The radio-frequency (RF) electromagnetic spectrum, extending from below 1 MHz to above 100 GHz, represents a precious resource. It is used for a wide range of purposes, including communications, radio and television broadcasting, radionavigation, and sensing. Radar represents a fundamentally important use of the electromagnetic (EM) spectrum, in applications which include air traffic control, geophysical monitoring of Earth resources from space, automotive safety, severe weather tracking, and surveillance for defense and security. Nearly all services have a need for greater bandwidth, which means that there will be ever-greater competition for this finite resource. The paper explains the nature of the spectrum congestion problem from a radar perspective, and describes a number of possible approaches to its solution both from technical and regulatory points of view. These include improved transmitter spectral purity, passive radar, and intelligent, cognitive approaches that dynamically optimize spectrum use.

/pdf/radar-spectrum-engineering-and-management-technical-and-1akd12nvp3.pdf

Radar Spectrum Engineering and Management: Technical and Regulatory Issues

We address covariance estimation in the sense of minimum mean-squared error (MMSE) when the samples are Gaussian distributed. Specifically, we consider shrinkage methods which are suitable for high dimensional problems with a small number of samples (large p small n). First, we improve on the Ledoit-Wolf (LW) method by conditioning on a sufficient statistic. By the Rao-Blackwell theorem, this yields a new estimator called RBLW, whose mean-squared error dominates that of LW for Gaussian variables. Second, to further reduce the estimation error, we propose an iterative approach which approximates the clairvoyant shrinkage estimator. Convergence of this iterative method is established and a closed form expression for the limit is determined, which is referred to as the oracle approximating shrinkage (OAS) estimator. Both RBLW and OAS estimators have simple expressions and are easily implemented. Although the two methods are developed from different perspectives, their structure is identical up to specified constants. The RBLW estimator provably dominates the LW method for Gaussian samples. Numerical simulations demonstrate that the OAS approach can perform even better than RBLW, especially when n is much less than p . We also demonstrate the performance of these techniques in the context of adaptive beamforming.

Shrinkage Algorithms for MMSE Covariance Estimation

Part I Introduction to LPI radar and waveform design: to see and not be seen LPI technology and applications ambiguity analysis of LPI waveforms FMCW radar phase shift keying techniques frequency shift keying techniques case study - anti-ship LPI missile seeker. Part II Intercept receiver strategies and signal processing: strategies for LPI radar interception Wigner distribution analysis of LPI waveforms LPI radar analysis using quadrature mirror filtering cyclostationary bi-frequency analysis of LPI radar waveforms. Appendix: MATLAB software.

Detecting and Classifying Low Probability of Intercept Radar

2017 25th European Signal Processing Conference (EUSIPCO)

Several problems arising in electromagnetics can be directly formulated or suitably recast for an effective solution within the compressive sensing (CS) framework. This has motivated a great interest in developing and applying CS methodologies to several conventional and innovative electromagnetic scenarios. This work is aimed at presenting, to the best of the authors knowledge, a review of the state-of-the-art and most recent advances of CS formulations and related methods as applied to electromagnetics. Toward this end, a wide set of applicative scenarios comprising the diagnosis and synthesis of antenna arrays, the estimation of directions of arrival, and the solution of inverse scattering and radar imaging problems are reviewed. Current challenges and trends in the application of CS to the solution of traditional and new electromagnetic problems are also discussed.

Compressive Sensing in Electromagnetics - A Review

The application of multi-input multi-output (MIMO) radar concepts to HF over-the-horizon radar is considered to improve radar timeline management flexibility and to permit adaptivity on transmit. MIMO radar concepts in the literature are inconsistent and in this paper the taxonomy of MIMO radar is clarified and distinctions between different MIMO radar types drawn. The term "spatially waveform diverse radar" is introduced, and performance equivalences between element-space and beamspace orthogonality discussed. Finally, we outline significant constraints on the design of waveform sets for the case of transmit arrays where (at least for some part of the operational regime) the array inter-element spacing is less than one half wavelength.

Spatially Waveform Diverse Radar: Perspectives for High Frequency OTHR

Direction-of-arrival estimation performance of MUSIC and maximum-likelihood estimation in the so-called ldquothresholdrdquo area is analyzed by means of general statistical analysis (GSA) (also known as random matrix theory). Both analytic predictions and direct Monte Carlo simulations demonstrate that the well-known MUSIC-specific ldquoperformance breakdownrdquo is associated with the loss of resolution capability in the MUSIC pseudo-spectrum, while the sample signal subspace is still reliably separated from the actual noise subspace. Significant distinctions between (MUSIC/G-MUSIC)-specific and MLE-intrinsic causes of ldquoperformance breakdown,rdquo as well as the role of ldquosubspace swaprdquo phenomena, are specified analytically and supported by simulation.

MUSIC, G-MUSIC, and Maximum-Likelihood Performance Breakdown

For sensors where the number of available independent identically distributed training samples T is less than the number of antenna array elements M, we propose nondegenerate properly normalized likelihood ratio (LR) tests (both standard and scale-invariant) to support detection-estimation of m point sources (m < T) in white noise, based on a generalized likelihood-ratio test (GLRT) approach. We demonstrate that these tests can detect MUSIC-specific ldquooutliersrdquo in the direction-of-arrival (DOA) estimation of closely spaced independent sources caused by insufficient training volume and/or signal-to-noise ratio (SNR). We then compare the performance of the introduced LRs to other test statistics available in this undersampled regime. We show that a search for solutions that increase the introduced LR allows us to replace the detected outliers by proper DOA estimates. This ldquopredict and curerdquo process leverages the SNR ldquogaprdquo between MUSIC breakdown and breakdown of maximum-likelihood estimation itself. The resultant LR maximization makes the associated covariance model statistically ldquoas likelyrdquo as the true covariance matrix and removes the vast percentage of outliers in certain scenarios.

GLRT-Based Detection-Estimation for Undersampled Training Conditions

In over-the-horizon radar (OTHR) the need to preferentially select propagation mode arises when one or more modes are perturbed by ionospheric disturbances. Due to mixed-mode propagation and range-elevation coupling, such control is only implementable using noncausal beamforming via MIMO radar architectures. We introduce three key principles that govern mode-selective multiple-input multiple-output (MIMO) OTHR design. Numerical examples illustrate the high potential efficiency of mode-selective MIMO OTHR, while field trials support the introduced main principles.

Principles of Mode-Selective MIMO OTHR

We introduce an iterative procedure for design of adaptive KL-variate linear beamformers that are structured as the Kronecker product of K-variate (transmit) and L-variate (receive) beamformers. We focus on MIMO radar applications for scenarios where only joint transmit and receive adaptive beamforming can efficiently mitigate multi-mode propagated backscatter interference. This is because the direction-of-departure (DoD) on one interference mode, and the direction-of-arrival (DoA) on the other, coincide with those of a target, respectively. We introduce a Markov model for the adaptive iterative routine, specify its convergence condition, and derive final (stable) signal-to-interference-plus-noise ratio (SINR) performance characteristics. Simulation results demonstrate high accuracy of the analytical derivations. In addition, we demonstrate, that for the considered class of multiple-input multiple-output (MIMO) radar interference scenarios, the diagonally loaded sample matrix inversion (SMI) algorithm provides additional performance improvement and convergence rate for this iterative adaptive Kronecker beamformer.

Ben A. Johnson

Papers

Spatially Waveform Diverse Radar: Perspectives for High Frequency OTHR

MUSIC, G-MUSIC, and Maximum-Likelihood Performance Breakdown

GLRT-Based Detection-Estimation for Undersampled Training Conditions

Principles of Mode-Selective MIMO OTHR

Iterative Adaptive Kronecker MIMO Radar Beamformer: Description and Convergence Analysis