Keith W. Forsythe

Multiple-input multiple-output (MIMO) radar and imaging: degrees of freedom and resolution

In this paper, radar is discussed in the context of a multiple-input multiple-output (MIMO) system model. A comparison is made between MIMO wireless communication and MIMO radar. Examples are given showing that many traditional radar approaches can be interpreted within a MIMO context. Furthermore, exploiting this MIMO perspective, useful extensions to traditional radar can be constructed. Performance advantages in terms of degrees of freedom and resolution are discussed. Finally, a MlMO extension to space-time adaptive processing (STAP) is introduced as applied to ground moving-target indication (GMTI).

Range Compression and Waveform Optimization for MIMO Radar: A CramÉr–Rao Bound Based Study

A multi-input multi-output (MIMO) radar system, unlike standard phased-array radar, can transmit via its antennas multiple probing signals that may be correlated or uncorrelated with each other. This waveform diversity offered by MIMO radar enables superior capabilities compared with a standard phased-array radar. One of the common practices in radar has been range compression. We first address the question of ldquoto compress or not to compressrdquo by considering both the Cramer-Rao bound (CRB) and the sufficient statistic for parameter estimation. Next, we consider MIMO radar waveform optimization for parameter estimation for the general case of multiple targets in the presence of spatially colored interference and noise. We optimize the probing signal vector of a MIMO radar system by considering several design criteria, including minimizing the trace, determinant, and the largest eigenvalue of the CRB matrix. We also consider waveform optimization by minimizing the CRB of one of the target angles only or one of the target amplitudes only. Numerical examples are provided to demonstrate the effectiveness of the approaches we consider herein.

Multiple-input multiple-output (MIMO) radar: performance issues

The application of multiple-input multiple-output (MIMO) techniques to multistage radar offers a number of advantages, including improved resolution and sensitivity. Depending upon the radars mode of operation, the array design and the environment, these advantages may or may not be significant. In this paper, a simple analytic model for ground moving target indicator (GMTI) radar detection is presented and its predictions are compared with simulation. Conventional single-input multiple output (SIMO) and MIMO GMTI radars are compared in terms of minimum detectable velocity, area-search rates and array sidelobes. Optimal waveform cross-correlation matrices are identified for MIMO radars, given different performance criteria. Finally, bounds are developed for MIMO arrays with uniformly sampled virtual apertures.

Environmental issues for MIMO capacity

Wireless communication using multiple-input multiple-output (MIMO) systems enables increased spectral efficiency for a given total transmit power. Increased capacity is achieved by introducing additional spatial channels that are exploited using space-time coding. In this paper, the environmental factors that affect MIMO capacity are surveyed. These factors include channel complexity, external interference, and channel estimation error. The maximum spectral efficiency of MIMO systems in which both transmitter and receiver know the channel (using channel estimate feedback) is compared with MIMO systems in which only the receiver knows the channel. Channel complexity is studied using both simple stochastic physical scattering and asymptotic large random matrix models. Both uncooperative (worst-case) and cooperative (amenable to multiuser detection) interference are considered. An analysis for capacity loss associated with channel estimation error at the transmitter is introduced.

Waveform Correlation and Optimization Issues for MIMO Radar

In this paper, waveform optimization for multiple- input multiple-output (MIMO) radar systems is considered. Two types of waveform optimization are studied for static radar envi- ronments. Firstly, image-energy optimization using environmen- tally adaptive waveform design is considered. Secondly, waveform optimization for angle estimation in clutter-free environments is treated using Cramer-Rao bounds.

GMTI MIMO radar

Multiple-input multiple-output (MIMO) extensions to radar systems enable a number of advantages compared to traditional approaches. These advantages include improved angle estimation and target detection. In this paper, MIMO ground moving target indication (GMTI) radar is addressed. The concept of coherent MIMO radar is introduced. Comparisons are presented comparing MIMO GMTI and traditional radar performance. Simulations and theoretical bounds for MIMO GMTI angle estimation and minimum detectable velocity are presented. The simulations are evaluated in the time domain, enabling waveform design studies. For some applications, these results indicate significant potential improvements in clutter-mitigation SINR loss and reduction in angle-estimation error for slow-moving targets.

Ultra-wideband (UWB) transmitter location using time difference of arrival (TDOA) techniques

Ultra-wideband (UWB) signals lend themselves to localization measurements using time difference of arrival (TDOA), or time delay estimation (TDE), techniques. However, TDOA estimation is complicated by pulse distortion due to the antenna elements, other system components, and the multipath propagation environment. This paper explores the use of cross correlation based TDOA methods in conjunction with a novel technique for combining the TDOA estimates from multiple antenna pairs to build an estimate of the transmitter position. Experiments that simulate foliage and industrial environments were performed in an anechoic chamber. Results from these measurements are presented.

MIMO Radar Waveform Constraints for GMTI

Ground moving-target indication (GMTI) provides both an opportunity and challenge for coherent multiple-input multiple-output (MIMO) radar. MIMO techniques can improve a radars angle estimation and the minimum detectable velocity (MDV) for a target. However, the challenge of clutter mitigation places significant constraints on MIMO radar waveforms. In this paper, the loss of target return because of clutter mitigation (signal-to-noise ratio (SNR) loss) is the driving performance metric. The ideal, orthogonal repeated-pulse waveform is shown not to exist. Pulse-to-pulse time-varying waveforms, such as Doppler-division multiple access (DDMA), are shown to offer SNR loss performance approaching ideal MIMO systems.

MIMO environmental capacity sensitivity

Wireless communications using multiple input multiple output (MIMO) systems enable increased spectral efficiency for a given total transmit power. The increased capacity is achieved through the introduction of additional spatial channels (space-time coding). In this paper, MIMO capacity is calculated as a function of environmental factors, including channel complexity, external interference and channel estimation error. The capacity of MIMO systems, where both the transmitter and receiver know the channel (channel estimate feedback), is compared with single input multiple output (SIMO) and MIMO systems, where only the receiver knows the channel. The channel complexity is studied using a simple statistical physical scattering model. Finally, an expression for capacity loss particular to channel estimation error at the transmitter is introduced.

A class of polynomial rooting algorithms for joint azimuth/elevation estimation using multidimensional arrays

Polynomial rooting techniques for efficient high resolution estimation of direction parameters from linear arrays are well documented in the literature. These techniques are limited, however, to cases of estimating a scalar direction parameter (say, either azimuth or elevation). The paper introduces a methodology for extending the polynomial rooting philosophy to the case of multidimensional arrays, which will be used to estimate jointly both azimuth and elevation parameters of the signal directions. It is shown via simulation that the resolution capabilities of the polynomial root intersection for multidimensional estimation (PRIME) class of algorithms is superior to the spectral algorithms they supplant, and that the variance of the direction estimates is equal to that of the corresponding spectral algorithms. It is shown analytically that the mean squared error of the PRIME estimates can be asymptotically equal to that of spectral MUSIC estimates. Finally, some extensions are discussed.<<ETX>>