Xumin Zhu

On Using a priori Knowledge in Space-Time Adaptive Processing

In space-time adaptive processing (STAP), the clutter covariance matrix is routinely estimated from secondary ldquotarget-freerdquo data. Because this type of data is, more often than not, rather scarce, the so-obtained estimates of the clutter covariance matrix are typically rather poor. In knowledge-aided (KA) STAP, an a priori guess of the clutter covariance matrix (e.g., derived from knowledge of the terrain probed by the radar) is available. In this note, we describe a computationally simple and fully automatic method for combining this prior guess with secondary data to obtain a theoretically optimal (in the mean-squared error sense) estimate of the clutter covariance matrix. The authors apply the proposed method to the KASSPER data set to illustrate the type of achievable performance.

Waveform Synthesis for Diversity-Based Transmit Beampattern Design

Transmit beampattern design is a critically important task in many fields including defense and homeland security as well as biomedical applications. Flexible transmit beampattern designs can be achieved by exploiting the waveform diversity offered by an array of sensors that transmit probing signals chosen at will. Recently proposed techniques for waveform diversity-based transmit beampattern design have focused on the optimization of the covariance matrix R of the waveforms, as optimizing a performance metric directly with respect to the waveform matrix is a more complicated operation. Given an R, the problem becomes that of determining a signal waveform matrix X whose covariance matrix is equal or close to R, and which also satisfies some practically motivated constraints. We propose a cyclic optimization algorithm for the synthesis of such an X, which (approximately) realizes a given optimal covariance matrix R under various practical constraints. A number of numerical examples are presented to demonstrate the effectiveness of the proposed algorithm.

High Resolution Angle-Doppler Imaging for MTI Radar

To reduce the need for training data or for accurate prior knowledge of the clutter statistics in space-time adaptive processing (STAP), we consider high resolution angle-Doppler imaging by processing each range bin of interest independently. Specifically, we use a weighted least-squares-based iterative adaptive approach (IAA) to form angle-Doppler images of both clutter and targets for each range bin of interest. The resulting angle-Doppler images can be used with localized detection approaches for moving target indication (MTI). We show via numerical examples that the robust and nonparametric IAA algorithm can be used to enhance the MTI performance significantly as compared with existing approaches.

MIMO radar angle-doppler imaging via iterative space-time adaptive processing

We consider using multi-input multi-output (MIMO) radar to improve the ground moving target indication (GMTI) performance, especially for slowly moving targets, for airborne surveillance systems. The increased virtual aperture afforded by MIMO radar systems enables many advantages, including enhanced spatial resolution, improved parameter identifiability and better performance for GMTI. To obviate the need of secondary data for space-time adaptive processing (STAP), we apply herein a user parameter-free and secondary data-free fully automatic weighted least squares based iterative adaptive approach (IAA) to angle-Doppler imaging via a standard MIMO scheme, two simplified MIMO schemes (which employ switching strategies for transmission), and also a conventional single-input multi-output (SIMO) scheme. The high-resolution angle-Doppler images formed by IAA, using the primary data only, are provided to compare the performance of the three MIMO schemes as well as the SIMO scheme.

MIMO radar receiver design

Compared to traditional phased-array radar, multiple-input multiple-output (MIMO) radar can offer better parameter identifiability and higher resolution, enable the direct applicability of adaptive techniques for effective interference and jamming suppression, and allow for much flexibility for transmit beampattern design and waveform optimization. Reaping the full benefit of the superior performance enabled by the MIMO radar requires a novel design of its receive filters to minimize the impact of scatterers in nearby range bins on the received signals from the range bin of interest (the so-called range compression problem). Recently, an instrumental variables (IV) approach to MIMO radar receive filter design was proposed and shown to outperform the conventional matched filter approach. In this paper, we will present a least squares (LS) based MIMO radar receiver design. We will compare the performances of the IV and LS approaches with that of the matched filter method under a variety of test conditions.

Knowledge-Aided Space-Time Adaptive Processing

A fundamental issue in knowledge-aided space-time adaptive processing (KA-STAP) is to determine the degree of accuracy of the a priori knowledge and the optimal emphasis that should be placed on it. In KA-STAP, the a priori knowledge consists usually of an initial guess of the clutter covariance matrix. This can be obtained either by previous radar probings or by a map-based study. In this paper, we consider a linear combination of the a priori clutter covariance matrix with the sample covariance matrix obtained from secondary data, and derive an optimal weighting factor on the a priori knowledge by a two-step maximum likelihood (ML) approach. The performance of the two-step ML approach is compared with that of the convex combination (CC) approach and is evaluated using the KASSPER data.

Iterative Space-Time Adaptive Processing

To reduce the need of secondary data and/or accurate prior knowledge of the clutter statistics in space-time adaptive processing (STAP), we present herein a user parameter-free and secondary data-free fully automatic weighted least squares based iterative adaptive approach (IAA) to angle-Doppler imaging for airborne surveillance radar systems.

Knowledge-aided adaptive beamforming

In array processing, when the available snapshot number is comparable with or even smaller than the sensor number, the sample covariance matrix R is a poor estimate of the true covariance matrix R. To estimate R more accurately, we can make use of prior environmental knowledge, which is manifested as knowing an a priori covariance matrix R0. In this paper, we consider both modified general linear combinations (MGLC) and modified convex combinations (MCC) of the a priori covariance matrix R0, the sample covariance matrix R, and an identity matrix I to get an enhanced estimate of R, denoted as R. Numerical examples are provided to demonstrate the type of achievable performance by using R instead of R in the standard Capon beamformer.