Nikola S. Subotic

Multiresolution detection of coherent radar targets

We develop and investigate several novel multiresolution algorithms for detecting coherent radar targets embedded in clutter. These multiresolution detectors exploit the fact that prominent target scatterers interfere in a characteristic manner as resolution is changed, while multiresolution clutter signatures are random. We show, both on simulated and collected synthetic aperture radar data, that these multiresolution algorithms yield significant detection improvements over single-pixel, single-resolution constant false alarm rate (CFAR) methods that use only the finest available resolution.

Cyclostationary signal models for the detection and characterization of vibrating objects in SAR data

We present a novel method of detecting and characterizing vibrating objects in synthetic aperture radar (SAR) data. We model the SAR phase history as having cyclostationary characteristics when a vibrating object is present in the scene. Within this framework, we develop a generalized likelihood ratio test to detect the presence of the vibrating object and provide estimates of the vibration frequency, amplitude, and the spread of the vibration spectrum. We provide analytical and empirical results outlining the performance of this detection scheme.

A multiresolution approach to target detection in synthetic aperture radar data

We demonstrate the utility of a multiresolution approach for target detection in SAR imagery. In particular, man-made objects exhibit characteristic phase and amplitude fluctuations as the image resolution is varied, while natural terrain (i.e. clutter) has a random signature. We show that the multiresolution clutter process decomposes into a Brownian motion process in resolution. We then construct an optimal invariant multiresolution detector based on a derived multiresolution increments process and show that it significantly outperforms a standard energy detector operating on the finest available SAR resolution.<<ETX>>

Sequential processing of SAR phase history data for rapid detection

We apply a Wald sequential hypothesis test to synthetic aperture radar (SAR) phase history data. A 2D autoregressive (AR) signal model is applied to the SAR phase history data to exploit the signatures produced by cultural objects. We model the sequential probability ratio decision statistic as a Brownian motion process with drift. The test can then be simply approximated as a test for the drift in the Brownian motion process. We show that the required aperture extent (number of pulses) to determine the presence of a target within the scene is significantly smaller than the full aperture.

Robust material identification in hyperspectral data via multiresolution wavelet techniques

Reviews the characteristics of hyperspectral imaging sensors and describes several important data exploitation applications in remote sensing. The authors then focus on a particular signal processing application, material identification, and propose a novel algorithm based on multiresolution wavelet techniques. Finally, they demonstrate the multiresolution material identification algorithm on data collected with a all-band hyperspectral sensor.

Multiresolution generalized likelihood ratio detection approach to target screening in synthetic aperture radar data

We present a detection concept for initial target screening based on features that are derived from a multiresolution decomposition of synthetic aperture radar (SAR) data. The physical motivation of the multiresolution feature-based approach is the exploitation of signature oscillations produced by the interference between prominant scatterers in cultural objects when resolution is varied. We develop a generalized likelihood ratio test detector which differentiates between first order autoregressive multiresolution processes attributed to different spatial areas. We then derive two special cases of this detector motivated by arguments regarding the clutter statistics. We show that these schemes significantly outperform a standard energy detector operating on the finest available SAR resolution only.

Time-integrating compact hybrid optical processor for SAR image formation

The development and airborne demonstration of a compact realtime optical processor for synthetic aperture radar (SAR) image formation under the DARPA TOPS program is described. The ERIM spotlight mode SAR system and its processing requirements are presented. It is shown that a 2-D Fourier transforming time-integrating interferometrically based optical processor is an attractive solution to the processing requirements. The optical processor uses a modulated laser diode for radar signal insertion, crossed acousto-opto scanners for 2-D scanning, a modified Köster interferometer for fringe generation, fast detector arrays for light detection and integration, and accumulating frame grabbers to build up the dynamic range of the image. Analysis, simulation, and laboratory experimental results are presented.

Compact real-time interferometric Fourier transform processors

The spatially-scanning, 2D, time-integrating hybrid interferometric processors presented employ directly-modulated CW laser diodes as input sources and are applicable to complex SAR data processing in real time. Crossed acoustooptic cells scan the input signal over a virtual 2D input space, and an optical interference pattern is detected with a solid-state detector array camera. Continuous transfer of the frames of integrated interference from the camera to a digital image processor, overall dynamic range is increased over that of the camera alone. Envelope detection in the display circuitry generates a continuous real-time representation of image magnitude.

Construction of hybrid templates from collected and simulated data for SAR ATR algorithms

In this paper we describe a hybrid method of using collected and computer generated signatures for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) algorithms. Currently, there is significant activity in developing both model-based (e.g. MSTAR) and collected template-based (e.g. STARLOS, SAIP) approaches. Each approach has significant strengths and weaknesses. The strength of the model-based approach is that it can finely sample the many competing hypotheses that describe the data under test. Collected template-based approaches have a strong sense of realism due to the fact that the templates were collected using actual deployed targets and SAR systems. A hybrid approach attempts to meld the strengths of both. We describe methodologies for determining which segments of a reference signature should be provided by collected data or models and how they should be combined. We also show that these new hybrid templates outperform either a model-only or collected template-only approach for target classification. ATR performance results are provided in the form of ROC curves. Also, some topics for future research are discussed.

Optical processor for SAR image formation: performance trades

We describe some performance trades for a hybrid optical processor for real-time synthetic aperture radar (SAR) image formation. A 2D Fourier transforming time-integrating interferometrically based optical processor is a key element of the system. The optical processor uses a modulated laser diode for radar signal insertion, crossed 1D acousto-optic scanners for 2D scanning, a modified Koster interferometer for fringe generation, and fast detector arrays (cameras) for light detection and integration. The image dynamic range is enhanced by processing many camera frames. Digital pre- and post-processing play essential roles in the system enhancement. We present the characteristics of this type of processor and consider some of its performance trades. The optical processor design approach lends itself to the important attributes of high (real-time) data rates, multiple SAR mode processing capabilities, compact and rugged packaging, and power efficiency.