Duc Vu

Ground Moving Target Indication via Multichannel Airborne SAR

We consider moving target detection and velocity estimation for multichannel synthetic-aperture-radar (SAR)-based ground moving target indication (GMTI). Via forming velocity versus cross-range images, we show that small moving targets can be detected even in the presence of strong stationary ground clutter. Furthermore, the velocities of the moving targets can be estimated, and the misplaced moving targets can be placed back to their original locations based on the estimated velocities. An iterative adaptive approach, which is robust and user parameter free, is used to form velocity versus cross-range images for each range bin of interest. Moreover, we discuss calibration techniques to estimate the relative antenna distances and antenna gains in practical systems. Furthermore, we present a simple algorithm for stationary clutter cancelation. We conclude by demonstrating the effectiveness of our approaches by using the Air Force Research Laboratory publicly released Gotcha airborne SAR-based GMTI data set.

Nonparametric Missing Sample Spectral Analysis and Its Applications to Interrupted SAR

We consider nonparametric adaptive spectral analysis of complex-valued data sequences with missing samples occurring in arbitrary patterns. We first present two high-resolution missing-data spectral estimation algorithms: the Iterative Adaptive Approach (IAA) and the Sparse Learning via Iterative Minimization (SLIM) method. Both algorithms can significantly improve the spectral estimation performance, including enhanced resolution and reduced sidelobe levels. Moreover, we consider fast implementations of these algorithms using the Conjugate Gradient (CG) technique and the Gohberg-Semencul-type (GS) formula. Our proposed implementations fully exploit the structure of the steering matrices and maximize the usage of the fast Fourier transform (FFT), resulting in much lower computational complexities as well as much reduced memory requirements. The effectiveness of the adaptive spectral estimation algorithms is demonstrated via several numerical examples including both 1-D spectral estimation and 2-D interrupted synthetic aperture radar (SAR) imaging examples.

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.

On MIMO radar transmission schemes for ground moving target indication

We compare several multiple-input multiple-output (MIMO) radar transmission schemes, including code division, time division and Doppler frequency division multiplexing approaches, for ground moving target indication (GMTI). To utilize probing waveforms with low sidelobe levels for range compression, we transmit sequences specifically designed to have low correlation levels. At the receiver side, we apply the iterative adaptive approach (IAA), which uses only the primary data, to form high resolution angle-Doppler images. To mimic real world scenarios, we apply our algorithms to a simulated dataset which contains high-fidelity, site-specific, simulated ground clutter returns. By combining the usage of intelligent transmission schemes, probing waveforms with good correlation properties, and the adaptive angle-Doppler imaging approach, we show that slow moving targets can be more clearly separated from the clutter ridge in the angle-Doppler images and potentially more easily detected by MIMO radar than by its conventional single-input multiple-output (SIMO) counterpart.

Construction of unimodular sequence sets for periodic correlations

Sequence sets with good periodic correlation properties can be used in many areas, including communications, medical imaging, radar (such as over-the-horizon radar) and sonar. Practical hardware constraints, such as power amplifiers, usually require the transmitted waveforms be unimodular. We present herein new computationally efficient algorithms that can be used for the design of unimodular sequence sets with essentially zero auto-correlation sidelobes and cross-correlations in a specified time lag zone, as well as of sequence sets with good correlations over all time lags. The proposed algorithms start from random phase initializations and can generate many different sequence sets (including very long sequence sets) possessing similarly good correlation properties.

SAR based adaptive GMTI

We consider ground moving target indication (GMTI) and target velocity estimation based on multi-channel synthetic aperture radar (SAR) images. Via forming velocity versus cross-range images, we show that small moving targets can be detected even in the presence of strong stationary ground clutter. Moreover, the velocities of the moving targets can be estimated, and the misplaced moving targets can be placed back to their original locations based on the estimated velocities. Adaptive beamforming techniques, including Capon and generalizedlikelihood ratio test (GLRT), are used to form velocity versus cross-range images for each range bin of interest. The velocity estimation ambiguities caused by the multi-channel array geometry are analyzed. We also demonstrate the effectiveness of our approaches using the Air Force Research Laboratory (AFRL) publicly-released Gotcha SAR based GMTI data set.

Ground moving target indication via multi-channel airborne SAR

We consider moving target detection and velocity estimation for multi-channel synthetic aperture radar (SAR) based ground moving target indication (GMTI). Via forming velocity versus cross-range images, we show that small moving targets can be detected even in the presence of strong stationary ground clutter. Furthermore, the velocities of the moving targets can be estimated, and the misplaced moving targets can be placed back to their original locations based on the estimated velocities. An iterative adaptive approach (IAA), which is robust and user parameter free, is used to form velocity versus cross-range images for each range bin of interest. Moreover, we discuss calibration techniques to combat near-field coupling problems encountered in practical systems. Furthermore, we present a sparse signal recovery approach for stationary clutter cancellation. We conclude by demonstrating the effectiveness of our approaches by using the Air Force Research Laboratory (AFRL) publicly-released Gotcha airborne SAR based GMTI data set.

Probing waveform synthesis and receive filter design for active sensing systems

Probing waveform synthesis and receive filter design play crucial roles in achievable performance for active sensing applications, including radar, sonar, and medical imaging. We focus herein on conventional single-input single-output (SISO) radar systems. A flexible receive filter design approach, at the costs of lower signal-to-noise ratio (SNR) and higher computational complexity, can be used to compensate for missing features of the probing waveforms. A well synthesized waveform, meaning one with good autocorrelation properties, can reduce computational burden at the receiver and improve performance. Herein, we will highlight the interplay between waveform synthesis and receiver design. We will review a novel, cyclic approach to waveform design, and then compare the merit factors of these waveforms to other well-known sequences. In our comparisons, we will consider chirp, Frank, Golomb, and P4 sequences. Furthermore, we will overview several advanced techniques for receiver design, including data-independent instrumental variables (IV) filters, a data-adaptive iterative adaptive approach (IAA), and a data-adaptive Sparse Bayesian Learning (SBL) algorithm. We will show how these designs can significantly outperform conventional matched filter (MF) techniques for range compression as well as for range-Doppler imaging.

Nonparametric missing sample spectral analysis and its applications to interrupted SAR

We consider nonparametric adaptive spectral analysis of complex-valued data sequences with missing samples occurring in arbitrary patterns. We first present two high-resolution missing-data spectral estimation algorithms: the Iterative Adaptive Approach (IAA) and the Sparse Learning via Iterative Minimization (SLIM) method. Both algorithms can significantly improve the spectral estimation performance, including enhanced resolution and reduced sidelobe levels. Moreover, we consider fast implementations of these algorithms using the Conjugate Gradient (CG) technique and the Gohberg-Semencul-type (GS) formula. Our proposed implementations fully exploit the structure of the steering matrices and maximize the usage of the Fast Fourier Transform (FFT), resulting in much lower computational complexities as well as much reduced memory requirements. The effectiveness of the adaptive spectral estimation algorithms is demonstrated via several 2-D interrupted synthetic aperture radar (SAR) imaging examples.

Fiber optic sensor system for controlling salt contents in the near-coastal shrimp farms

The fiber optic sensor system, that consists of 6 - 8 fiber sensor heads and can measure the salt-content in the water of near-coastal region of 1 Km2, is presented. The fiber optic sensor head was made by polishing the end of a double fiber in the form of a prism, with reflecting coating on one side, and had a dimension of 250 - 300 microns. The fiber optic sensor with lead-in power of 3 - 15 mW from visible laser diodes was tested for measuring salt content in the seawater from 0 to 4 wt.%. The results are repeatable and the accuracy less than 10-3 is suitable for controlling the salinity degree in the range of 0.5 - 1.7% in the near coastal shrimp farms, especially for the childhood shrimp fields.