Forming images of aircraft using passive radar systems that exploit "illuminators of opportunity," such as commercial television and FM radio systems, involves reconstructing an image from sparse samples of ...
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(纸本)0819444774
Forming images of aircraft using passive radar systems that exploit "illuminators of opportunity," such as commercial television and FM radio systems, involves reconstructing an image from sparse samples of its Fourier transform. For a given flight path, a single receiver-transmitter pair produces one arc of data in Fourier space. Since the resulting Fourier sampling patterns bear a superficial resemblance to those found in radio astronomy, we consider using deconvolution techniques borrowed from radio astronomy, namely the clean algorithm, to form images from passive radar data. Some deconvolution techniques, such as the clean algorithm, work best on images which are well-modeled as a set of distinct point scatterers. Hence, such algorithms are well-suited to high-frequency imaging of man-made targets, as the current on the scatterer surface tends to collect at particular points. When using low frequencies of interest in passive radar, the images are more distributed. In addition, the complex-valued nature of radar imaging presents a complication not present in radio astronomy, where the underlying images are real valued. These effects conspire to present a great challenge to the clean algorithm, indicating the need to explore more sophisticated techniques.
A method for spectral analysis of nonequidistantly spaced time series is presented: the clean algorithm performs an iterative deconvolution of the spectral window in the frequency domain. We demonstrate the capability...
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A method for spectral analysis of nonequidistantly spaced time series is presented: the clean algorithm performs an iterative deconvolution of the spectral window in the frequency domain. We demonstrate the capability of the method on synthetic data examples and apply clean to seismological data, in an example where we seek temporal changes in elastic wave velocities. The observed periodic changes of phase differences consist of frequencies, which in principle can be explained by the influence of solid earth tides, but also by other effects with similar periodicities. Only clean enabled us to enlarge the time window over missing data segments until the frequency resolution was accurate enough to rule out solid earth tides as cause for the observed periodic changes. A MATLAB version of the clean algorithm is available from the authors, or from the IAMG server, (C) 1999 Elsevier Science Ltd. All rights reserved.
The application of sensor array processing methods for estimation and localization of wavefield sources is well known and has been intensively studied in literature. In this paper we extend sensor array processing app...
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The application of sensor array processing methods for estimation and localization of wavefield sources is well known and has been intensively studied in literature. In this paper we extend sensor array processing approach to estimating the parameters of the fields of nonwave nature (the so-called nonwave fields). Considering the static and the diffusion field as typical examples of nonwave fields, and assuming that measurements are carried out by an antenna array, we derive the Cramer-Rao bounds of source parameter estimation errors. These theoretical results are completed by the experimental results of localization of the diffusion sources in distilled water by chemical sensor array, showing high performance of sensor array processing approach to the problem considered. A modified version of the well-known clean deconvolution algorithm has been used for experimental data processing.
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