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Since the seminal paper by Marzetta from 2010, the Massive MIMO paradigm in communication systems has changed from being a theoretical scaled-up version of MIMO, with an infinite number of antennas, to a practical technology. Its key concepts have been adopted in the 5G new radio standard and base stations, where $64$ fully-digital transceivers have been commercially deployed. Motivated by these recent developments, this paper considers a co-located MIMO radar with $M_T$ transmitting and $M_R$ receiving antennas and explores the potential benefits of having a large number of virtual spatial antenna channels $N=M_TM_R$. Particularly, we focus on the target detection problem and develop a \textit{robust} Wald-type test that guarantees certain detection performance, regardless of the unknown statistical characterization of the clutter disturbance. Closed-form expressions for the probabilities of false alarm and detection are derived for the asymptotic regime $N\to \infty$. Numerical results are used to validate the asymptotic analysis in the finite system regime with different disturbance models. Our results imply that there always exists a sufficient number of antennas for which the performance requirements are satisfied, without any a-priori knowledge of the clutter statistics. This is referred to as the Massive MIMO regime of the radar system., Comment: 12 pages, 6 figures, accepted for publication in IEEE Transactions on Signal Processing. A related work has been presented at ICASSP19, Brighton, UK, and is available at arXiv:1904.04184

Multipath signals arise in many active sensing modalities, such as radar and sonar. Moving targets cause Doppler effects that could vary for different paths. Target Doppler information corresponding to direct and non-direct paths is important for moving target localizations and classifications, particularly when narrowband signals are involved. This information, however, becomes difficult to reveal when dealing with nonlinear time-varying multi-component Doppler signals. In this paper, we introduce a new time-frequency analysis technique based on the local phase information to accurately extract complex Doppler signature of each signal arrival. This is achieved through short-time polynomial phase modeling of data segments. The global behavior is obtained by fusion of the phases across neighboring segments using the data phase continuity property. The offering of the proposed technique is demonstrated using synthetic data in an over-the-horizon radar platform., See also ADA561051. AOARD-CSP-111007 International Conference on Acoustics, Speech and Signal Processing (37th) (ICASSP 2012) Held in Kyoto, Japan on March 25-30, 2012. U.S. Government or Federal Purpose Rights License., The original document contains color images.

In this article, we consider the joint estimation of direction-of-departure (DOD) and direction-of- arrival (DOA) information of maneuvering targets in a bistatic multiple-input multiple-output (MIMO) radar system that exploits spatial time-frequency distribution (STFD). STFD has been found useful in solving various array processing problems, such as direction finding and blind source separation, where nonstationary signals with time-varying spectral characteristics are encountered. The STFD approach to array processing has been primarily limited to conventional problems for passive radar platform that deals with signal arrivals, while its use in a MIMO radar configuration has received much less attention. This paper examines the use of STFD in MIMO radar systems with application to direction finding of moving targets with nonstationary signatures. Within this framework, we consider the use of joint transmit and receive apertures for the improved estimation of both target time-varying Doppler signatures and joint DOD/DOA. It is demonstrated that the STFD is an effective tool in MIMO radar processing when moving targets produce Doppler signatures that are highly localized in the time-frequency domain. Keywords: radar signal processing; MIMO radar; direction finding; joint DOD/DOA estimation; time- frequency analysis; moving target tracking., Pub in EURASIP Journal on Advances in Signal Processing 2012, Prepared in collaboration with Air Force Research Lab., (AFRL/RYMD), Wright-Patterson AFB, OH

Target altitude estimation is an important problem in overthe-horizon radar (OTHR) systems for target tracking and classification. Narrow signal bandwidth and rich propagation environment in OTHR render this problem difficult and challenging. In this paper, we develop a novel technique that allows accurate altitude estimation for a maneuvering target during its ascending or descending flight. The proposed technique considers a multiple-input multiple-output (MIMO) OTHR platform and utilizes the difference in the Doppler signatures generated in a micro-multipath propagation environment. The separability of the multi-component Doppler signatures in the time-frequency domain is used to individually estimate the elevation angles of these paths, which are otherwise infeasible to obtain, due to their small angular separation. The estimated elevation angle information is then used to achieve a reliable estimate of the target altitude., Presented at the IEEE Sensor Array and Multichannel Signal Processing Workshop held in Hoboken, NJ on 17-20 June 2012. The original document contains color images.

This paper considers the problem of detecting a multichannel signal in partially homogeneous environments, where the disturbances in both test signal and training signals share the same covariance matrix up to an unknown power scaling factor. Two different parametric Rao tests, referred to as the normalized parametric Rao (NPRao) test and the scale-invariant parametric Rao (SI-PRao) test, respectively, are developed by modeling the disturbance as a multichannel autoregressive (AR)process. The NPRao and SI-PRao tests entail reduced training requirement and computational efficiency, compared with conventional fully adaptive, covariance matrix based solutions. The SI-PRao test attains asymptotically a constant false alarm rate (CFAR) that is independent of the covariance matrix and power scaling factor of the disturbance. Comparisons with the covariance matrix based, scale-invariant generalized likelihood ratio test (GLRT), also known as the adaptive coherent estimator (ACE), are included. Numerical results show that the parametric Rao detectors, in particular the SI-PRao test, attain considerably better detection performance and use significantly less training than the ACE detector., Published in IEEE Transactions on Aerospace and Electronic Systems, Preprint. The original document contains color images.

This paper considers the problem of space-time adaptive processing (STAP) in non-homogeneous environments where the disturbance covariance matrices of the training and test signals are assumed random and different with each other. A Bayesian detection statistic is proposed by incorporating the randomness of the disturbance covariance matrices, utilizing a priori knowledge, and exploring the inherent Block-Toeplitz structure of the spatial-temporal covariance matrix. Speci cally the Block-Toeplitz structure of the covariance matrix allows us to model the training signals as a multichannel auto-regressive (AR) process and hence, develop the Bayesian parametric adaptive matched lter (B-PAMF) to mitigate the training requirement and alleviate the computational complexity. Simulation using both simulated multichannel AR data and the challenging KASSPER data validates the effectiveness of the B-PAMF in non-homogeneous environments., See also ADM002322. Presented at the 2010 IEEE International Radar Conference (9th) Held in Arlington, Virginia on 10-14 May 2010. Sponsored in part by the Navy.

This paper reviews recent advances in designing unimodular sequences with good auto/cross correlation properties along with a new approach that emphasizes on independent receiver design. The general problem is to design single or multiple sequences with constant modulus in the timedomain such that their respective matched filter outputs ideally resemble delta functions and the cross-matched filter outputs are zeros. In this context CAN (cyclic-algorithm new) and WeCAN (Weighted CAN) have been proposed for designing such sequences with good auto-cross correlation properties. In this paper, the equivalence of the CAN algorithms and the classic Gerchberg-Saxton (GS) algorithm involving the sequential magnitude substitution operations in the time and frequency domain is demonstrated. The design of unimodular sequences is further generalized here by considering the receiver design to be more general than the respective matched input sequences. The receiver design is carried out by taking care of the desired output requirements and the freedom present at the input can be used to further minimize the output side-lobe level., See also ADM002322. Presented at the 2010 IEEE International Radar Conference (9th) held in Arlington, VA on 10-14 May 2010. Published in the Proceedings of the 2010 IEEE International Radar Conference (9th), p729-734, 2010. Sponsored in part by the U.S. Navy. The original document contains color images.

The high false alarm associated with conventional alongtrack interferometric synthetic aperture radar (ATI-SAR) is a big concern for any valuable military radar. To reduce the false alarm rate, this paper proposes a dual-threshold approach that combines the conventional interferometric phase detection with the SAR image amplitude detection. This yields two results: (1) the interferometric phase map (including target velocity information) obtained by applying the interferometric phase detection only to the pixels selected by the amplitude detection, and (2) the amplitude map (including target strength information) obtained by applying the amplitude detection only to the pixels selected by the interferometric phase detection. The concept is illustrated by the results obtained using the Jet Propulsion Laboratory's (JPL) AirSAR ATI data, collected in the Monterey Bay area, California. The ATI-SAR processing requires also a precise calibration of the platform's crab angle. This paper presents a simple blind-calibration method that does not require any knowledge of the background and/or the actual crab angle., See also ADM002017. Presented at the 2005 IEEE International Radar Conference held in Arlington, VA on 9-12 May 2005. Published in the Proceedings of the 2005 IEEE International Radar Conference, May 2005. The original document contains color images.

The range-dependent nature of the surface clutter power spectrum observed in monostatic or bistatic airborne radar systems results in a mismatch of the clutter covariance matrix (computed from a secondary set of range-cell data) relative to that of a possible target test cell, with attendant degradation of space-time adaptive processing (STAP) performance. In this paper, we develop a new method for predicting the test cell clutter covariance matrix by estimating the configuration system parameters that directly influence the clutter power spectrum. The method uses a multiple complex sinusoid model whose parameters are related to the configuration system parameters, which are then optimized to match the radar return pulse-train data in a least-squares sense. The estimated configuration parameters are then used to predict the clutter covariance matrix in the test cell, which is then used with traditional STAP methods. Computer simulation results are presented that demonstrate the significantly improved STAP performance obtained by the method developed here compared to the conventional method of using the sample covariance matrix estimated from secondary data., See also ADM002017. Presented at the International Radar Conference held in Arlington, VA on 9-12 May 2005. Prepared in cooperation with Raytheon Space and Airborne Systems. The original document contains color images.

A space based radar (SBR) by virtue of its motion generates a Doppler frequency component to the clutter return from any point on the earth as a function of the SBR - earth geometry. The effect of earth's rotation around its own axis also adds an additional component to this Doppler frequency. The overall effect of the earth's rotation on the Doppler turns out to be two correction factors in terms of a crab angle affecting the azimuth angle, and a crab magnitude scaling the Doppler magnitude of the clutter patch. Interestingly both factors depend only on the SBR orbit inclination and its latitude and not on the specific location of the clutter patch of interest. Further, it is shown that the crab angle has maximum effect for an SBR on a polar orbit that is above the equator. The crab magnitude on the other hand peaks for an SBR on an equatorial orbit. It is also shown that earth's rotation together with range foldover phenomenon significantly degrade the clutter suppression performance of adaptive processing algorithms. Detailed derivations of these results are presented in this paper., See also ADM002017. Presented at Proceedings of the 2005 IEEE International Radar Conference Record held in Arlington, VA on 9-12 May 2005. Published in 2005 IEEE International Radar Conference Record Proceedings, May 2005. U.S. Government or Federal Purpose Rights License. The original document contains color images.

In this paper, we describe a space-time adaptive processing (STAP) approach for bistatic space-based radar (SBR) systems. A candidate SBR system employing a transmitter at medium earth orbit (MEO) and an airborne receiver is defined. A STAP configuration for performing wide area ground moving target indication (GMTI) is also specified. STAP performance is analyzed using simulated data in two different bistatic SBR geometries. Standard STAP performance degradation is shown to be a function of the relative severity of clutter non-stationarity in the two scenarios. To provide enhanced estimation of the clutter statistics, we apply an approach based on an efficient form of an expectation-maximization (EM) algorithm for estimating covariance matrices in non-stationary interference. The STAP performance of the EM technique is compared to that of standard STAP processing and higher order Doppler warping. The EM approach is seen to produce a usable Doppler space fraction (USDF) that is significantly improved over the other techniques. It has several powerful features, including the ability to align non-linear angle-Doppler contours, adaptivity to interference without applying a fixed range-dependent transformation, and the ability to simultaneously compensate for different range dependencies in higher range and Doppler ambiguities. The EM STAP approach also naturally accommodates knowledge-aided STAP techniques that improve understanding of clutter reflectivity variations., See also ADM002017. Presented at the 2005 IEEE International Radar Conference Record held in Arlington, Virginia on May 9-12, 2005. Published in the Proceedings of the 2005 IEEE International Radar Conference Record. The original document contains color images.

Knowledge aided signal processing calls for the use of existing information such as digitized image maps to further suppress clutter in a typical radar detection operation. In the context of space based radar (SBR) applications, given the SBR parameters and the look angles the first problem is to translate these parameters into a data point on a map. This is realized by converting these parameters into conventional latitude and longitude and then using the corresponding image intensity to estimate the clutter covariance matrix. Using this approach, it is possible to pre-compute the desired weight vector for a location of interest on earth that the SBR is assigned to visit. As a result, using the measured data together with the weight vector, it is possible to test for target detection hypothesis accurately at the time of visit by the SBR., See also ADM002017. Presented at the IEEE International Radar Conference held in Arlington, VA on 9-12 May 2005. Published in the Proceedings of the IEEE International Radar Conference, 2005. The original document contains color images.

We present a statistical analysis of the recently proposed non-homogeneity detector (NHD) for Gaussian interference statistics. Specifically, we show that a formal goodness-of-fit test can be constructed by accounting for the statistics of the generalized inner product (GIP) used as the NHD test statistic. The normalized-GIP follows a central-F distribution and admits a canonical representation in terms of two statistically independent chi-squared distributed random variables. Moments of the GIP can be readily calculated as a result. These facts are used to derive the goodness-of-fit tests, which facilitate intelligent training data selection. We then address the issue of space-time adaptive processing (STAP) algorithm performance using the NHD as a pre-processing step for training data selection. Performance of the adaptive matched filter (AMP) method is reported using simulated as well as measured data., Published in Jnl. of Digital Signal Processing, v14 p253-267, 2004. Portions of this paper were presented at the Asilomar Conference on Signals, Systems and Computers (34th) held in Pacific Grove, CA in November 2000 and the National Radar Conference (RADAR-2001) held in Atlanta, GA in May 2001.

We present a statistical analysis of the recently proposed non-homogeneity detector (NHD) for Gaussian interference statistics. We show that a formal goodness-of-fitness test can be constructed by accounting for the statistics of the generalized inner product (GIP) used as the NHD test statistic. Specifically, the Normalized-GIP is shown to follow a central-F distribution and admits a canonical representation in terms of two statistically independent Chi-squared distributed random variables. Moments of the GIP can be readily calculated as a result. These facts are used to derive the goodness-of-fit tests, which facilitate intelligent training data selection. Additionally, we address the issue of space-time adaptive processing (STAP) algorithm performance using the NHD as a pre-processing step for training data selection. Performance results for the adaptive matched filter (AMF) method are reported using simulated as well as measured data.

We consider the statistical analysis of a recently proposed non-homogeneity detector (NHD) for Gaussian interference statistics. We show that a formal goodness-of-fit test can be constructed by accounting for the statistics of the generalized inner product (GIP). Specifically the Normalized-GIP follows a central-F distribution. This fact is used to derive the goodness-of-fit test in this paper. We also address the issue of space-time adaptive processing (STAP) algorithm performance using the NHD as a pre-processing step for training data selection. Performance results are reported using simulated as well as measured data., AFRL Contract Manager: Dr. James H. Michels/AFRL/SNRT/315-330-4432. Conference paper published in Proceedings of the 2001 IEEE Radar Conference, Atlanta, GA, pp.193-197, May 2001.

The purpose of this report is to assess the quality of the Multi-Channel Airborne Radar Measurements (MCARM) airborne data collected, evaluate several reduced rank Space-Time Adaptive Processing (STAP) algorithms, and provide guidance to researchers. The goal of MCARM is the development of STAP algorithms for enhanced target detection in severe environments through the use of real world multi-channel airborne data. The Rome Laboratory's MCARM program provides real data, monostatic and bistatic, for performance evaluation of existing and future STAP approaches. This report evaluates the performance of several state of the art STAP algorithms using monostatic MCARM data. The algorithm list includes Factored Time Space (FTS), Extended FTS Approach (EFA), Cross Spectral Metric (CSM), Joint Domain Localized (JDL), Principal Components, Parametric Adaptive Matched Filter (PAMP), and others. As expected, adaptive processing results in significant detection performance improvements over conventional beamforming techniques. N single algorithm can be deemed "best" as several factors affect performance. Two primary factors are the sample support requirement for interference estimation and computational complexity. A significant finding from the work is the non-homogeneous nature of the clutter returns. This nature violates the assumption of independent, identically distributed data for interference estimation illustrating the need for Non-Homogeneity Detectors (NHDs). NHDs offer improved detection and false alarm performance through better selection of secondary data., See also AD-A364 943.

This report documents the development, implementation and evaluation of signal generation and processing techniques oriented towards improving the detection and estimation of targets and other parameters in noisy and cluttered environments. It also attempts to quantify improvements in the detection and estimation of targets in noisy and cluttered environments. Three methods for signal generation are discussed in the report. The first is referred to as a physical model signal generation. The second technique describes the representative model. The third technique is hybrid generated from both previously described technique.

This report considers the impact of antenna mutual coupling on adaptive phased array radar signal processing. First, the impedance matrix is developed via the method of moments and utilized in an airborne phased array radar data generation code. Next, the effect of mutual coupling on the true and estimated covariance matrices is considered. Finally, performance results are considered for a space-time adaptive processing algorithm in terms of probability of detection as well as plots of the test statistics versus angle-doppler.

This report presents an analysis of the clutter characteristics as observed from a radar platform either the monostatic or the bistatic configuration, airborne or stationary. The study shows the effects of the system parameters, particularly the system geometry, on the observed clutter characteristics. A preliminary study of adaptive space-time processing for multistatic active radar is also included in the report. There are two major factors that need to be investigated further development of space-time processing for the multistatic system; (1) the correlation of the observed interference among the receivers; and (2) the effects of system geometry on the observed reverberation characteristics. The analysis shows that problem interferences are uncorrelated. However, this may not be the case when multipath occurs.

A procedure is designed for the Automated Statistical Characterization and Partitioning of Environments (A'SCAPE). A'SCAPE uses two procedures to partition regions into homogeneous areas. These are: (1) a mapping procedure that uses image processing to separate regions; and (2) a statistical procedure that uses statistics to separate regions. A'SCAPE is able to process real data through its image processing, statistical algorithms, and expert system rules. A parametric technique developed by LeCadre for the detection of signals in the presence of clutter and additive noise was also studied. This technique assumes an AR(MA) model of the interference. The order of the model is first estimated, the AR parameters are estimated, and then a covariance matrix is derived.

The Matrix Pencil Approach (1) was shown to be an effective and efficient method for estimating the angles of arrival of multiple narrowband sources. It is classified as non search procedure. Therefore, it is computationally less complicated and eliminates problems encountered in search procedures with regard to memory storage and system calibration. Having collected the data from the outputs of a linear uniformly spaced array consisting of m sensors, the objective is to estimate the locations of the d sources (d m). The information about the parameters of interest are contained in the rank reducing values of a matrix pencil generated from the set of data.

The ESPRIT algorithm has been shown to be an effective solution to the angle of arrival estimation problem. One possibility for the implementation of ESPRIT is the use of a linear array to provide the sensor paris. This paper discusses a technique for compensation of mutual coupling effects between array elements. Computer simulations demonstrate a significant improvement in performance. (r.r.h.)

This effort deals with the compensation of mutual coupling effects in a sensor array when using the subspace approach known as the matrix pencil. This method is easy to implement and is applicable even in the presence of coherent sources. Computer simulation shows that use of the mutual impedance matrix effectively compensates for the presence of mutuals. (r.h.)

The Matrix Pencil Approach (1) was shown to be an effective and efficient method for estimating the angles of arrival of multiple narrow-band sources. Having collected the data from the outputs of a linear array consisting of m sensors, the objective is to estimate the locations of the d sources (d $<$ m). The information about the parameters of interest are contained in the rank reducing values of a matrix pencil generated from the set of data. Several extensions of the Matrix Pencil Approach appear in this dissertation. Given d correlated sources, it is shown that improved performance can be achieved using a data window of length L = d. Previous developments of high resolution algorithms neglected the effects of mutual coupling which occurs between the elements of an array. Failure to account for these effects results in poor performance. We have successfully devised compensating schemes for both ESPRIT and the Moving Window. Three techniques dealing with wideband signals are proposed in this dissertation. In the first method the wideband signals are modeled as sums of exponentials. This model is suitable for non stationary signals. The estimation procedure consists of estimating both natural frequencies and angles of arrival of the sources by means of three matrix pencils. A second method is proposed where the wideband sources are assumed to be linear systems driven by white noise. This model is appropriate for stationary signals. The analysis is carried out on the unit circle using the Discrete Fourier Transform. The third approach makes use of the coherent signal subspace method proposed by Wang and Kaveh (56). Finally, we have studied the effects of perturbation due to noise and due to sensor spacing. We have derived upper bounds for the Chordal Metric which is a measure between the true eigenvalue and the perturbed one. The chordal metric is shown to be a functional of the true and the perturbed angles of arrival. Computer simulations are carried out for each

This work deals with the problem of direction finding using the matrix-pencil approach. Consider a linear array of m sensor and assume there are d narrowband sources. Having collected the data at the m sensors, the problem is to estimate the direction of arrival of these sources. The matrix-pencil approach is a non-search procedure, thus very easy to use. In this study, a generalization of the method to a linear array of m identical sensors with some arbitrary beam pattern was performed. It is also shown that the method still works when using different windows. The only restriction is that at least d elements of the window be non-zero to ensure the validity of the algorithm. A perturbation analysis due to equal sensor spacing was also performed. The concept of the chordal metric was introduced. It is shown that the bound derived on the chordal metric is equivalent to the chordal metric itself. The problem of estimating both the angular frequencies and the angles of arrival of the sources was then posed. It is proven that the method still works; i.e., the angular frequencies and the locations of the sources were estimated using two matric pencils. The rank values of these matrices is shown to contain both the angles of arrival and the angular frequencies of the sources. A computer simulation was performed each time to ensure the effectiveness of the method.