Daniel J. Rabideau

Ubiquitous MIMO multifunction digital array radar

Radar equipment specifications are often driven by the need to detect small targets in clutter. Relevant specifications include dynamic range, phase noise, system stability, isolation and spurs. Furthermore, the desire for low probability of intercept radar operation also influences the radar hardware design. This paper describes how digital array radars can be used to manage radar time and energy, thereby simplifying radar equipment design. Digital arrays enable both highly focused transmit beams (e.g., for track) and broad transmit illumination (e.g., for search). Regarding the latter, multi-input multi-output (MIMO) techniques, which allow wide angular coverage, are described.

Fast, rank adaptive subspace tracking and applications

High computational complexity and inadequate parallelism have deterred the use of subspace-based algorithms in real-time systems. We proposed a new class of fast subspace tracking (FST) algorithms that overcome these problems by exploiting the matrix structure inherent in multisensor processing. These algorithms simultaneously track an orthonormal basis for the signal subspace and preserve signal eigenstructure information while requiring only O(Nr) operations per update (where N is the number of channels, and r is the effective rank). Because of their low computational complexity, these algorithms have applications in both recursive and block data processing. Because they preserve the signal eigenstructure as well as compute an orthonormal basis for the signal subspace, these algorithms may be used in a wide range of sensor array applications including bearing estimation, beamforming, and recursive least squares. We present a detailed description of the FST algorithm and its rank adaptive variation (RA-FST) as well as a number of enhancements. We also demonstrate the FSTs rapid convergence properties in a number of application scenarios.

Clutter and jammer multipath cancellation in airborne adaptive radar

Airborne surveillance radars must detect and localize targets in diverse interference environments consisting of ground clutter, conventional jamming, and terrain scattered jammer multipath. Multidimensional adaptive filtering techniques have been proposed to adaptively cancel this interference. However, a detailed analysis that includes the effects of multipath nonstationarity has been elusive. This work addresses the nonstationary nature of the jammer multipath and its impact on clutter cancellation and target localization. It is shown that the weight updating needed to track this interference will also modulate sidelobe signals. At the very least, this complicates the localization of targets. At the worst, it also greatly complicates the rejection of clutter. Several techniques for improving cancellation of jammer multipath and clutter are proposed, including 1) weight vector interpolation, extrapolation, and updating; 2) filter architecture, constraint, and beamspace selection; 3) prefilters; 4) 3-D STAP architectures; and 5) multidimensional sidelobe target editing.

Mitigation of digital array nonlinearities

In a digital array, each receiver performs analog-to-digital-conversion (ADC), with the resulting digital data later combined via digital beamforming techniques. Since ADC is performed prior to beamforming, it is thus possible to enhance the dynamic range of each digital receiver through post-ADC array integration gain. Unfortunately, receiver correlation nonlinearities, such as spurious signals (spurs) or intermodulation distortion (intermods), across receivers can severely limit the achievable dynamic range enhancement. This paper proposes a methodology for mitigating the impact of receiver nonlinearities. In our approach the receiver input signals are carefully modified in a way that varies from channel to channel. The resulting signals are then processed by each digital receiver, introducing nonlinearities. Finally, the digital signals are corrected to restore the desired linear signal components. Examples are given of how this methodology can be applied to ADC, direct digital synthesizers (DDS), digital-to-analog converters (DAC), amplifiers, and mixers. In some cases, distortion is effectively reduced by a factor of N to N/sup 2/, where N is the number of receivers.

Adaptive MIMO radar waveforms

Multiple-Input, Multiple-Output (MIMO) radars enhance performance by transmitting and receiving coded waveforms from multiple locations. To date, the theoretical literature on MIMO radar has focused largely on the use of ldquoorthogonal waveforms.rdquo Practical approaches to approximate orthogonality (e.g., via waveforms characterized by low cross-correlation and low autocorrelation sidelobe levels) have also started to emerge. We show, however, that such waveforms can still perform poorly in adaptive systems. For adaptive MIMO radar systems, another factor is equally important; we call it ldquoMIMO Cancellation Ratio (CR).rdquo In this paper, we introduce MIMO CR and assess the performance of standard waveform classes (e.g., CDMA, TDMA and FDMA) with regard to this new metric. Then, we describe how to create new waveforms that facilitate higher performance adaptive cancellation. Finally, we apply our waveform design methodology to the problem of airborne MTI radar, in which STAP techniques are used to mitigate clutter.

Clutter mitigation techniques for space-based radar

The mission of a ground moving target indication (GMTI) radar, as its name implies, is to detect and classify ground-based vehicles, even ones with very low velocities. This type of radar can provide a wide area of coverage and frequent updates of a specific area of interest if the radar is placed on a satellite with a low Earth orbit. However because of the large footprint of the radar on the ground and the high satellite velocity target signals must compete with very strong, nearby clutter. This paper describes how space-time adaptive processing (STAP) can be used for the purposes of clutter rejection in order to perform the GMTI function. In addition, we confront several important issues for a space-based radar such as pulse repetition frequency (PRF) selection, the choice of a STAP algorithm, and the number of spatial channels. These results are quantified in terms of clutter cancellation and angle accuracy.

A signal processing architecture for space-based GMTI radar

Ground moving target indicator (GMTI) radars detect and classify targets with low velocities. Placing such radars in the Earths orbit can provide wide area coverage with high revisit rates. However, because of the radars large footprint (on the ground) and high velocity, target signals must compete with extremely intense nearby clutter. Requirements on antenna aperture, bandwidth, coverage rate, and computational complexity all play significant roles in shaping the radars signal processing chain. This paper describes a signal processing architecture that rejects interference. By addressing issues such as aperture configuration, bandwidth-induced decorrelation, adaptive training, and degree-of-freedom requirements, a multistage space-time adaptive processing (STAP) architecture is constructed.

Hybrid mitigation of distortion in digital arrays

Radars and other RF systems are often used to detect weak signals in the presence of strong interference. Consequently, these systems must be designed to accommodate both (1) high SNR levels, and (2) high instantaneous dynamic range (IDR) levels. Recently, digital beam forming (DBF) techniques have been proposed as a means of improving both SNR and IDR. In a DBF system, high SNR levels can be produced by coherently combining many lower IDR transmit/receive (T/R) channels. However, this does not necessarily result in an equally improved IDR because distortion, which is introduced by the RF electronics within each T/R channel, could also be coherently integrated by the beamformer. The resulting integrated distortion could exceed the noise level at the beamformers output, thereby limiting IDR improvement. This paper describes an approach to increasing the IDR levels achieved by digital arrays. The approach combines traditional DBF techniques with new IDR enhancement measures. Dynamic range enhancement is achieved through the use of two complementary processes: (1) decorrelation of spurs, phase noise, and various intermodulation products, (2) linearization of selected intermodulation products. This approach provides an efficient means for reducing the broad range of distortion products that typically limit IDR.

Mitigation of correlated non-linearities in digital phased arrays using channel-dependent phase shifts

In an active phased array, each Transmitter/Receiver Module (TRM) performs a set of approximately linear functions (e.g., amplification, mixing, etc.) with the resulting signals later combined via beamforming techniques. Since these nearly-linear functions are performed prior to beamforming, it is theoretically possible to improve upon the dynamic range (DR) of each TRM through post-module array integration gain. It has been demonstrated, however, that DR enhancement may be limited by correlated nonlinear distortion (i.e., correlated from module to module). A general technique that ensures nonlinearities do not add constructively from module to module has been proposed recently, and verified experimentally for a special case. Another special case of the general technique has been described analytically, but with no experimental verification. In this paper, we correct a flaw in this analysis, and extend it. Measurements on a thirteen channel digital phased array demonstrate that introducing random phase shifts into an array can substantially mitigate nonlinear distortion, thus improving DR over the array.

Doppler-offset waveforms for MIMO radar

In a Multiple-Input, Multiple-Output (MIMO) radar, independent waveforms are transmitted from different locations, with the resulting reflections processed to form a “virtual antenna array” that is larger than the physical aperture of the radar. This paper examines the design of Doppler-offset waveforms for use in adaptive MIMO GMTI radar systems. Such waveforms provide good adaptive cancellation performance, but are also subject to strong range and Doppler ambiguities. We analyze these ambiguities, and show how they relate to array topology and waveform design. Then, we describe a new waveform approach, called “Dithered DDMA,” which enables high performance clutter cancellation over large range-Doppler regions without introducing ambiguous ranges or blind speeds, and without increasing the computational load on the MIMO processor.