Daniel E. Hack

Centralized Passive MIMO Radar Detection Without Direct-Path Reference Signals

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.

Direct Cartesian detection, localization, and de-ghosting for passive multistatic radar

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.

Multichannel Detection of an Unknown Rank-N Signal Using Uncalibrated Receivers

This letter addresses the problem of detecting an unknown rank-N signal using multiple receivers that are uncalibrated in the sense that each applies an unknown scaling to the received signal and the (possibly unequal) receiver noise powers are unknown. This problem has been addressed for the case in which the signal can be modeled as a linear combination of N Gaussian random vectors. We consider the alternative approach of modeling the signal as a deterministic unknown. We derive an approximate generalized likelihood ratio test (GLRT) for low signal-to-noise ratios (SNRs). The resulting detector is invariant to relative scalings of the data, and is therefore constant false alarm rate (CFAR) with respect to the unknown noise powers. Numerical examples show this low-SNR GLRT performs well at all SNRs and can outperform other CFAR detectors when N = 1. However, CFAR detectors derived assuming unknown Gaussian signals appear to perform better for N > 1.

Multichannel detection of an unknown rank-one signal with uncalibrated receivers

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.

Analysis of SAR Moving Grid Processing for Focusing and Detection of Ground Moving Targets

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.

Detection in passive MIMO radar networks

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.

On the applicability of source localization techniques to passive multistatic radar

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.

A unified detection framework for distributed active and passive RF sensing

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.

Direct Cartesian localization in passive MIMO radar networks

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.

Adaptive pulse design for space-time adaptive processing

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.