Underwater Direction-of-Arrival Finding: Maximum Likelihood Estimation and Performance Analysis

In this dissertation, we consider the problems of direction-of-arrival: DOA) finding using acoustic sensor arrays in underwater scenarios, and develop novel signal models, maximum likelihood: ML) estimation methods, and performance analysis results. We first examine the underwater scenarios where the noise on sensor arrays are spatially correlated, for which we consider using sparse sensor arrays consisting of widely separated sub-arrays and develop ML DOA estimators based on the Expectation-Maximization scheme. We examine both zero-mean and non-zero-mean Gaussian incident signals and provide detailed estimation performance analysis. Our results show that non-zero means in signals improve the accuracy of DOA estimation. Then we consider the problem of DOA estimation of marine vessel sources such as ships, submarines, or torpedoes, which emit acoustic signals containing both sinusoidal and random components. We propose a mixed signal model and develop an ML estimator for narrow-band DOA finding of such signals and then generalize the results to the wide-band case. We provide thorough performance analysis for the proposed signal model and estimators. We show that our mixed signal model and ML estimators improve the DOA estimation performance in comparison with the typical stochastic ones assuming zero-mean Gaussian signals. At last, we derive a Barankin-type bound: BTB) on the mean-square error of DOA estimation using acoustic sensor arrays. The typical DOA estimation performance evaluation are usually based on the Cram\u27{e}r-Rao Bound: CRB), which cannot predict the threshold region of signal-to-noise ratio: SNR), below which the accuracy of the ML estimation degrades rapidly. Identification of the threshold region has important applications for DOA estimation in practice. Our derived BTB provides an approximation to the SNR threshold region

Performance bounds on matched-field methods for source localization and estimation of ocean environmental parameters

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2001Matched-field methods concern estimation of source location and/or ocean environmental parameters by exploiting full wave modeling of acoustic waveguide propagation. Typical estimation performance demonstrates two fundamental limitations. First, sidelobe ambiguities dominate the estimation at low signal-to-noise ratio (SNR), leading to a threshold performance behavior. Second, most matched-field algorithms show a strong sensitivity to environmental/system mismatch, introducing some biased estimates at high SNR. In this thesis, a quantitative approach for ambiguity analysis is developed so that different mainlobe and sidelobe error contributions can be compared at different SNR levels. Two large-error performance bounds, the Weiss-Weinstein bound (WWB) and Ziv-Zakai bound (ZZB), are derived for the attainable accuracy of matched-field methods. To include mismatch effects, a modified version of the ZZB is proposed. Performance analyses are implemented for source localization under a typical shallow water environment chosen from the Shallow Water Evaluation Cell Experiments (SWellEX). The performance predictions describe the simulations of the maximum likelihood estimator (MLE) well, including the mean square error in all SNR regions as well as the bias at high SNR. The threshold SNR and bias predictions are also verified by the SWellEX experimental data processing. These developments provide tools to better understand some fundamental behaviors in matched-field performance and provide benchmarks to which various ad hoc algorithms can be compared.Financial support for my research was provided by the Office of Naval Research and the WHOI Education Office

Target localization in passive and active systems : performance bonds

The main goal of this dissertation is to improve the understanding and to develop ways to predict the performance of localization techniques as a function of signal-to-noise ratio (SNR) and of system parameters. To this end, lower bounds on the maximum likelihood estimator (MLE) performance are studied. The Cramer-Rao lower bound (CRLB) for coherent passive localization of a near-field source is derived. It is shown through the Cramer-Rao bound that, the coherent localization systems can provide high accuracies in localization, to the order of carrier frequency of the observed signal. High accuracies come to a price of having a highly multimodal estimation metric which can lead to sidelobes competing with the mainlobe and engendering ambiguity in the selection of the correct peak. The effect of the sidelobes over the estimator performance at different SNR levels is analyzed and predicted with the use of Ziv-Zakai lower bound (ZZB). Through simulations it is shown that ZZB is tight to the MLEs performance over the whole SNR range. Moreover, the ZZB is a convenient tool to assess the coherent localization performance as a function of various system parameters. The ZZB was also used to derive a lower bound on the MSE of estimating the range and the range rate of a target in active systems. From the expression of the derived lower bound it was noted that, the ZZB is determined by SNR and by the ambiguity function (AF). Thus, the ZZB can serve as an alternative to the ambiguity function (AF) as a tool for radar design. Furthermore, the derivation is extended to the problem of estimating target’s location and velocity in a distributed multiple input multiple output (MIMO) radar system. The derived bound is determined by SNR, by the product between the number of transmitting antennas and the number of receiving antennas from the radar system, and by all the ambiguity functions and the cross-ambiguity functions corresponding to all pairs transmitter-target-receiver. Similar to the coherent localization, the ZZB can be applied to study the performance of the estimator as a function of different system parameters. Comparison between the ZZB and the MSE of the MLE obtained through simulations demonstrate that the bound is tight in all SNR regions

Acoustic vector-sensor array processing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 145-148).Existing theory yields useful performance criteria and processing techniques for acoustic pressure-sensor arrays. Acoustic vector-sensor arrays, which measure particle velocity and pressure, offer significant potential but require fundamental changes to algorithms and performance assessment. This thesis develops new analysis and processing techniques for acoustic vector-sensor arrays. First, the thesis establishes performance metrics suitable for vector sensor processing. Two novel performance bounds define optimality and explore the limits of vector-sensor capabilities. Second, the thesis designs non-adaptive array weights that perform well when interference is weak. Obtained using convex optimization, these weights substantially improve conventional processing and remain robust to modeling errors. Third, the thesis develops subspace techniques that enable near-optimal adaptive processing. Subspace processing reduces the problem dimension, improving convergence or shortening training time.by Jonathan Paul Kitchens.Ph.D

Performance Analysis of Bearings-only Tracking Problems for Maneuvering Target and Heterogeneous Sensor Applications

State estimation, i.e. determining the trajectory, of a maneuvering target from noisy measurements collected by a single or multiple passive sensors (e.g. passive sonar and radar) has wide civil and military applications, for example underwater surveillance, air defence, wireless communications, and self-protection of military vehicles. These passive sensors are listening to target emitted signals without emitting signals themselves which give them concealing properties. Tactical scenarios exists where the own position shall not be revealed, e.g. for tracking submarines with passive sonar or tracking an aerial target by means of electro-optic image sensors like infrared sensors. This estimation process is widely known as bearings-only tracking. On the one hand, a challenge is the high degree of nonlinearity in the estimation process caused by the nonlinear relation of angular measurements to the Cartesian state. On the other hand, passive sensors cannot provide direct target location measurements, so bearings-only tracking suffers from poor target trajectory estimation accuracy due to marginal observability from sensor measurements. In order to achieve observability, that means to be able to estimate the complete target state, multiple passive sensor measurements must be fused. The measurements can be recorded spatially distributed by multiple dislocated sensor platforms or temporally distributed by a single, moving sensor platform. Furthermore, an extended case of bearings-only tracking is given if heterogeneous measurements from targets emitting different types of signals, are involved. With this, observability can also be achieved on a single, not necessarily moving platform. In this work, a performance bound for complex motion models, i.e. piecewisely maneuvering targets with unknown maneuver change times, by means of bearings-only measurements from a single, moving sensor platform is derived and an efficient estimator is implemented and analyzed. Furthermore, an observability analysis is carried out for targets emitting acoustic and electromagnetic signals. Here, the different signal propagation velocities can be exploited to ensure observability on a single, not necessarily moving platform. Based on the theoretical performance and observability analyses a distributed fusion system has been realized by means of heterogeneous sensors, which shall detect an event and localize a threat. This is performed by a microphone array to detect sound waves emitted by the threat as well as a radar detector that detects electromagnetic emissions from the threat. Since multiple platforms are involved to provide increased observability and also redundancy against possible breakdowns, a WiFi mobile ad hoc network is used for communications. In order to keep up the network in a breakdown OLSR (optimized link state routing) routing approach is employed

Limits of performance of chirped- pulse phase-sensitive OTDR

Distributed acoustic sensing is an emerging field of research which aims to develop methods capable of using a single optical fiber as a long, dense, and high-sensitivity sensor array. Currently, the most promising implementations measure the interference of Rayleigh backscattered light, obtained by probing the fiber with light from a source of high coherence. These methods are known as Phase-sensitive Optical Time-Domain Reflectometers (φOTDR), and are currently undergoing a period of active research and development, both academically and industrially. One of its variants, known as the Chirped-Pulse φOTDR (CP-φOTDR), was developed in 2016. This technique has proven to be remarkably sensitive to strain and temperature, with an attractively simple implementation. In this thesis, we delve into the intricacies of this technique, probing its fundamental limits and addressing current limitations. We discuss the implications of estimation on the performance statistics, the impact of different noise sources and the origin of cross-talk between independent measured positions. In doing so, we also propose methods to reach the current fundamental limitations, and overcome the upper bound of measurable perturbations. We then demonstrate new potential applications of the technique: in seismology, by exploiting the high spatial density of measurements for array signal processing; in the fast characterization of linear birefringence in standard single-mode fibers; and on the measurement of sound pressure waves, by using a special flat cable structure to embed the fiber under test. Finally, we summarize and comment on the aforementioned achievements, proposing some open lines of research that may originate from these results.Distributed acoustic sensing is an emerging field of research which aims to develop methods capable of using a single optical fiber as a long, dense, and highsensitivity sensor array. Currently, the most promising implementations measure the interference of Rayleigh backscattered light, obtained by probing the fiber with light from a source of high coherence. These methods are known as Phase-sensitive Optical Time-Domain Reflectometers (φOTDR), and are currently undergoing a period of active research and development, both academically and industrially. One of its variants, known as the Chirped- Pulse φOTDR (CP-φOTDR), was developed in 2016. This technique has proven to be remarkably sensitive to strain and temperature, with an attractively simple implementation. In this thesis, we delve into the intricacies of this technique, probing its fundamental limits and addressing current limitations. We discuss the implications of estimation on the performance statistics, the impact of different noise sources and the origin of cross-talk between independent measured positions. In doing so, we also propose methods to reach the current fundamental limitations, and overcome the upper bound of measurable perturbations. We then demonstrate new potential applications of the technique: in seismology, by exploiting the high spatial density of measurements for array signal processing; in the fast characterization of linear birefringence in standard single-mode fibers; and on the measurement of sound pressure waves, by using a special flat cable structure to embed the fiber under test. Finally, we summarize and comment on the aforementioned achievements, proposing some open lines of research that may originate from these results

Distributed Source Localization and Tracking Algorithms for Ad-hoc Acoustic Sensor Networks

In this dissertation, we construct an algorithmic framework for systematic tracking of moving sources in large-scale sensor networks. The tracking algorithms we developed generate the estimates of the tracking locations from fusion of space-time data by first fusing the data in space and subsequently by fusing the data in time. Fusion in space is performed by fusing current sensed data that is sufficiently high-quality from the sensor nodes to produce the current source location estimate. These location estimates are indexed as they become available and subsequently fused iteratively in time to produce tracking estimates. Both fusion in space and fusion in time are performed distributively over the ad-hoc sensor network by exploiting distributed algorithms of computation of averages. The distributed tracking algorithms are locally constructed at each participating sensor node exploiting only locally available sensor observations and local available network connectivity information. These algorithms we developed are also resource efficient, scalable, fault-tolerant and can readily adapt to local changes in network topologies. We present methods for optimizing and characterizing the performance of the algorithms as a function of the quality of the sensor measurements, the source dynamics, the sensor density and the network connectivity

Spatiotemporal arrayed MIMO radar

In the last decade, Multiple Input Multiple Output (MIMO) radar has emerged as a leading candidate for stimulating major new advancement in radar theory. A fundamental challenge in MIMO radar is to identify a theoretical framework within which the radar system may be represented and analysed. In the relatively well-established field of Single Input Multiple Output (SIMO) array signal processing, this task has already been achieved using the array manifold (which is a geometric object that completely characterises the array system). A central objective of this thesis is therefore to bridge the gap between SIMO and MIMO by developing a manifold representation of the MIMO radar system. A new differential geometric framework, based on the complex Cartan matrix, is exploited in this thesis for characterising array manifold curves. New formulas are presented for recursively calculating the strictly orthonormal moving frame, U(s), and corresponding complex Cartan Matrix, C(s), for arbitrary array geometries. The circular approximation of the array manifold is derived under this new framework and compact closed-form expressions are provided for the popular uniform linear array geometry. Based on a number of approximations derived using the circular approximation of the array manifold, the performance capabilites of various popular detection and parameter estimation algorithms are investigated. The figure of merit "C" is then used to place these capabilities into the context of the theoretically ideal algorithm. The concept of a virtual SIMO array system is used as a basis for characterising the full MIMO radar configuration using a single equivalent response vector. By tracing out this response vector across the whole parameter space, a manifold is formed that fully characterises the MIMO radar system. In the important case of orthogonal transmit waveforms, the fundamental performance bounds of the MIMO radar system are studied. A space-time receiver architecture is proposed which exploits the virtual SIMO structure as part of a subspace-based joint Doppler, delay and direction of arrival (DOA) estimation framework. Due to the great computational burden of an exhaustive 3-parameter search, the joint Doppler-delay-DOA estimation is partitioned into an equivalent two-stage algorithm. The proposed approach is evaluated via computer simulation studies and shown to outperform existing methods.Open Acces

Performance evaluation and waveform design for MIMO radar

Multiple-input multiple-output (MIMO) radar has been receiving increasing attention in recent years due to the dramatic advantages offered by MIMO systems in communications. The amount of energy reflected from a common radar target varies considerably with the observation angle, and these scintillations may cause signal fading which severely degrades the performance of conventional radars. MIMO radar with widely spaced antennas is able to view several aspects of a target simultaneously, which realizes a spatial diversity gain to overcome the target scintillation problem, leading to significantly enhanced system performance. Building on the initial studies presented in the literature, MIMO radar is investigated in detail in this thesis. First of all, a finite scatterers model is proposed, based on which the target detection performance of a MIMO radar system with arbitrary array-target configurations is evaluated and analyzed. A MIMO radar involving a realistic target is also set up, whose simulation results corroborate the conclusions drawn based on theoretical target models, validating in a practical setting the improvements in detection performance brought in by the MIMO radar configuration. Next, a hybrid bistatic radar is introduced, which combines the phased-array and MIMO radar configurations to take advantage of both coherent processing gain and spatial diversity gain simultaneously. The target detection performance is first assessed, followed by the evaluation of the direction finding performance, i.e., performance of estimating angle of arrival as well as angel of departure. The presented theoretical expressions can be used to select the best architecture for a radar system, particularly when the total number of antennas is fixed. Finally, a novel two phase radar scheme involving signal retransmission is studied. It is based on the time-reversal (TR) detection and is investigated to improve the detection performance of a wideband MIMO radar or sonar system. Three detectors demanding various amounts of a priori information are developed, whose performance is evaluated and compared. Three schemes are proposed to design the retransmitted waveform with constraints on the transmitted signal power, further enhancing the detection performance with respect to the TR approach

Peak Sidelobe Level Distribution Computation for Ad Hoc Arrays using Extreme Value Theory

Extreme Value Theory (EVT) is used to analyze the peak sidelobe level distribution for array element positions with arbitrary probability distributions. Computations are discussed in the context of linear antenna arrays using electromagnetic energy. The results also apply to planar arrays of random elements that can be transformed into linear arrays.Engineering and Applied Science