In real-time digital-signalprocessing systems, data often enter or leave the computationally intensive parts at small integer multiples of the clocking interval. In these cases, traditional microprocessor-based archi...
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In real-time digital-signalprocessing systems, data often enter or leave the computationally intensive parts at small integer multiples of the clocking interval. In these cases, traditional microprocessor-based architectures are inadequate to meet the throughput requirements, and so clusters of dedicated data paths, hard-wired to closely match the algorithmic data flow, are often used. These architectures typically contain multiple concurrently operating data-path pipelines. The circuit reported targets the rapid implementation and prototyping of such architectures using reconfigurable multiprocessors. This circuit contains eight execution units (EXUs) connected via a dynamically controlled crossbar switch. It can operate at 25 MHz (200 MIPs) with a data I/O bandwidth of 400 MB/s and a typical power consumption of 0.45 W. It contains 140106 transistors on a 8.9*9.5 mm/sup 2/ die, in 1.2- mu m CMOS technology.< >
Features extracted from the bispectrum of radar signals are used for classification of unknown radar targets. The classification performance compared with the performance of other classifiers that are not based on hig...
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Features extracted from the bispectrum of radar signals are used for classification of unknown radar targets. The classification performance compared with the performance of other classifiers that are not based on higher-order spectral processing of the measured radar data. The radar signals used are experimental measurements that correspond to scattering from real radar targets. The data is corrupted with different types of disturbances that are likely to occur in a typical radar system. Although the number of data samples is relatively small and may be insufficient to produce very accurate bispectral estimates, it is concluded that the bispectrum classifier may outperform other known classifiers under conditions of colored noise and non-Gaussian noise.< >
For radar applications requiring angle measurements on ground clutter over a large range swath or large elevation-angle extent, the single-pulse clutter-to-noise power ratio can be small due to low intercept grazing a...
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For radar applications requiring angle measurements on ground clutter over a large range swath or large elevation-angle extent, the single-pulse clutter-to-noise power ratio can be small due to low intercept grazing angles or low reflectivity. An approach is presented for using multiple returns to derive an angle variance estimate as a direct measure of data validity, rather than an amplitude threshold to better detect the presence of angle glint prior to angle estimation. The variance estimator can be used to detect the presence of spatially distributed interference returns such as those from a rain cloud. Results of a Monte Carlo signal simulation are presented to demonstrate the expected angle accuracy performance when the variance estimator is used in a phase interferometer radar. These results are compared to angle accuracy estimates based on an amplitude discriminant. small integration sample size and degradation due to thermal noise, clutter, and glint are considered. The results are applicable to measurements in either azimuth or elevation angle and to discrete targets with Swerling-I amplitude fluctuation.
Image reconstruction from projections has been extensively studied in radioastronomy and medical imaging. The same techniques can be applied to multiple target detection tasks such as radar or sonar signalprocessing....
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Image reconstruction from projections has been extensively studied in radioastronomy and medical imaging. The same techniques can be applied to multiple target detection tasks such as radar or sonar signalprocessing. However, in medical imaging and radioastronomy, the images to be reconstructed are generally "compact" and the ratio between the required image size and resolution is small compared to that of the target detection problem where the images are sparse and fine resolution is required. Therefore a larger number of basis functions will be necessary to discretize the general image. Thus medical image reconstruction techniques applied directly to target detection can result in excessive memory requirements, computational time and required number of measurements. With modifications that consider the sparseness and positiveness of the images, reconstruction techniques have a potentially valuable application in the multiple target detection problem. In this paper we propose two modifications of the medical image reconstruction technique for the multi-target detection problem. One consists of pre-processing the data in order to reduce the total image to a smaller set of regions likely to contain targets. The second consists of dividing the image into pixels much larger than the expected size of a target and estimating the total target intensity for each pixel.
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