Browsing by Author "Krolik, Jeffrey L"

In this dissertation, novel uses of millimeter-wave (mmW) radars are developed and analyzed. While automotive mmW radars have been ubiquitous in advanced driver assistance systems (ADAS), their ability to sense motions at sub-millimeter scale allows them to also find application in systems that require accurate measurements of surface vibrations. While laser Doppler vibrometers (LDVs) are routinely used to measure such vibrations, the lower size, weight, power, and cost (SWAPc) of mmW radars make vibrometry viable for a variety of new applications. In this work, we consider two such applications: everything-to-vehicle (X2V) wireless communications and non-acoustic human speech analysis.

Within this dissertation, a wireless communication system that uses the radar as a vibrometer is introduced. This system, termed vibrational radar backscatter communications (VRBC), receives messages by observing phase modulations on the radar signal that are caused by vibrations on the surface of a transponder over time. It is shown that this form of wireless communication provides the ability to simultaneously detect, isolate, and decode messages from multiple sources thanks to the spatial resolution of the radar. Additionally, VRBC requires no RF emission on the end of the transponder. Since automotive radars and the conventional X2V solutions are often at odds for spectrum allocations, this characteristic of VRBC is incredibly valuable.

Using an off-the-shelf, resonant transponder, a real VRBC data collection is presented and used to demonstrate the signal processing techniques necessary to decode a VRBC message. This real data collection proves to achieve a data rate just under 100 bps at approximately 5 meters distance. Rates of this scale can provide warning messages or concise situational awareness information in applications such as X2V, but naturally higher rates are desirable. For that reason, this dissertation includes discussion on how to design a more optimal VRBC system via transponder design, messaging scheme choice, and using any afforded flexibility in radar parameter choice.

Through the use of an analytical upper bound on VRBC rate and simulation results, we see that rates closer to 1 kbps should be achievable for a transponder approximately the size of a license plate at ranges under 200 meters. The added benefits of requiring no RF spectrum or network scheduling protocols uniquely positions VRBC as a desirable solution in spaces like X2V over commonly considered, higher rate solutions such as direct short range communications (DSRC).

Upon implementing a VRBC system, a handful of complications were encountered. This document designates a full chapter to solving these cases. This includes properly modeling intersymbol interference caused by resonant surfaces and utilizing sequence detection methods rather than single symbol maximum likelihood methods to improve detection in these cases. Additionally, an analysis on what an ideal clutter filter should look like and how it can begin to be achieved is presented. Lastly, a method for mitigating platform vibrational noise at both the radar and the transponder are presented. Using these methods, message detection errors are better avoided, though more optimal system design fundamentally proves to limit what rates are achievable.

Towards non-acoustic human speech analysis, it is shown in this dissertation that the vibrations of a person's throat during speech generation can be accurately captured using a mmW radar. These measurements prove to be similar to those achieved by the more expensive vibrometry alternative of an LDV with less than 10 dB of SNR depreciation at the first two speech harmonics in the signal's spectrogram. Furthermore, we find that mmW radar vibrometry data resembles a low-pass filtered version of its corresponding acoustic data. We show that this type of data achieves 53% performance in a speaker identification system as opposed to 11\% in a speech recognition system. This performance suggests potential for a mmW radar vibrometry in context-blind speaker identification systems if the performance of the speaker identification system can be further improved without causing the context of the speech more recognizable.

In this dissertation, mmW radar vibrational returns are modelled and signal processing chains are provided to allow for these vibrations to be estimated and used in application. In many cases, the work outlined could be used in other areas of mmW radar vibrometry even though it was originally motivated by potentially unrelated applications. It is the hope of this dissertation that the provided models, signal processing methods, visualizations, analytical bound, and results not only justify mmW radar in human speech analysis and backscatter communications, but that they also contribute to the community's understanding of how certain vibrational movements can be best observed, processed, and made useful more broadly.

This dissertation presents methods for calibrating and beamforming a distributed array for detecting and localizing sources of interest using passive sonar. Passive sonar is critical for underwater acoustic surveillance, marine life tracking, and environmental monitoring but is increasingly difficult with greater shipping traffic and other man-made noise sources. Large aperture hydrophone arrays are needed to suppress these sources of interference and find weak targets of interest. Traditionally, large hydrophone arrays are densely sampled uniform arrays which are expensive and time-consuming to deploy and maintain. There is growing interest instead in forming distributed arrays out of low-cost, individually small arrays which are coherently processed to achieve high gain and resolution. Conventional array processing methods are not well suited to this end and this dissertation develops new methods for array calibration and beamforming which ultimately enable high resolution passive sonar at low-cost. This work develops estimation methods for array parameters in uncalibrated, unsynchronized collections of acoustic sensors and also develops adaptive beamforming techniques on such arrays in complex and uncertain ocean environments.

Methods for estimating sampling rate offset (SRO) are developed using a single narrowband source of opportunity whose parameters need not be estimated. A search-free method which jointly estimates all SRO parameters in an acoustic sensor network is presented and shown to improve as the network size increases. A second SRO estimation method is developed for unsynchronized sub-arrays to enable SRO estimation with a source that has a bearing rate. This is of particular value in ocean environments where transiting cargo ships are the most prevalent calibration sources.

Next, a technique for continuously estimating multiple sub-array positions using a single, tonal moving source is presented. Identical, well-calibrated sub-arrays with unknown relative positions exhibit a rotational invariance in the signal structure which is exploited to blindly estimate the inter-array spatial wavefronts. These wavefront measurements are used in an Unscented Kalman Filter (UKF) to continuously improve sub-array position estimates.

Lastly, this work addresses adaptive beamforming in uncertain, complex propagation environments where the modeled wavefronts will inevitably not match the true wavefronts. Adaptive beamforming techniques are developed which maintain gain even with significant signal mismatch due to unknown or uncertain source wavefronts by estimating a target-free covariance matrix from the received data and using just a single gain constraint in the beamformer optimization. Target-free covariances are estimated using an eigendecomposition of the received data and assuming that modes which potentially contain sources of interest can be identified. This method is applied to a distributed array where only part of the array wavefront is explicitly modeled and shown to improve interference suppression and the output signal-to-interference-plus-noise ratio (SINR).

This idea is then extended to realistic environments and a method for finding potential target components is developed. Blind source separation (BSS) methods using second-order statistics are adopted for wideband source separation in shallow-water environments. BSS components are associated with either target or interference based on their received temporal spectra and are automatically labeled with a convolutional neural network (CNN). This method is applicable when sources have overlapping but distinct transmitted spectra, but also when the channel itself colors the received spectra due to range-dependent frequency-selective fading. Simulations in realistic shallow-water environments demonstrate the ability to blindly separate and label uncorrelated components based on frequency-selective fading patterns. These simulations then validate the robustness of the developed wavefront adaptive sensing (WAS) beamformer compared to a standard minimum variance distortionless response (MVDR) beamformer. Finally, this method is demonstrated using real shallow-water data from the SWellEx96 S59 experiment off the coast of Southern California. A simulated target is injected into this data and masked behind a loud towed source. It is shown that the WAS beamformer is able to suppress the towed source and achieve an target output SINR which is close to that of the optimal beamformer.

This dissertation presents waveform design and signal processing methods for continuous active sonar (CAS). The work presented focuses on methods for achieving high range, Doppler, and angular resolution, while maintaining a high signal-to-interference plus noise ratio (SINR).

CAS systems transmit at or near 100\% duty cycle for improved update rates compared to pulsed systems. For this reason, CAS is particularly attractive for use in shallow, reverberation-limited environments to provide more ``hits'' to adequately reject false alarms due to reverberation. High resolution is particularly important for CAS systems operating in shallow water for three reasons: (1) To separate target returns from the direct blast, (2) To separate targets from reverberation, and (3) To resolve direct and multipath target returns for maximum SINR. This dissertation presents two classes of high resolution CAS waveform designs and complementary signal processing techniques.

The first class of waveforms presented are co-prime comb signals that achieve high range and Doppler resolution at the cost of range ambiguities. Co-prime combs consist of multiple tones at non-uniformly spaced frequencies according to a 2-level nested co-prime array. Specialized non-matched filter processing enables recovery of a range-velocity response similar to that of a uniform comb, but using fewer tonal components. Cram\'er-Rao Bounds on range and Doppler estimation errors are derived for an arbitrary comb signal and used as a benchmark for comparing three range-velocity processing algorithms. Co-prime comb results from the littoral CAS 2015 (LCAS-15) sea trial are also presented, as well as a strategy to mitigate range ambiguities. An adaptive beamformer that achieves high angular resolution is also presented that leverages the various tonal components of the waveform for snapshot support.

The second class of waveforms presented are slow-time Costas (SLO-CO) CAS signals that achieve high range resolution, but are relatively insensitive to Doppler. SLO-CO CAS signals consist of multiple short duration linear FM (LFM) chirps that are frequency-hopped according to a Costas code. Rapid range updates can be achieved by processing each SLO-CO sub-chirp independently in a cyclical manner. Results from the LCAS-15 trial validate the performance of a SLO-CO signal in a real shallow water environment. A range processing method, novel to sonar, called bandwidth synthesis (BWS) is also presented. This method uses autoregressive modeling together with linear-predictive extrapolation to synthetically extend the bandwidth of received sonar returns. It is shown that BWS results in increased SINR and improved range resolution over conventional matched filtering in the reverberation-limited LCAS-15 environment.

This thesis addresses the problem of source localization and time-varying spatial spectrum estimation with maneuverable arrays. Two applications, each having different environmental assumptions and array geometries, are considered: 1) passive broadband source localization with a rigid 2-sensor array in a shallow water, multipath environment and 2) time-varying spatial spectrum estimation with a large, flexible towed array. Although both applications differ, the processing scheme associated with each is designed to exploit array maneuverability for improved localization and detection performance.

In the first application considered, passive broadband source localization is accomplished via time delay estimation (TDE). Conventional TDE methods, such as the generalized cross-correlation (GCC) method, make the assumption of a direct-path signal model and thus suffer localization performance loss in shallow water, multipath environments. Correlated multipath returns can result in spurious peaks in GCC outputs resulting in large bearing estimate errors. A new algorithm that exploits array maneuverability is presented here. The multiple orientation geometric averaging (MOGA) technique geometrically averages cross-correlation outputs to obtain a multipath-robust TDE. A broadband multipath simulation is presented and results indicate that the MOGA effectively suppresses correlated multipath returns in the TDE.

The second application addresses the problem of field directionality mapping (FDM) or spatial spectrum estimation in dynamic environments with a maneuverable towed acoustic array. Array processing algorithms for towed arrays are typically designed assuming the array is straight, and are thus degraded during tow ship maneuvers. In this thesis, maneuvering the array is treated as a feature allowing for left and right disambiguation as well as improved resolution towards endfire. The Cramer Rao lower bound is used to motivate the improvement in source localization which can be theoretically achieved by exploiting array maneuverability. Two methods for estimating time-varying field directionality with a maneuvering array are presented: 1) maximum likelihood estimation solved using the expectation maximization (EM) algorithm and 2) a non-negative least squares (NNLS) approach. The NNLS method is designed to compute the field directionality from beamformed power outputs, while the ML algorithm uses raw sensor data. A multi-source simulation is used to illustrate both the proposed algorithms' ability to suppress ambiguous towed-array backlobes and resolve closely spaced interferers near endfire which pose challenges for conventional beamforming approaches especially during array maneuvers. Receiver operating characteristics (ROCs) are presented to evaluate the algorithms' detection performance versus SNR. Results indicate that both FDM algorithms offer the potential to provide superior detection performance in the presence of noise and interfering backlobes when compared to conventional beamforming with a maneuverable array.

The Slow-time Costas or "SLO-CO" Continuous Active Sonar (CAS) waveform shows promise for enabling high range and velocity revisit rates and wideband processing gains while suppressing range ambiguities. SLO-CO is made up of non-recurrent wideband linear FM chirps that are frequency staggered according to a Costas code across the pulse repetition interval. SLO-CO is shown to provide a near-thumbtack ambiguity functions with controllable sidelobes, good Doppler and range resolution at high revisit rates. The performance of the SLO-CO waveform was tested using the Sonar Simulation Toolset (SST) as well as in the shallow water Target and Reverberation Experiment 2013 (TREX13). For both the real and simulated results, the performance of the SLO-CO is compared to the conventional CAS waveform. Amplitude-Range-Velocity (ARV) processing of SLO-CO experimental trials reveal that relatively high direct blast sidelobes mask the target peak. Methods of suppressing the direct blast are discussed including adaptive filtering and re-designing the waveform.

This dissertation presents optimal signal processing methods and performance analysis for passive, waveguide invariant (WI)-based acoustic source range estimation in shallow water marine environments. The WI, commonly denoted by β, characterizes the range- and frequency-varying channel fading pattern that can be observed in the time-frequency spectrum of hydrophone data. The structure of the fading pattern is governed by the physics of ducted acoustic propagation and can be exploited to estimate source range using a variety of methods; this work focuses on model-based, single-hydrophone techniques for both narrowband (tonal) as well as broadband sources.

Maximum likelihood (ML) estimators are presented for both β and source range for the case of tonal sources. Estimator performance is analyzed for various signal-to-noise ratios (SNRs) and numbers of tones processed in both Pekeris and complex environments using the KRAKEN normal mode program. Acoustic data from the SWellEx-96 experiment is analyzed, and source range is estimated with root-mean-square error (RMSE) under 3% of source range using knowledge of β for the local environment and 6% using an estimate β obtained from an area several kilometers away.

The Cram´er-Rao lower bound (CRLB) on achievable variance of unbiased range and β estimates is derived for the case of a broadband source in an ideal waveguide and is seen to exhibit similar trends as the performance curves for the ML estimators derived for tonal sources. Additionally, an example is provided showing how the framework for derivation of the bounds can be extended to a complex environment modeled after the SWellEx-96 experiment.

Receiver localization can be performed by combining the time-varying WI-based range estimates with knowledge of the source track, and this has a potentially significant application to autonomous underwater vehicle (AUV) navigation. To this end, three receiver localization methods are presented that use either the Doppler effect, WI-based range estimates, or both. Results from Monte Carlo simulations as well as from processing experimental data demonstrate the potential to localize AUVs with an error on the order of a few hundred meters under realistic assumptions regarding source and environmental parameters.

Shallow water, coastal regions with high volumes of shipping traffic provide an excellent opportunity to passively characterize the ocean acoustic propagation environment. In this paper, a hybrid maximum-likelihood method is presented for estimating the value of the waveguide invariant parameter, $\beta$, which succinctly characterizes the interference structure inherent to ducted acoustic propagation. A similar method is also presented by which the range can be estimated to a tonal source that later transits a shallow water region that has previously been characterized by $\beta$. This paper focuses solely on tonal acoustic sources, exploiting one of the defining characteristics of cargo ship emissions. The methods presented require minimal a priori environmental knowledge and relatively few assumptions regarding the acoustic sources. $\beta$ is estimated through spectral analysis of the fading pattern of a received acoustic signal from a transiting cargo ship that broadcasts its GPS location through Automatic Identification System (AIS) data. Range is estimated using a similar method, but also requiring a rough source velocity estimate. Close agreement is seen between simulated results obtained using Kraken and experimental results using data from the Swellex ’96 experiment, in which a shallow source was used to estimate the range to a deep source.

Radar systems are increasingly being deployed in environments where identifying targets of interest is complicated by complex propagation, complex scattering from clutter, or both. Recent radar sensing involving complex environments have a wide range of applications such as: autonomous vehicle collision avoidance, stand-off human health monitoring, and urban surveillance. This dissertation develops and demonstrates new radar signal processing methods for two important problems: human gait estimation and unmanned aerial system (UAS) detection, classification, and tracking. In each of these applications, radars must overcome challenges associated with operating in complex environments, whether they are due to multipath propagation and/or clutter which is difficult to distinguish from targets of interest.

Gait biometric identification in a scattering rich environment is a notoriously difficult problem because features or parameters useful for identification can be difficult to estimate with traditional methods due to multipath scattering. This dissertation shows in simulation that body part detection and micro-Doppler feature extraction can be performed using a wideband, high-resolution radar. The similarity between a pendulum model and a limb swinging is discussed and a real pendulum experiment is used to show proof of concept of how the bidirectional spectrum can be used to exploit multipath to help estimate physically meaningful features that be used for biometric identification.

Extreme clutter inhomogeneity in urban environments precludes unmanned aerial system detection and classification using conventional radar array processing methods. Conventional array processing methods typically rely on the availability of signal-free training data from ranges neighboring the range bin of interest. Due to clutter inhomogeneity, this signal-free data from neighboring range bins can not be relied upon in urban environments. This dissertation addresses this problem by tightly integrating a neural network (NN) classifier and blind source separation (BSS) to construct a signal-free covariance matrix using only data from each range bin of interest. It is demonstrated that adaptive beamforming for effective clutter suppression and improved small target detection can be achieved using this method. A technique for automatically labeling radar training and test data using video data is also presented.

For slow, small targets in the presence of other non-target movers, data association is arguably the most difficult part of the tracking problem. In traditional trackers, measurements are associated with existing tracks based on proximity based distance measures. This dissertation presents a recurrent neural network (RNN) based tracker which uses learned target dynamic models, a proximity based distance measure, and classification scores to associate measurements with target tracks. Classification scores for each radar measurement are generated by a NN trained using in situ "pattern-of-life" radar data. These scores together with the distances are then passed to RNN trackers with long-short-term-memory (LSTM), trained for each object class. Simulated and real data results indicate that classification scores significantly improves small UAS track holding times in urban environments.

Pulsed radars have an ambiguous relationship between range and velocity which is proportional to the pulse repetition frequency (PRF), leading to potential tradeoffs. High PRFs are necessary to avoid velocity aliasing but suffer at the expense of unambiguous range. Obscuration due to ambiguous range foldover from distant clutter echos seriously degrades target detectability. For the case of skywave HF over-the-horizon (OTH) radar, ionospheric motion causes spreading of surface clutter in Doppler space and coupled with range folded clutter, introduces the effect of so-called 'separated' spread Doppler clutter (SDC). Selection of a nonrecurrent waveform (NRWF) with a quadratic phase interpulse code has been shown to mitigate long-range SDC by folding the multi-hop returns into known disassociated Doppler regions.

Utilizing multiple receive elements, spatial processing can be preformed to exploit the correlation between spatial frequency and Doppler shift produced by ionospheric winds. Adaptive beamforming is known to provide asymptotically optimal array gain if sufficient training data is available. In highly nonstationary environments however, obtaining this asymptotic performance is difficult as neither knowledge of the target wavefront nor signal-free training data is easily obtainable for training. A blind adaptive spatial processing (BASP) technique has been proposed, combining minimum variance (MV) adaptive beamforming and blind source separation (BSS). The unique idea of BASP is the formulation of a signal-free covariance matrix from BSS clutter and noise components at a single range bin, and utilizing it in adaptive beamforming to suppress clutter.

This research explores a clutter mitigation method that will combine NRWF and BASP in order to recover targets masked by Doppler-spread surface backscatter from points beyond the radar's maximum unambiguous range while maintaining target detectability elsewhere in Doppler. Current methods for mitigating range-folded clutter, such as reducing the pulse-repetition frequency or the use of non-recurrent waveforms, suffer loss in the usable Doppler space available for target detection or a reduction in target revisit rate. The proposed research uses BSS methods to exploit the known Doppler separation afforded by NRWF in order to estimate the spatial wavefront of the clutter across a linear receive array. Spatial adaptive processing using this estimated wavefront is then used to suppress range-folded clutter at each range bin without sacrificing the radar timeline or usable Doppler space.

Simulation is conducted to understand the NRWF and its ability to separate range-folded clutter in Doppler. The BASP method is applied to the NRWF and its results demonstrate performance improvement in terms of achievable signal-to-clutter and noise ratio gain. Laboratory experimental results show the NRWF's ability to separate range-folded clutter into designed Doppler regions. BASP is then applied and demonstrated to mitigate the separated range folded clutter and recover usable Doppler space.

Human motion monitoring is an important function in numerous applications. In this dissertation, two systems for monitoring motions of multiple human targets in wide-area indoor environments are discussed, both of which use radio frequency (RF) signals to detect, localize, and classify different types of human motion. In the first system, a coherent monostatic multiple-input multiple-output (MIMO) array is used, and a joint spatial-temporal adaptive processing method is developed to resolve micro-Doppler signatures at each location in a wide-area for motion mapping. The downranges are obtained by estimating time-delays from the targets, and the crossranges are obtained by coherently filtering array spatial signals. Motion classification is then applied to each target based on micro-Doppler analysis. In the second system, multiple noncoherent multistatic transmitters (Tx's) and receivers (Rx's) are distributed in a wide-area, and motion mapping is achieved by noncoherently combining bistatic range profiles from multiple Tx-Rx pairs. Also, motion classification is applied to each target by noncoherently combining bistatic micro-Doppler signatures from multiple Tx-Rx pairs. For both systems, simulation and real data results are shown to demonstrate the ability of the proposed methods for monitoring patient repositioning activities for pressure ulcer prevention.

The spatial spectrum, also known as a field directionality map, is a description of the spatial distribution of energy in a wavefield. By sampling the wavefield at discrete locations in space, an estimate of the spatial spectrum can be derived using basic wave propagation models. The observable data space corresponding to physically realizable source locations for a given array configuration is referred to as the array manifold. In this thesis, array manifold ambiguities for linear arrays of omni-directional sensors in non-dispersive fields are considered.

First, the problem of underwater a hydrophone array towed behind a maneuvering platform is considered. The array consists of many hydrophones mounted to a flexible cable that is pulled behind a ship. The towed cable will bend or distort as the ship performs maneuvers. The motion of the cable through the turn can be used to resolve ambiguities that are inherent to nominally linear arrays. The first significant contribution is a method to estimate the spatial spectrum using a time-varying array shape in a dynamic field and broadband temporal data. Knowledge of the temporal spectral shape is shown to enhance detection performance. The field is approximated as a sum of uncorrelated planewaves located at uniform locations in angle, forming a gridded map on which a maximum likelihood estimate for broadband source power is derived. Uniform linear arrays also suffer from spatial aliasing when the inter-element spacing exceeds a half-wavelength. Broadband temporal knowledge is shown to significantly reduce aliasing and thus, in simulation, enhance target detection in interference dominated environments.

As an extension, the problem of towed array shape estimation is considered when the number and location of sources are unknown. A maximum likelihood estimate of the array shape using the field directionality map is derived. An acoustic-based array shape estimate that exploits the full 360$^\circ$ field via field directionality mapping is the second significant contribution. Towed hydrophone arrays have heading sensors in order to estimate array shape, but these sensors can malfunction during sharp turns. An array shape model is described that allows the heading sensor data to be statistically fused with heading sensor. The third significant contribution is method to exploit dynamical motion models for sharp turns for a robust array shape estimate that combines acoustic and heading data. The proposed array shape model works well for both acoustic and heading data and is valid for arbitrary continuous array shapes.

Finally, the problem of array manifold ambiguities for static under-sampled linear arrays is considered. Under-sampled arrays are non-uniformly sampled with average spacing greater than a half-wavelength. While spatial aliasing only occurs in uniformly sampled arrays with spacing greater than a half-wavelength, under-sampled arrays have increased spatial resolution at the cost of high sidelobes compared to half-wavelength sampled arrays with the same number of sensors. Additionally, non-uniformly sampled arrays suffer from rank deficient array manifolds that cause traditional subspace based techniques to fail. A class of fully agumentable arrays, minimally redundant linear arrays, is considered where the received data statistics of a uniformly spaced array of the same length can be reconstructed in wide sense stationary fields at the cost of increased variance. The forth significant contribution is a reduced rank processing method for fully augmentable arrays to reduce the variance from augmentation with limited snapshots. Array gain for reduced rank adaptive processing with diagonal loading for snapshot deficient scenarios is analytically derived using asymptotic results from random matrix theory for a set ratio of sensors to snapshots. Additionally, the problem of near-field sources is considered and a method to reduce the variance from augmentation is proposed. In simulation, these methods result in significant average and median array gains with limited snapshots.

In this work, we present the Simultaneous Target and Multipath Positioning (STAMP) technique to jointly estimate the unknown target position and uncertain multipath channel parameters. We illustrate the applications of STAMP for target tracking/geolocation problems using single-station hybrid TOA/AOA system, monostatic MIMO radar and multistatic range-based/AOA based localization systems. The STAMP algorithm is derived using a recursive Bayesian framework by including the target state and multipath channel parameters as a single random vector, and the unknown correspondence between observations and signal propagation channels is solved using the multi-scan multi-hypothesis data association. In the presence of the unknown time-varying number of multipath propagation modes, the STAMP algorithm is modified based on the single-cluster PHD filtering by modeling the multipath parameter state as a random finite set. In this case, the target state is defined as the parent process, which is updated by using a particle filter or multi-hypothesis Kalman filter. The multipath channel parameter is defined as the daughter process and updated based on an explicit Gaussian mixture PHD filter. Moreover, the idenfiability analysis of the joint estimation problem is provided in terms of Cramér-Rao lower bound (CRLB). The Fisher information contributed by each propagation mode is investigated, and the effect of Fisher information loss caused by the measurement origin uncertainty is also studied. The proposed STAMP algorithms are evaluated based on a set of illustrative numeric simulations and real data experiments with an indoor multi-channel radar testbed. Substantial improvement in target localization accuracy is observed.

Estimation of a time-varying field is essential for situational awareness in many subject areas. Adaptive processing often assumes both the field is stationary and the array is fixed for multiple observation windows. For passive sonar, highly dynamic scenarios such as high bearing rate sources or underwater maneuvers severely limit the utilization of multiple snapshots. Several models are considered for time-varying fields, and a broadband maximum-likelihood estimator is introduced that is solved with an expectation maximization algorithm using as few as one snapshot. The number of estimated parameters can be reduced for broadband data when information, such as shape, is known about the source temporal spectrum. Cramér-Rao bound analysis is used to understand the effects of temporal spectrum knowledge on broadband processing. An example is given for the flat spectrum case to compare with conventional processing. Another feature of dynamic environments is array motion. Since underwater arrays are often subject to motion, the estimate must consider arbitrary, dynamic array shapes. Platforms such as autonomous underwater vehicles provide mobility but constrain the number of sensors. Exploiting a maneuverable linear array with the new estimate allows for left-right or front-back disambiguation and suppression of spatial grating lobes. Multi-source simulations are used to demonstrate the ability of a short, maneuvering array to reduce array backlobes as well as operate in the spatial grating lobe region.

In this thesis, we develop methods for addressing the deficiencies of array processing with linear thinned arrays. Our methods are designed for array systems mounted on moving platforms and exploit synthetic aperture processing techniques. In particular, we use array motion to decrease the sidelobe levels and increase the degrees of freedom available from thinned array systems. In this work, we consider two application areas 1) passive SONAR and 2) ultrasound imaging.

Synthetic aperture processing is a methodology for exploiting array motion and has been successfully used in practice to increase array resolution. By spatially sampling along the path of the array virtual sensors can be realized and coherently fused to the existing array. The novel contribution of this work is our application of synthetic aperture processing. Here our goal is not to increase array resolution, instead we propose to use the synthetic aperture process to expand the spatial covariance and spatial frequency sensing capabilities of thinned array system.

In the passive sensing case, we use a class of thinned arrays know as co-prime linear sensor arrays for source localization. The class of co-prime arrays provides roughly half the aperture worth of spatial covariances and with modest array motion can be extended to the full aperture of the array. The amount of motion required to produce a full set of spatial covariances is shown to be a function of the co-prime array parameters and is only a fraction of the total aperture of the array. The full set of spatial covariances can be used to form a spatial covariance matrix with dimension equal to that of a uniform array. With a spatial covariance matrix in hand one can perform signal processing tasks as if the array were fully populated. Three methods for spatial covariance matrix estimation are compared in different source localization scenarios. In the work presented here, we demonstrate the benefits of our approach for achieving reduced sidelobe levels and extending the source localization capabilities above the limits of the static co-prime array.

In the active sensing case, we develop a framework for incorporating motion using thinned arrays for ultrasound imaging. In this setting, array motion is used to augment the spatial frequency sensing capabilities of the thinned array system. Here we develop an augmentation strategy based on using quarter-wavelength array translations to fill-in missing spatial frequencies not measured by the static thinned array. The quarter-wavelength translation enables the thinned array system to sample missing spatial frequencies and increase the redundancy of other spatial frequencies sampled by the array. We compare the level of redundancy in sampling the spatial frequencies achieved by the thinned arrays post translation to different levels of sample redundancy derived from pruning the transmit/receive events of a uniform array. In this manner, we are able to examine how the level of spatial frequency redundancy afforded by different thinned arrays compare over the full redundancy range of the uniform array. While artificially pruning the uniform array does not necessarily create realizable arrays, it provides the means to compare image quality at different spatial frequency redundancy levels. In this work, we are able to conclude that images formed from thinned arrays using the translated synthetic aperture process are capable of approximating images formed from the corresponding uniform array. In particular, the systems considered in this work have approximately one-third of the active sensors when compared to the uniform array.

In both application areas, the use of thinned arrays offers a reduction in the cost to deploy and maintain a given array system. The feature that makes it possible to overcome the spatial sampling deficiencies of thinned array systems is motion and it is at the core of the performance gains in these applications.

Radar systems have long applied electronically-steered phased arrays to discriminate returns in azimuth angle and elevation angle. On receiver arrays, beamforming is performed after reception of the data, allowing for many adaptive array processing algorithms to be employed. However, on transmitter arrays, up until recently pre-determined phase shifts had to applied to each transmitter element before transmission, precluding adaptive transmit array processing schemes. Recent advances in multiple-input multiple-output radar techniques have allowed for transmitter channels to separated after data reception, allowing for virtual non-causal "after-the-fact" transmit beamforming. The ability to discriminate in both direction-of-arrival and direction-of-departure allows for the novel ability to discriminate line-of-sight returns from multipath returns. This works extends the concept of virtual non-causal transmit beamforming to the broader concept of a bi-directional spatial spectrum, and describes application of such a spectrum to applications such as spread-Doppler multipath clutter mitigation in ground-vehicle radar, and calibration of a receiver array of a MIMO system with ground clutter only. Additionally, for this work, a low-power MIMO radar testbed was developed for lab testing of MIMO radar concepts.

Backscatter communication is an emerging wireless technology that recently has gained an increase in attention from both academic and industry circles. The key innovation of the technology is the ability of ultra-low power devices to utilize nearby existing radio signals to communicate. As there is no need to generate their own energetic radio signal, the devices can benefit from a simple design, are very inexpensive and are extremely energy efficient compared with traditional wireless communication. These benefits have made backscatter communication a desirable candidate for distributed wireless sensor network applications with energy constraints.

The backscatter channel presents a unique set of challenges. Unlike a conventional one-way communication (in which the information source is also the energy source), the backscatter channel experiences strong self-interference and spread Doppler clutter that mask the information-bearing (modulated) signal scattered from the device. Both of these sources of interference arise from the scattering of the transmitted signal off of objects, both stationary and moving, in the environment. Additionally, the measurement of the location of the backscatter device is negatively affected by both the clutter and the modulation of the signal return.

This work proposes a channel coding framework for the backscatter channel consisting of a bi-static transmitter/receiver pair and a quasi-cooperative transponder. It proposes to use run-length limited coding to mitigate the background self-interference and spread-Doppler clutter with only a small decrease in communication rate. The proposed method applies to both binary phase-shift keying (BPSK) and quadrature-amplitude modulation (QAM) scheme and provides an increase in rate by up to a factor of two compared with previous methods.

Additionally, this work analyzes the use of frequency modulation and bi-phase waveform coding for the transmitted (interrogating) waveform for high precision range estimation of the transponder location. Compared to previous methods, optimal lower range sidelobes are achieved. Moreover, since both the transmitted (interrogating) waveform coding and transponder communication coding result in instantaneous phase modulation of the signal, cross-interference between localization and communication tasks exists. Phase discriminating algorithm is proposed to make it possible to separate the waveform coding from the communication coding, upon reception, and achieve localization with increased signal energy by up to 3 dB compared with previous reported results.

The joint communication-localization framework also enables a low-complexity receiver design because the same radio is used both for localization and communication.

Simulations comparing the performance of different codes corroborate the theoretical results and offer possible trade-off between information rate and clutter mitigation as well as a trade-off between choice of waveform-channel coding pairs. Experimental results from a brass-board microwave system in an indoor environment are also presented and discussed.

Reverberation and clutter are two of the principle obstacles to active sonar target detection in shallow water. Diffuse seabed backscatter can obscure low energy target returns, while clutter discretes, specific features of the sea floor, produce temporally compact returns which may be mistaken for targets of interest. Detecting weak targets in the presence of reverberation and discriminating water column targets from bottom clutter are thus critical to good performance in active sonar. Both problems are addressed in this thesis using the time-frequency interference pattern described by a constant known as the waveguide invariant which summarizes in a scalar parameter the dispersive properties of the ocean environment.

Conventional active sonar detection involves constant false alarm rate (CFAR) normalization of the reverberation return which does not account for the frequency-selective fading in a wideband pulse caused by multipath propagation. An alternative to conventional reverberation estimation is presented, motivated by striations observed in time-frequency analysis of active sonar data. A mathematical model for these reverberation striations is derived using waveguide invariant theory. This model is then used to motivate waveguide invariant reverberation estimation which involves averaging the time-frequency spectrum along these striations. An evaluation of this reverberation estimate using real Mediterranean data is given and its use in a generalized likelihood ratio test (GLRT) based CFAR detector is demonstrated. CFAR detection using waveguide invariant reverberation estimates is shown to outperform conventional cell-averaged and frequency-invariant CFAR detection methods in shallow water environments producing strong reverberation returns which exhibit the described striations. Results are presented on simulated and real Mediterranean data from the SCARAB98 experiment.

The ability to discriminate between water column targets and clutter discretes is vital to maintaining low false alarm rates in active sonar. Moreover, because of the non-stationarity of the active sonar return, classification is most typically achieved using a single snapshot of test data. As an aid to classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide-invariant spectral density matrix (WI-SDM) which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is performed by a waveguide-invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) and averaging the signal replica vectors over the unknown channel parameters. Simulation and real data results from the SCARAB98, CLUTTER07, and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach.