Richard A. Schneible

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This paper details a preliminary investigation into space-time-waveform adaptive processing for waveform diverse distributed apertures. The large baseline of such a distributed radar results in angular resolution that is orders of magnitude better than the resolution of a monolithic system (single large radar) with the same power-aperture. This capability(More)
This paper furthers the development of signal processing for distributed, waveform diverse, antenna arrays. The long term goal is to develop practical waveform-time-space adaptive processing algorithms for distributed apertures. A crucial issue identified in previous works is that, in practice, the target and interfering sources are within the near-field of(More)
We argue that technological advances, changes in financial regulation, and changes in investor composition over the past 30 years have increased the available financial information of small firms and the investor diversity of large firms. This leads us to hypothesize and test for a positive shift in the relation between trading volume reactions to earnings(More)
Prior research suggests that the party with greater power in an exchange relationship can dictate the terms of exchange. In this study, we examine whether supply chain power influences the extent and valuation consequences of real earnings management (REM), a form of earnings manipulation which necessarily involves transactions with one or more parties(More)
An airborne ground looking radar sensor's performance may be enhanced by selecting algorithms adaptively as the environment changes. A short description of an airborne intelligent radar system (AIRS) is presented with a description of the knowledge based filter and detection portions. A second level of artificial intelligence (AI) processing is presented(More)
Range-spread Doppler-spread signals in interference are readily discernable via the application of classical algorithms and architectures presented by Van Trees [1], and more recently by Kay [2] and others. However, when these returns emanate from stationary objects, the Generalized Inner Product (GIP) offers a unique tool for detection and discrimination(More)
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