Coherent variably spaced pulse trains can be processed using a time domain representation, i.e., describing the signal processor by a set of complex weights applied to the successive pulses rather than by a filter transfer function. The variations in clutter return during the pulse train are expressed in terms of a covariance matrix rather than a frequency spectrum. Using this approach, methods of selecting the optimum set of complex weights for a pulse train are derived. A test based on the likelihood ratio is discussed and an optimum set of complex weights is obtained for detecting targets of known doppler frequency. A more tractable criterion, based on the maximum signal-to-clutter ratio, is developed for selection of the pulse weights. The analogous problem for equally spaced pulse trains has been discussed by Rummler. With equally spaced trains, the design problem is one of selecting an optimum set of complex weights for the pulses and of specifying the interpulse spacing. With variable spacings there is the additional problem of selecting a good interpulse spacing code. No direct method for optimizing the spacing code was found. It is shown that for a particular form of the clutter autocorrelation function, the covariance matrix can be inverted and an analytic expression obtained for system performance as a function of target doppler frequency. This should be useful in testing different spacing codes so that one can be selected for a particular system application.

Annotation The first volume in a new series. Contributed papers give a theory of radar signal processing at a level accessible and useful to practicing radar engineers concerned with design and analysis. No index. Annotation(c) 2003 Book News, Inc., Portland, OR (booknews.com)

Learn about the most recent theoretical and practical advances in radar signal processing using tools and techniques from compressive sensing. Providing a broad perspective that fully demonstrates the impact of these tools, the accessible and tutorial-like chapters cover topics such as clutter rejection, CFAR detection, adaptive beamforming, random arrays for radar, space-time adaptive processing, and MIMO radar. Each chapter includes coverage of theoretical principles, a detailed review of current knowledge, and discussion of key applications, and also highlights the potential benefits of using compressed sensing algorithms. A unified notation and numerous cross-references between chapters make it easy to explore different topics side by side. Written by leading experts from both academia and industry, this is the ideal text for researchers, graduate students and industry professionals working in signal processing and radar.

Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.