Michael C. Wicks

Frequency diverse array radars

This paper presents a generalized structure for a frequency diverse array radar. In its simplest form, the frequency diverse array applies a linear phase progression across the aperture. This linear phase progression induces an electronic beam scan, as in a conventional phased array. When an additional linear frequency shift is applied across the elements, a new term is generated which results in a scan angle that varies with range in the far-field. This provides more flexible beam scan options, as well as providing resistance to point interference such as multipath. More general implementations provide greater degrees of freedom for space-time-frequency-phase-polarization control, permitting novel concepts for simultaneous multi-mission operation, such as performing synthetic aperture radar and ground moving target indication at the same time.

Knowledge-Aided (Potentially Cognitive) Transmit Signal and Receive Filter Design in Signal-Dependent Clutter

We consider the problem of knowledge-aided (possibly cognitive) transmit signal and receive filter design for point-like targets in signal-dependent clutter. We suppose that the radar system has access to a (potentially dynamic) database containing a geographical information system (GIS), which characterizes the terrain to be illuminated, and some a priori electromagnetic reflectivity and spectral clutter models, which allow the raw prediction of the actual scattering environment. Hence, we devise an optimization procedure for the transmit signal and the receive filter which sequentially improves the signal- to-interference-plus-noise ratio (SINR). Each iteration of the algorithm, whose convergence is analytically proved, requires the solution of both a convex and a hidden convex optimization problem. The resulting computational complexity is linear with the number of iterations and polynomial with the receive filter length. At the analysis stage we assess the performance of the proposed technique in the presence of either a homogeneous ground clutter scenario or a heterogeneous mixed land and sea clutter environment.

Space-time adaptive processing: a knowledge-based perspective for airborne radar

This article provides a brief review of radar space-time adaptive processing (STAP) from its inception to state-of-the art developments. The topic is treated from both intuitive and theoretical aspects. A key requirement of STAP is knowledge of the spectral characteristics underlying the interference scenario of interest. Additional issues of importance in STAP include the computational cost of the adaptive algorithm as well as the ability to maintain a constant false alarm rate (CFAR) over widely varying interference statistics. This article addresses these topics, developing the need for a knowledge-based (KB) perspective. The focus here is on signal processing for radar systems using multiple antenna elements that coherently process multiple pulses. An adaptive array of spatially distributed sensors, which processes multiple temporal snapshots, overcomes the directivity and resolution limitations of a single sensor.

Radio Frequency Tomography for Tunnel Detection

Radio frequency (RF) tomography is proposed to detect underground voids, such as tunnels or caches, over relatively wide areas of regard. The RF tomography approach requires a set of low-cost transmitters and receivers arbitrarily deployed on the surface of the ground or slightly buried. Using the principles of inverse scattering and diffraction tomography, a simplified theory for below-ground imaging is developed. In this paper, the principles and motivations in support of RF tomography are introduced. Furthermore, several inversion schemes based on arbitrarily deployed sensors are devised. Then, limitations to performance and system considerations are discussed. Finally, the effectiveness of RF tomography is demonstrated by presenting images reconstructed via the processing of synthetic data.

A deterministic least-squares approach to space-time adaptive processing (STAP)

A direct data domain (D/sup 3/) least-squares space-time adaptive processing (STAP) approach is presented for adaptively enhancing signals in a nonhomogeneous environment. The nonhomogeneous environment may consist of nonstationary clutter and could include blinking jammers. The D/sup 3/ approach is applied to data collected by an antenna array utilizing space and in time (Doppler) diversity. Conventional STAP generally utilizes statistical methodologies based on estimating a covariance matrix of the interference using data from secondary range cells. As the results are derived from ensemble averages, one filter (optimum in a probabilistic sense) is obtained for the operational environment, assumed to be wide sense stationary. However for highly transient and inhomogeneous environments the conventional statistical methodology is difficult to apply. Hence, the D/sup 3/ method is presented as it analyzes the data in space and time over each range cell separately. The D/sup 3/ method is deterministic in approach. From an operational standpoint, an optimum method could be a combination of these two diverse methodologies. This paper represents several new D/sup 3/ approaches. One is based on the computation of a generalized eigenvalue for the signal strength and the others are based on the solution of a set of block Hankel matrix equations. Since the matrix of the system of equations to be solved has a block Hankel structure, the conjugate gradient method and the fast Fourier transform (FFT) can be utilized for efficient solution of the adaptive problem. Illustrative examples presented in this paper use measured data from the multichannel airborne radar measurements (MCARM) database to detect a Sabreliner in the presence of urban, land, and sea clutter. An added advantage for the D/sup 3/ method in solving real-life problems is that simultaneously many realizations can be obtained for the same solution for the signal of interest (SOI). The degree of variability amongst the different results can provide a confidence level of the processed results. The D/sup 3/ method may also be used for mobile communications.

Improving practical space-time adaptive radar

This paper discusses the incorporation of nonhomogeneity detection with space-time adaptive processing to improve the formation of adaptive weights in practical adaptive airborne radar. We examine the problem of improving interference covariance matrix estimation in real-world environments and discuss several approaches for integrating nonhomogeneity detection with space-time adaptive processing. We use measured airborne data from the Rome Laboratory Multichannel Airborne Radar Measurements Program to illustrate key points.

Radar Spectrum Engineering and Management: Technical and Regulatory Issues

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.

Knowledge-based radar signal and data processing: a tutorial review

Radar systems are an important component in military operations. In response to increasingly severe threats from military targets with reduced radar cross sections (RCSs), slow-moving and low-flying aircraft hidden in foliage, and in environments with large numbers of targets, knowledge-based (KB) signal and data processing techniques offer the promise of significantly improved performance of all radar systems. Radars under KB control can be deployed to utilize valuable resources such as airspace or runways more effectively and to aid human operators in carrying out their missions. As battlefield scenarios become more complex with increasing numbers of sensors and weapon systems, the challenge will be to use already available information effectively to enhance radar performance, including positioning, waveform selection, and modes of operation. KB processing fills this need and helps meet the challenge.

Multi-mission multi-mode waveform diversity

Previously, the authors presented a novel method for achieving range-dependent beamforming through the use of a frequency diverse array. The frequency diverse array concept was developed using a quasi-stationary waveform assumption. This concept can be extended to non-continuous wave signals for narrowband and wideband applications such as moving target indication (MTI) and synthetic aperture radar (SAR). Alternative implementations are described, which offer the potential for achieving MTI and SAR simultaneously.

Assessment of multichannel airborne radar measurements for analysis and design of space-time processing architectures and algorithms

System design studies and detailed radar simulations have identified the utility of space-time adaptive processing (STAP) to accomplish target detection in cases where the target Doppler is immersed in sidelobe clutter and jamming. A recent US Air Force investment in STAP has produced a database of multichannel airborne data, through Rome Laboratorys Multichannel Airborne Radar Measurement (MCARM) program, to further develop STAP architectures and algorithms suited to operational environments. An aspect of actual data not typically incorporated into simulation scenarios is the nonhomogeneous features of real-world clutter and interference scenarios. In this paper we investigate the impact of nonhomogeneous data on the performance of STAP. Furthermore, we propose a preliminary scheme to detect and excise nonhomogeneous secondary data in the sample covariance estimation, thereby dramatically improving STAP performance as shown through a specific example using monostatic MCARM data.