In continuous wave (CW) radar systems, multiple signal copies impinge the receiver simultaneously. Often, undesired multipath and direct-path copies are many times stronger than potential targets. When applying matched filter signal processing techniques, the undesired signal components can mask weaker targets and decrease performance of post-processing techniques, such as target indication or estimation. In this manuscript, we propose a method of rejecting multipath-scattered returns over a continuous region in range and Doppler. We explore the computational cost of this method and additionally propose an approximate method of rejection which leverages the well-known discrete prolate spheroidal sequences (DPSS)-typically referred to as Slepian sequences-to gain a computational advantage. Results are shown to decrease the effective noise floor when applying matched filtering techniques as well as increase target signal-to-interference-plus-noise ratio (SINR) outside of an undesired multipath region. Comparisons are shown to traditional CW multipath removal in terms of rejection performance and run-time.

In distributed sensing applications, the nodes of a sensor network cooperatively detect and localize targets of interest. In particular, active multiple-input multipleoutput (MIMO) radar (AMR) uses multiple transceivers to transmit separable signals and receive the scattered returns, while passive MIMO radar (PMR) uses multiple receivers to receive the direct-path (transmitter-to-receiver) and target-path (transmitter-to-target-to-receiver) signals originated by multiple non-cooperative transmitters to detect and localize targets. This chapter surveys recent results in the theory of centralized detection in PMR networks. Generalized likelihood ratio test (GLRT)-based detectors for PMR detection have been developed and their performances are compared to related detectors for AMR and passive source localization (PSL) sensor networks. PMR detection sensitivity and ambiguity are then analyzed as a function of both the target-path and direct-path signals-to-noise ratios (SNRs). The results demonstrate that PMR detection sensitivity and ambiguity approach that of active MIMO radar sensor networks when the direct-path SNRs are sufficiently high. Conversely, PMR detection sensitivity and ambiguity approximate that of passive source localization sensor networks when the direct-path SNRs are sufficiently low. In this way, PMR networks unify these related active and passive sensor network architectures in a common theoretical framework.

This paper extends prior work on target position and velocity estimation in active multiple-input multiple-out (MIMO) radar networks by introducing multichannel receivers into the problem formulation. The maximum likelihood estimate (MLE) of target position and velocity is derived assuming all received time-series can be aggregated at a central processor. This is in contrast to methods that first make range, Doppler, and/or angle-of-arrival (AoA) estimates at each receiver. The corresponding Cramér-Rao lower bound (CRLB) is also derived for this problem. Monte Carlo simulations demonstrate that the MLE can attain the CRLB at reasonable signal-to-noise ratios (SNRs) and position/velocity estimation accuracy is improved with respect to the single-channel receiver case.

Bistatic clutter measurement requires accurate characterization of the incident and scattering geometry, resolution cell area, and terrain type. This paper presents a methodology to characterize the geometry and terrain type of bistatic clutter range-Doppler resolution cells over uneven terrain using digital elevation map (DEM) and Land-Use Land-Cover (LULC) data. A procedure to calculate the local ground plane using DEM data is presented, which enables calculation of the incident and scattering angles relative to the ground plane normal vector. Then, closed form expressions for a parallelogram approximation to the bistatic resolution cell area are presented for arbitrary bistatic geometries. Finally, terrain type is classified using LULC data. These procedures are illustrated using one acquisition from the Multi-Channel Airborne Radar Measurement (MCARM) bistatic clutter collections.

This paper considers detection in passive multiple-input multiple-output (MIMO) radar sensor networks. Multiple centralized and decentralized detection architectures are surveyed and compared via Monte Carlo simulation. A recently proposed generalized likelihood ratio test (GLRT) detector, termed the reference-surveillance GLRT, is shown to have superior detection performance because it maximally exploits the correlations in the measured data. Specifically, it exploits inter-receiver reference-surveillance correlations and inter-receiver surveillance-surveillance correlations, two concepts that are explained in this paper. In this way, the reference-surveillance GLRT achieves better sensitivity under all direct-path-to-noise ratio conditions than more conventional matched filter-inspired detection approaches, which exploit only some of the correlations within the measured data.

This paper considers target localization in passive multiple-input multiple-output (MIMO) radar sensor networks comprised of multiple non-cooperative transmitters and multiple multichannel receivers. The maximum likelihood estimator is derived for direct estimation of target position and velocity in Cartesian space using all measurement data. Localization performance is shown to vary significantly as a function of both target-path SNR and direct-path SNR. In addition to angle information, this estimator is shown to make use of bistatic range, bistatic Doppler, time-difference-of-arrival, and frequency-difference-of-arrival information to varying degrees depending on the target-path and direct-path SNR regime.

This work addresses the problem of target detection in passive multiple-input multiple-output (MIMO) radar networks without utilization of direct-path reference signals. A generalized likelihood ratio test for this problem is derived, and the distribution of the test statistic is identified under both hypotheses. Equivalence is established between passive MIMO radar networks without references and passive source localization networks. Numerical examples demonstrate important characteristics of the detector, namely, the asymmetric contributions to detection performance from transmitters and receivers, and non-coherent integration gain as a function of signal length. The ambiguity properties of this detector are also investigated, and it is shown that the salient ambiguities can be explained in terms of the time-difference of arrival, frequency-difference of arrival, and angle-of-arrival of the target signals.

This paper addresses the problem of detecting an unknown rank-one signal using multiple receivers that are uncalibrated in the sense that they each apply an unknown scaling to the received signal, and their respective noise powers are unknown. This problem has been addressed for the case in which the unknown signal can be modeled as a Gaussian random vector. However, that assumption is not applicable to some signal types, such as the constant modulus signals found in radar and communications. For these problems, the signal can be modeled as a deterministic unknown, which is the approach taken here. We derive a generalized likelihood ratio test for this problem under a low signal-to-noise ratio (SNR) assumption. The resulting detector is invariant to relative scalings of the data, and therefore possesses the constant false alarm rate (CFAR) property with respect to the unknown noise powers. Numerical examples show the proposed detector can outperform CFAR detectors derived under the Gaussian assumption.

This work considers centralized detection in distributed RF sensor networks. We present a comparative analysis of GLRT detection in active and passive networks including active multiple-input multiple-output (MIMO) radar (AMR), passive MIMO radar (PMR), and passive source localization (PSL). Our results demonstrate that PMR generalizes AMR and PSL in that PMR detection sensitivity may approximate that of AMR or PSL depending on the average direct-path-to-noise ratio (DNR). At high DNR, PMR sensitivity equals AMR sensitivity. At low DNR, PMR sensitivity approximates PSL sensitivity. At intermediate DNRs, PMR sensitivity transitions from PSL to AMR sensitivity with increasing DNR. Thus, PMR may be regarded as the link between AMR and PSL sensor networks that unifies them within a common theoretical framework.

: Passive multiple-input multiple-output (MIMO) radar is a sensor network comprised of multiple distributed receivers that detects and localizes targets using the emissions from multiple non-cooperative radio frequency transmitters. This dissertation advances the theory of centralized passive MIMO radar (PMR) detection by proposing two novel generalized likelihood ratio test (GLRT) detectors. The first addresses detection in PMR networks without direct-path signals. The second addresses detection in PMR networks with direct-path signals. The probability distributions of both test statistics are investigated using recent results from random matrix theory. Equivalence is established between PMR networks without direct-path signals and passive source localization (PSL) networks. Comparison of both detectors with a centralized GLRT for active MIMO radar (AMR) detection reveals that PMR may be interpreted as the link between AMR and PSL sensor networks. In particular, under high direct-path-tonoise ratio (DNR) conditions, PMR sensitivity and ambiguity approaches that of AMR. Under low-DNR conditions, PMR sensitivity and ambiguity approaches that of PSL. At intermediate DNRs, PMR sensitivity and ambiguity smoothly varies between that of AMR and PSL. In this way, PMR unifies PSL and AMR within a common theoretical framework. This result provides insight into the fundamental natures of active and passive distributed sensing.

Space-time adaptive processing (STAP) is used in radar to adaptively suppress both ground clutter returns and radio frequency interference (RFI). However, RFI suppression utilizes degrees-of-freedom that would otherwise be applied to clutter suppression. This paper considers designing the transmit pulse so that fast-time correlated (i.e., colored) RFI is suppressed in the pulse-compression stage preceding the STAP processor. The objective function and constraints for the waveform optimization are derived, and a numerical example demonstrating the efficacy of the approach is provided.

Conventional passive multistatic radar systems, which are comprised of multiple transmitters and receivers, detect and localize targets in a two-stage process. First, detections are performed independently for each bistatic transmit-receive pair. A multilateration process then uses the resulting bistatic range estimates to localize the targets in Cartesian space. Multilateration results in additional false “ghost” targets, which must be removed by a subsequent process. This paper presents a single-stage approach that performs detection, localization, and deghosting directly in Cartesian space. This approach is the generalized likelihood ratio test (GLRT) for scintillating targets, which provides improved probability of detection compared to the conventional approach by making use of available diversity gain. Furthermore, as target localization and deghosting are performed implicitly, separate localization and deghosting processes are not required. Detection performance equations are provided, as are numerical examples illustrating the inherent localization and deghosting nature of the proposed architecture.

The source localization problem concerns the detection and localization of an emitter whose transmission is observed by geographically separated receivers. The passive multistatic radar (PMR) problem concerns the detection and localization of a target that scatters an illumination signal to geographically separated receivers. By modeling the scattering target as an emitter, the techniques of source localization can be applied to the PMR problem. Indeed, this approach has recently been introduced in the literature. However, the exact relationship between the two problems has not been made explicit. In this work, we derive a centralized generalized likelihood ratio test (GLRT) detector that performs the processing characteristic of both source localization and PMR. This detector is used to assess the relative detection benefit provided by source localization in PMR. We show that source localization techniques are of limited utility in PMR due to the SNR regimes of the target-scattered and direct-path signals typical of the PMR signal environment.

Modern remote sensing applications, including radar, require thoughtful consideration of electromagnetic principles for constructing advanced signal processing algorithms for detection, estimation, tracking, imaging and feature extraction. Starting from a generalized picture of the distributed sensing paradigm, this paper presents current efforts at the Radar Instrumentation Laboratory, Air Force Institute of Technology to develop efficient target models for use in integrated imaging and feature extraction algorithms for fully polarimetric synthetic aperture radar (SAR). In continuing the development of anisotropic target models, closed form expressions are presented for canonical objects, and using both simulation and measurement from the Gotcha challenge data, a new and efficient integrated algorithm is demonstrated for finding distributed objects in SAR imagery.

This paper investigates the performance of single-channel SAR-GMTI systems in the focusing and detection of translating ground targets moving in the presence of a clutter background. Specifically, focusing and detection performance is investigated by applying the Moving Grid Processing (MGP) focusing technique to a scene containing an accelerating target moving in the presence of both uniform and correlated K-distributed clutter backgrounds. The increase in detection sensitivity resulting from the focusing operation is found to result from two separable effects, target focusing and clutter defocusing. While the detection sensitivity gain due to target focusing is common for both clutter types, the gain due to clutter defocusing is found to be significantly greater for textured clutter than for uniform clutter, by approximately 5 to 6 dB in the simulated scenario under consideration. This paper concludes with a discussion of the phenomenological causes for this difference and implications of this finding for single channel SAR-GMTI systems operating in heterogeneous clutter environments.

This paper investigates the relationship between a ground moving target's kinematic state and its SAR image. While effects such as cross-range offset, defocus, and smearing appear well understood, their derivations in the literature typically employ simplifications of the radar/target geometry and assume point scattering targets. This study adopts a geometrical model for understanding target motion effects in SAR imagery, termed the target migration path, and focuses on experimental verification of predicted motion effects using both simulated and empirical datasets based on the Gotcha GMTI challenge dataset. Specifically, moving target imagery is generated from three data sources: first, simulated phase history for a moving point target; second, simulated phase history for a moving vehicle derived from a simulated Mazda MPV X-band signature; and third, empirical phase history from the Gotcha GMTI challenge dataset. Both simulated target trajectories match the truth GPS target position history from the Gotcha GMTI challenge dataset, allowing direct comparison between all three imagery sets and the predicted target migration path. This paper concludes with a discussion of the parallels between the target migration path and the measurement model within a Kalman filtering framework, followed by conclusions.