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Absfmef-This paper dseusss emulati moving targeb on compad radar ranges We show that Ul6 d e ranges used today are limited to emulating target & of 4 ds, and can not produce &ta with the range bin migralion effecis that ( E " when targets are traveling at realistic Speeas. We prupose a Y64* ?e systcm capable of emulating higher target speeds. I d e x rems-. Compact Range, Moving Target, Range Bin Migration L INTRODUCTIONCompact radar ranges (CRRs) are used to collect scaled radar cross section data to circumvent the high cost of obtaining the data'using airborne synthetic aperture radar (SAR) and full-sale targets. CRRs use a fixed radar Transmitter and Receiver with a scaled target on a rotating platform. The rotation of the platform synthesizes an extended coherent aperture for cross-range resolution.For range resolution. CRRs use N E coherent-time gated sinusoidal pulsed signals with wave numbers k. = 21r J . and uniform stepping interval Ak , k, = k, + ( P I -( Nr -1)/2) At,for n = 0.1 ,..., N ' -1. (1) Continuous wave (CW) signals are transmitted for a time interval Tp , with Tp 2 1/Ak . After coherent demodulation to an intermediate frequency. the scattered pulses are all individually simpled at a rate A, commensurate with the Nyquist sampling requirements and digitized. commonly used to effectively extract the scattering density. The fmt, which we call tomographic deconvolution [ 1.2, 31, solves for the scattering density by taking the discrete Fourier transformof the spatially sampled data This approach is usually.limited to applications where the illuminating fields can be treated as plane waves. The second approach uses phase shift focusing in lieu of time delay focusing to focus the coherent scattered signals captured over a sampled spatial apemue. The image in the second approach is defined as the square mapirude of the coherent sum of the focused signals. For cases where the targets are effectively in the far field of the spatial synthetic aperture, both these approaches produce the same results. Both approaches contain various approximations and specific choices of pixel parameters to expedite the processing. The mathematical development of the second .-* ?his work was supported in pan by the NSF under grant -.There are two signal-processing methods that are I CCR-0208830 approach follows in a smightfomard logical fashion. Moreover it is particularly effective for treating imaging scenarios involving both non-uniform illuminating fields and moving targets. Accordingly, the mathematical development featured herein will follow this second approach. II. S C A~O S O U m O N T O W A V E WLJATIONCRRs are operated at small bistatic angles, with the distances from the feed horn to the image maps very large compared to the wavelength and dimensions of antennas [6]. In this regime the radiation signals are described by 'outgoing spherical waves with axial envelopes dictated by the diffraction pattern of the transmining physical antenna. In the case of the indoor CRR, the transmit receive (TR) coherent apertur...

We consider in this paper the improvement of side-attack mine detection by performing confidence level fusion with data collected from vehicle-mounted forward-looking IR and GPR (FL-GPR) sensors. The mine detection system is vehicle based, and has both IR and FL-GPR sensors mounted on the top of the vehicle. The IR images and FL-GPR data are captured as the vehicle moves forward. The detections from IR images are obtained from the Scale-Invariant Feature Transform (SIFT) and Morphological Shared-Weight Neural Networks (MSNN) depending on target characteristics, and those from FL-GPR are derived from the FL-GPR SAR images through object-tracking.Since the IR and FL-GPR alarms do not occur at the same location, the fusion process begins with each IR alarm and looks at the nearby FL-GPR alarms with confidences weighted by values that are inversely proportional to their distances to the IR alarm. The FL-GPR alarm with the highest weighted confidence is selected and combined with the IR confidence through geometric mean. An experimental dataset collected from a government test site is used for performance evaluation. At the highest Pd and comparing with IR only, fusing IR and FL-GPR yields a reduction of FAR by 26%. When the Hough transform is applied to reject the IR alarms that have irregular shapes, the fusion results provides a reduction of FAR by 35% at the highest Pd.

Mathematical morphology is a field of knowledge and techniques involving the application of nonlinear image processing operations to perform image enhancement, feature extraction, and segmentation as well as a variety of other tasks. Morphological operations have previously been combined with neural networks to produce detectors that learn features and classification rules simultaneously. The previous networks have been demonstrated to provide the capability for detecting occluded vehicles of specific types using LADAR, SAR, Infrared, and Visible imagery. In this paper, we describe the application of morphological shared weight neural networks to detecting off-route, or "side attack", mines. A pair of image sequences, both of the same scene, with and without a mine are presented to the system. The network then performs detection and decision-making on a per sequence basis.

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