: MIMO) radar systems. MIMO radar systems have attracted much attention in the recent past due to the additional degrees of freedom they offer. They are commonly used in two different antenna configurations: widely-separated: distributed) and colocated. Distributed MIMO radar exploits spatial diversity by utilizing multiple uncorrelated looks at the target. Colocated MIMO radar systems offer performance improvement by exploiting waveform diversity. Each antenna has the freedom to transmit a waveform that is different from the waveforms of the other transmitters. First, we propose a radar system that combines the advantages of distributed MIMO radar and fully polarimetric radar. We develop the signal model for this system and analyze the performance of the optimal Neyman-Pearson detector by obtaining approximate expressions for the probabilities of detection and false alarm. Using these expressions, we adaptively design the transmit waveform polarizations that optimize the target detection performance. Conventional radar design approaches do not consider the goal of the target itself, which always tries to reduce its detectability. We propose to incorporate this knowledge about the goal of the target while solving the polarimetric MIMO radar design problem by formulating it as a game between the target and the radar design engineer. Unlike conventional methods, this game-theoretic design does not require target parameter estimation from large amounts of training data. Our approach is generic and can be applied to other radar design problems also. Next, we propose a distributed MIMO radar system that employs monopulse processing, and develop an algorithm for tracking a moving target using this system. We electronically generate two beams at each receiver and use them for computing the local estimates. Later, we efficiently combine the information present in these local estimates, using the instantaneous signal energies at each receiver to keep track of the target. Finally, we develop multiple-target estimation algorithms for both distributed and colocated MIMO radar by exploiting the inherent sparsity on the delay-Doppler plane. We propose a new performance metric that naturally fits into this multiple target scenario and develop an adaptive optimal energy allocation mechanism. We employ compressive sensing to perform accurate estimation from far fewer samples than the Nyquist rate. For colocated MIMO radar, we transmit frequency-hopping codes to exploit the frequency diversity. We derive an analytical expression for the block coherence measure of the dictionary matrix and design an optimal code matrix using this expression. Additionally, we also transmit ultra wideband noise waveforms that improve the system resolution and provide a low probability of intercept: LPI)

Bayesian approach for the spectrum sensing mimo-cognitive radio network with presence of the uncertainty

A cognitive radio technique has the ability to learn. This system not only can observe the surrounding environment, adapt to environmental conditions, but also efficiently use the radio spectrum. This technique allows the secondary users (SUs) to employ the primary users (PUs) spectrum during the band is not being utilized by the user. Cognitive radio has three main steps: sensing of the spectrum, deciding and acting. In the spectrum sensing technique, the channel occupancy is determined with a spectrum sensing approach to detect unused spectrum. In the decision process, sensing results are evaluated and the decision process is then obtained based on these results. In the final process which is called the acting process, the scholar determines how to adjust the parameters of transmission to achieve great performance for the cognitive radio network

Spectrum prediction in dynamic spectrum access systems

Despite the remarkable foreseen advancements in maximizing network capacities, the in-expansible nature of radio spectrum exposed outdated spectrum management techniques as a core limitation. Fixed spectrum allocation inefficiency has generated a proliferation of dynamic spectrum access solutions to accommodate the growing demand for wireless, and mobile applications. This research primarily focuses on spectrum occupancy prediction which equip dynamic users with the cognitive ability to identify and exploit instantaneous availability of spectrum opportunities. The first part of this research is devoted to identifying candidate occupancy prediction techniques suitable for SOP scenarios are extensively analysed, and a theoretical based model selection framework is consolidated. The performance of single user Bayesian/Markov based techniques both analytically and numerically. Understanding performance bounds of Bayesian/Markov prediction allows the development of efficient occupancy prediction models. The third and fourth parts of this research investigates cooperative decision and data-based occupancy prediction. The expected cooperative prediction accuracy gain is addressed based on the single user prediction model. Specifically, the third contributions provide analytical approximations of single user, as well as cooperative hard fusion based spectrum prediction. Finally, the forth contribution shows soft fusion is superior and more robust compared to hard fusion cooperative prediction in terms of prediction accuracy. Throughout this research, case study analysis is provided to evaluate the performance of the proposed approaches. Analytical approaches and Monte-Carlo simulation are compared for the performance metric of interest. Remarkably, the case study analysis confirmed that the statistical approximation can predict the performance of local and hard fusion cooperative prediction accurately, capturing all the essential aspects of signal detection performance, temporal dependency of spectrum occupancy as well as the finite nature of the network

Robust low power CMOS methodologies for ISFETs instrumentation

I have developed a robust design methodology in a 0.18 [Mu]m commercial CMOS process to circumvent the performance issues of the integrated Ions Sensitive Field Effect Transistor (ISFET) for pH detection. In circuit design, I have developed frequency domain signal processing, which transforms pH information into a frequency modulated signal. The frequency modulated signal is subsequently digitized and encoded into a bit-stream of data. The architecture of the instrumentation system consists of a) A novel front-end averaging amplifier to interface an array of ISFETs for converting pH into a voltage signal, b) A high linear voltage controlled oscillator for converting the voltage signal into a frequency modulated signal, and c) Digital gates for digitizing and differentiating the frequency modulated signal into an output bit-stream. The output bit stream is indistinguishable to a 1st order sigma delta modulation, whose noise floor is shaped by +20dB/decade. The fabricated instrumentation system has a dimension of 1565 [Mu] m 1565 [Mu] m. The chip responds linearly to the pH in a chemical solution and produces a digital output, with up to an 8-bit accuracy. Most importantly, the fabricated chips do not need any post-CMOS processing for neutralizing any trapped-charged effect, which can modulate on-chip ISFETs’ threshold voltages into atypical values. As compared to other ISFET-related works in the literature, the instrumentation system proposed in this thesis can cope with the mismatched ISFETs on chip for analogue-to-digital conversions. The design methodology is thus very accurate and robust for chemical sensing

Collaborative Information Processing in Wireless Sensor Networks for Diffusive Source Estimation

In this dissertation, we address the issue of collaborative information processing for diffusive source parameter estimation using wireless sensor networks (WSNs) capable of sensing in dispersive medium/environment, from signal processing perspective. We begin the dissertation by focusing on the mathematical formulation of a special diffusion phenomenon, i.e., an underwater oil spill, along with statistical algorithms for meaningful analysis of sensor data leading to efficient estimation of desired parameters of interest. The objective is to obtain an analytical solution to the problem, rather than using non-model based sophisticated numerical techniques. We tried to make the physical diffusion model as much appropriate as possible, while maintaining some pragmatic and reasonable assumptions for the simplicity of exposition and analytical derivation. The dissertation studies both source localization and tracking for static and moving diffusive sources respectively. For static diffusive source localization, we investigate two parametric estimation techniques based on the maximum-likelihood (ML) and the best linear unbiased estimator (BLUE) for a special case of our obtained physical dispersion model. We prove the consistency and asymptotic normality of the obtained ML solution when the number of sensor nodes and samples approach infinity, and derive the Cramer-Rao lower bound (CRLB) on its performance. In case of a moving diffusive source, we propose a particle filter (PF) based target tracking scheme for moving diffusive source, and analytically derive the posterior Cramer-Rao lower bound (PCRLB) for the moving source state estimates as a theoretical performance bound. Further, we explore nonparametric, machine learning based estimation technique for diffusive source parameter estimation using Dirichlet process mixture model (DPMM). Since real data are often complicated, no parametric model is suitable. As an alternative, we exploit the rich tools of nonparametric Bayesian methods, in particular the DPMM, which provides us with a flexible and data-driven estimation process. We propose DPMM based static diffusive source localization algorithm and provide analytical proof of convergence. The proposed algorithm is also extended to the scenario when multiple diffusive sources of same kind are present in the diffusive field of interest. Efficient power allocation can play an important role in extending the lifetime of a resource constrained WSN. Resource-constrained WSNs rely on collaborative signal and information processing for efficient handling of large volumes of data collected by the sensor nodes. In this dissertation, the problem of collaborative information processing for sequential parameter estimation in a WSN is formulated in a cooperative game-theoretic framework, which addresses the issue of fair resource allocation for estimation task at the Fusion center (FC). The framework allows addressing either resource allocation or commitment for information processing as solutions of cooperative games with underlying theoretical justifications. Different solution concepts found in cooperative games, namely, the Shapley function and Nash bargaining are used to enforce certain kinds of fairness among the nodes in a WSN

Robust Optical Wireless Links over Turbulent Media using Diversity Solutions

Free-space optic (FSO) technology, i.e., optical wireless communication (OWC), is widely recognized as superior to radio frequency (RF) in many aspects. Visible and invisible optical wireless links solve first/last mile connectivity problems and provide secure, jam-free communication. FSO is license-free and delivers high-speed data rates in the order of Gigabits. Its advantages have fostered significant research efforts aimed at utilizing optical wireless communication, e.g. visible light communication (VLC), for high-speed, secure, indoor communication under the IEEE 802.15.7 standard. However, conventional optical wireless links demand precise optical alignment and suffer from atmospheric turbulence. When compared with RF, they suffer a low degree of reliability and lack robustness. Pointing errors cause optical transceiver misalignment, adversely affecting system reliability. Furthermore, atmospheric turbulence causes irradiance fluctuations and beam broadening of transmitted light. Innovative solutions to overcome limitations on the exploitation of high-speed optical wireless links are greatly needed.Spatial diversity is known to improve RF wireless communication systems. Similar diversity approaches can be adapted for FSO systems to improve its reliability and robustness; however, careful diversity design is needed since FSO apertures typically remain unbalanced as a result of FSO system sensitivity to misalignment. Conventional diversity combining schemes require persistent aperture monitoring and repetitive switching, thus increasing FSO implementation complexities. Furthermore, current RF diversity combining schemes may not be optimized to address the issue of unbalanced FSO receiving apertures.This dissertation investigates two efficient diversity combining schemes for multi-receiving FSO systems: switched diversity combining and generalized selection combining. Both can be exploited to reduce complexity and improve combining efficiency. Unlike maximum ratio combing, equal gain combining, and selective combining, switched diversity simplifies receiver design by avoiding unnecessary switching among receiving apertures. The most significant advantage of generalized combining is its ability to exclude apertures with low quality that could potentially affect the resultant output signal performance.This dissertation also investigates mobile FSO by considering a multi-receiving system in which all receiving FSO apertures are circularly placed on a platform. System mobility and performance are analyzed. Performance results confirm improvements when using angular diversity and generalized selection combining.The précis of this dissertation establishes the foundation of reliable FSO communications using efficient diversity-based solutions. Performance parameters are analyzed mathematically, and then evaluated using computer simulations. A testbed prototype is developed to facilitate the evaluation of optical wireless links via lab experiments

COOPERATIVE NETWORKING AND RELATED ISSUES: STABILITY, ENERGY HARVESTING, AND NEIGHBOR DISCOVERY

This dissertation deals with various newly emerging topics in the context of cooperative networking. The first part is about the cognitive radio. To guarantee the performance of high priority users, it is important to know the activity of the high priority communication system but the knowledge is usually imperfect due to randomness in the observed signal. In such a context, the stability property of cognitive radio systems in the presence of sensing errors is studied. General guidelines on controlling the operating point of the sensing device over its receiver operating characteristics are also given. We then consider the hybrid of different modes of operation for cognitive radio systems with time-varying connectivity. The random connectivity gives additional chances that can be utilized by the low priority communication system. The second part of this dissertation is about the random access. We are specifically interested in the scenario when the nodes are harvesting energy from the environment. For such a system, we accurately assess the effect of limited, but renewable, energy availability on the stability region. The effect of finite capacity batteries is also studied. We next consider the exploitation of diversity amongst users under random access framework. That is, each user adapts its transmission probability based on the local channel state information in a decentralized manner. The impact of imperfect channel state information on the stability region is investigated. Furthermore, it is compared to the class of stationary scheduling policies that make centralized decisions based on the channel state feedback. The backpressure policy for cross-layer control of wireless multi-hop networks is known to be throughput-optimal for i.i.d. arrivals. The third part of this dissertation is about the backpressure-based control for networks with time-correlated arrivals that may exhibit long-range dependency. It is shown that the original backpressure policy is still throughput-optimal but with increased average network delay. The case when the arrival rate vector is possibly outside the stability region is also studied by augmenting the backpressure policy with the flow control mechanism. Lastly, the problem of neighbor discovery in a wireless sensor network is dealt. We first introduce the realistic effect of physical layer considerations in the evaluation of the performance of logical discovery algorithms by incorporating physical layer parameters. Secondly, given the lack of knowledge of the number of neighbors along with the lack of knowledge of the individual signal parameters, we adopt the viewpoint of random set theory to the problem of detecting the transmitting neighbors. Random set theory is a generalization of standard probability theory by assigning sets, rather than values, to random outcomes and it has been applied to multi-user detection problem when the set of transmitters are unknown and dynamically changing

An investigation on the use of SNR distributions for the optimisation of coarse-fine spectrum sensing for cognitive radio

This thesis investigates the optimisation of Coarse-Fine (CF) spectrum sensing architectures under a distribution of SNRs for Dynamic Spectrum Access (DSA). Three different detector architectures are investigated: the Coarse-Sorting Fine Detector (CSFD), the Coarse-Deciding Fine Detector (CDFD) and the Hybrid Coarse-Fine Detector (HCFD). To date, the majority of the work on coarse-fine spectrum sensing for cognitive radio has focused on a single value for the SNR. This approach overlooks the key advantage that CF sensing has to offer, namely that high powered signals can be easily detected without extra signal processing. By considering a range of SNR values, the detector can be optimised more effectively and greater performance gains realised. This work considers the optimisation of CF spectrum sensing schemes where the security and performance are treated separately. Instead of optimising system performance at a single, constant, low SNR value, the system instead is optimised for the average operating conditions. The security is still provided such that at the low SNR values the safety specifications are met. By decoupling the security and performance, the system’s average performance increases whilst maintaining the protection of licensed users from harmful interference. The different architectures considered in this thesis are investigated in theory, simulation and physical implementation to provide a complete overview of the performance of each system. This thesis provides a method for estimating SNR distributions which is quick, accurate and relatively low cost. The CSFD is modelled and the characteristic equations are found for the CDFD scheme. The HCFD is introduced and optimisation schemes for all three architectures are proposed. Finally, using the Implementing Radio In Software (IRIS) test-bed to confirm simulation results, CF spectrum sensing is shown to be significantly quicker than naive methods, whilst still meeting the required interference probability rates and not requiring substantial receiver complexity increases

Higher order asymptotic inference in remote sensing of oceanic and planetary environments

Thesis (Ph. D. in Ocean Engineering)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 223-230).An inference method based on higher order asymptotic expansions of the bias and covariance of the Maximum Likelihood Estimate (MLE) is used to investigate the accuracy of parameter estimates obtained from remote sensing measurements in oceanic and planetary environments. We consider the problems of (1) planetary terrain surface slope estimation, (2) Lambertian surface orientation and albedo resolution and (3) passive source localization in a fluctuating waveguide containing random internal waves. In these and other applications, measurements are typically corrupted by signal-independent ambient noise, as well as signal-dependent noise arising from fluctuations in the propagation medium, relative motion between source and receiver, scattering from rough surfaces, propagation through random inhomogeneities, and source incoherence. We provide a methodology for incorporating such uncertainties, quantifying their effects and ensuring that statistical biases and errors meet desired thresholds. The method employed here was developed by Naftali and Makris[84] to determine necessary conditions on sample size or Signal to Noise Ratio (SNR) to obtain estimates that attain minimum variance, the Cramer-Rao Lower Bound (CRLB), as well as practical design thresholds. These conditions are derived by first expanding the bias and covariance of the MLE in inverse orders of sample size or SNR, where the firstorder covariance term is the CRLB. The necessary sample sizes and SNRs are then computed by requiring that (i) the first-order bias and second-order covariance terms are much smaller than the true parameter value and the CRLB, respectively, and (ii) the CRLB falls within desired error thresholds. Analytical expressions have been derived for the asymptotic orders of the bias and covariance of the MLE obtained from general complex Gaussian vectors,[68, 109] which can then be used in many practical problems since (i) data distributions can often be assumed to be Gaussian by virtue of the central limit theorem, and (ii) they allow for both the mean and variance of the measurement to be functions of the estimation parameters, as is the case in the presence of signal-dependent noise. In the first part of this thesis, we investigate the problem of planetary terrain surface slope estimation from satellite images. For this case, we consider the probability distribution of the measured photo count of natural sunlight through a Charge- Coupled Device (CCD) and also include small-scale albedo fluctuation and atmospheric haze, besides signal-dependent (or camera shot) noise and signal-independent (or camera read) noise. We determine the theoretically exact biases and errors inherent in photoclinometric surface slope and show when they may be approximated by asymptotic expressions for sufficiently high sample size. We then determine the sample sizes necessary to yield surface slope estimates that have tolerable errors. We show how small-scale albedo variability often dominates biases and errors, which may become an order of magnitude larger than surface slopes when surface reflectance has a weak dependence on surface tilt. The method described above is also used to determine the errors of Lambertian surface orientation and albedo estimates obtained from remote multi-static acoustic, optical, radar or laser measurements of fluctuating radiance. Such measurements are typically corrupted by signal-dependent noise, known as speckle, which arises from complex Gaussian field fluctuations. We find that single-sample orientation estimates have biases and errors that vary dramatically depending on illumination direction measurement diversity due to the signal-dependent nature of speckle noise and the nonlinear relationship between surface orientation, illumination direction and fluctuating radiance. We also provide the sample sizes necessary to obtain surface orientation and albedo estimates that attain desired error thresholds. Next, we consider the problem of source localization in a fluctuating ocean waveguide containing random internal waves. Propagation through such a fluctuating environment leads to both the mean and covariance of the received acoustic field being parameter-dependent, which is typically the case in practice. We again make use of the new expression for the second-order covariance of the multivariate Gaussian MLE,[68 which allows us to take advantage of the parameter dependence in both the mean and the variance to obtain more accurate estimates. The degradation in localization accuracy due to scattering by internal waves is quantified by computing the asymptotic biases and variances of source localization estimates. We show that the sample sizes and SNRs necessary to attain practical localization thresholds can become prohibitively large compared to a static waveguide. The results presented here can be used to quantify the effects of environmental uncertainties on passive source localization techniques, such as matched-field processing (MFP) and focalization. Finally, a method is developed for simultaneously estimating the instantaneous mean velocity and position of a group of randomly moving targets as well as the respective standard deviations across the group by Doppler analysis of acoustic remote sensing measurements in free space and in a stratified ocean waveguide. It is shown that the variance of the field scattered from the swarm typically dominates the rangevelocity ambiguity function, but cross-spectral coherence remains and enables high resolution Doppler velocity and position estimation. It is shown that if pseudo-random signals are used, the mean and variance of the swarms' velocity and position can be expressed in terms of the first two moments of the measured range-velocity ambiguity function. This is shown analytically for free space and with Monte-Carlo simulations for an ocean waveguide. It is shown that these expressions can be used to obtain accurate, with less than 10% error, of a large swarm's instantaneous velocity and position means and standard deviations for long-range remote sensing applications.by loannis Bertsatos.Ph.D.in Ocean Engineerin