Eran Fishler

MIMO radar: an idea whose time has come

It has recently been shown that multiple-input multiple-output (MIMO) antenna systems have the potential to improve dramatically the performance of communication systems over single antenna systems. Unlike beamforming, which presumes a high correlation between signals either transmitted or received by an array, the MIMO concept exploits the independence between signals at the array elements. In conventional radar, target scintillations are regarded as a nuisance parameter that degrades radar performance. The novelty of MIMO radar is that it takes the opposite view; namely, it capitalizes on target scintillations to improve the radars performance. We introduce the MIMO concept for radar. The MIMO radar system under consideration consists of a transmit array with widely-spaced elements such that each views a different aspect of the target. The array at the receiver is a conventional array used for direction finding (DF). The system performance analysis is carried out in terms of the Cramer-Rao bound of the mean-square error in estimating the target direction. It is shown that MIMO radar leads to significant performance improvement in DF accuracy.

Evaluation of Transmit Diversity in MIMO-Radar Direction Finding

It has been recently shown that multiple-input multiple-output (MIMO) antenna systems have the potential to dramatically improve the performance of communication systems over single antenna systems. Unlike beamforming, which presumes a high correlation between signals either transmitted or received by an array, the MIMO concept exploits the independence between signals at the array elements. In conventional radar, the targets radar cross section (RCS) fluctuations are regarded as a nuisance parameter that degrades radar performance. The novelty of MIMO radar is that it provides measures to overcome those degradations or even utilizes the RCS fluctuations for new applications. This paper explores how transmit diversity can improve the direction finding performance of a radar utilizing an antenna array at the receiver. To harness diversity, the transmit antennas have to be widely separated, while for direction finding, the receive antennas have to be closely spaced. The analysis is carried out by evaluating several Cramer-Rao bounds for bearing estimation and the mean square error of the maximum likelihood estimate

Performance of MIMO radar systems: advantages of angular diversity

Inspired by recent advances in multiple-input multiple-output (MIMO) communications, this paper introduces the statistical MIMO radar concept. The fundamental difference between statistical MIMO and other radar array systems is that the latter seek to maximize the coherent processing gain, while statistical MIMO radar capitalizes on the diversity of target scattering to improve radar performance. Coherent processing is made possible by highly correlated signals at the receiver array, whereas in statistical MIMO radar, the signals received by the array elements are uncorrelated. It is well known that in conventional radar, slow fluctuations of the target radar cross-section (RCS) result in target fades that degrade radar performance. By spacing the antenna elements at the transmitter and at the receiver such that the target angular spread is manifested, the MIMO radar can exploit the spatial diversity of target scatterers opening the way to a variety of new techniques that can improve radar performance. In this paper, we focus on the application of the target spatial diversity to improve detection performance. The optimal detector in the Neyman-Pearson sense is developed and analyzed for the statistical MIMO radar. An optimal detector invariant to the signal and noise levels is also developed and analyzed. In this case as well, statistical MIMO radar provides great improvements over other types of array radars.

Detection of signals by information theoretic criteria: general asymptotic performance analysis

Detecting the number of sources is a well-known and a well-investigated problem. In this problem, the number of sources impinging on an array of sensors is to be estimated. The common approach for solving this problem is to use an information theoretic criterion like the minimum description length (MDL), or the Akaike information criterion (AIC). Although it has been gaining much popularity and has been used in a variety of problems, the performance of information theoretic criteria-based estimators for the unknown number of sources has not been sufficiently studied, yet. In the context of array processing, the performance of such estimators were analyzed only for the special case of Gaussian sources where no prior knowledge of the array structure, if given, is used. Based on the theory of misspecified models, this paper presents a general asymptotic analysis of the performance of any information theoretic criterion-based estimator, and especially of the MDL estimator. In particular, the performance of the MDL estimator, which assumes Gaussian sources and structured array when applied to Gaussian sources, is analyzed. In addition, it is shown that the performance of a certain MDL estimator is not very sensitive to the actual distribution of the source signals. However, appropriate use of prior knowledge about the array geometry can lead to significant improvement in the performance of the MDL estimator. Simulation results show a good fit between the empirical and the theoretical results.

Narrowband interference suppression in time-hopping impulse-radio systems

Ultra-wideband (UWB) radio systems have drawn a lot of attention during the last few years. These systems use very low transmission power, spread over a bandwidth of several gigahertz. The very low transmission power and the large bandwidth used enable UWB radio systems to coexist with other narrowband systems over the same frequency band without interfering with the narrowband systems. Nevertheless, these narrowband systems may cause interference which jams the UWB receiver completely. Since standard narrowband interference suppression techniques are not applicable, techniques for interference suppression have to be developed. This paper presents novel narrowband interference suppression algorithms for UWB radio systems. Theoretical analysis of these algorithms reveal that they can eliminate the narrowband interference almost completely.

On the use of order statistics for improved detection of signals by the MDL criterion

The minimum description length (MDL) criterion for model selection has been successfully applied to the problem of estimating the number of sources in noise. We show that by employing an order statistics approach in the existing solution, a new processor with uniformly improved performance is constructed at the cost of greater complexity. We prove that the resulting MDL-based processor is consistent. Results of simulations of practical scenarios of interest demonstrate how significant the performance improvement can be.

Order statistics approach for determining the number of sources using an array of sensors

A new approach for estimating the number of radiating, not fully correlated sources using the data received by an array of sensors is presented. The common approach is to apply information theoretic criteria, such as the minimum description length (MDL) or the Akaike information criterion (AIC), on the received data. Alternatively, we suggest to apply these criteria on the ordered eigenvalues of the sample data covariance matrix. While asymptotically, as the number of snapshots tends to infinity, the two approaches converge, we demonstrate that for any finite number of samples there exist physical conditions for which the proposed approach outperforms the traditional one. These cases are associated with spatially close sources, or with highly correlated sources, or with the case of sources with very different signal-to-noise ratio (SNR).

Narrowband interference mitigation in impulse radio

Impulse radio (IR) systems have drawn attention during the last few years. These systems are planned to coexist with narrowband systems without interfering them. Nevertheless, the narrowband systems can cause interference which may jam the IR receiver. This letter analyzes a low-complexity narrowband interference (NBI)-mitigation algorithm for IR systems, based on minimal mean-square error combining. Theoretical analysis reveals that these algorithms nearly eliminate the NBI. The concept is also extended to the case where the receiver has more correlators than channel taps.

Detection and parameter estimation of a transient signal using order statistics

Detection and parameter estimation of a transient signal in noise is a problem of many applications. It is characterized by the fact that some of the measurements consist of noise only. Modern statistical signal processing techniques are applied on a discrete version of the received data and are implemented by digital signal processing (DSP). In this correspondence, we show how order statistics (OS)-based signal processing, which is of a discrete nature, can be used for simultaneous detection and estimation of parameters (such as time of arrival and signal duration) of a sampled transient signal in white noise. We show that the resulting processors are more robust than the conventional processors, whereas their performance is about the same, at the cost of increased computational complexity.

General asymptotic analysis of the generalized likelihood ratio test for a Gaussian point source under statistical or spatial mismodeling

This paper investigates the robustness of the generalized likelihood ratio test (GLRT) for a far-field Gaussian point source. Given measurements from an array of sensors, the performance of the GLRT under two types of common modeling errors is investigated. The first type is spatial mismodeling, which relates to errors due to multipath effects or errors in the assumed number of sources, i.e., deviation from the single point source assumption. The second type is statistical mismodeling, which relates to errors due to non-Gaussianity in either the noise or the signal, i.e., deviation from the Gaussian assumption. It is shown that for some types of modeling errors, the detectors performance improves, and general conditions for such an improvement are found. Moreover, for both types of errors, the change in performance is analyzed and quantified. This analysis shows that for a distributed source with small spatial spreading, the degradation in performance is significant, whereas for a constant modulus point source, the performance improves. Simulations of various cases are shown to verify the analytical results.