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Most radar systems employ a feed-forward processing chain in which they first perform some low-level processing of received sensor data to obtain target detections and then pass the processed data on to some higher-level processor such as a tracker, which extracts information to achieve a system objective. System performance can be improved using adaptation between the information extracted from the sensor/processor and the design and transmission of subsequent illuminating waveforms. As such, cognitive radar systems offer much promise. In this paper, we develop a general cognitive radar framework for a radar system engaged in target tracking. The model includes the higher-level tracking processor and specifies the feedback mechanism and optimization criterion used to obtain the next set of sensor data. Both target detection (track initiation/termination) and tracking (state estimation) are addressed. By separating the general principles from the specific application and implementation details, our formulation provides a flexible framework applicable to the general tracking problem. We demonstrate how the general framework may be specialized for a particular problem using a distributed sensor model in which system resources (observation time on each sensor) are allocated to optimize tracking performance. The cognitive radar system is shown to offer significant performance gains over a standard feed-forward system.

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Can Australian equity returns be modelled by 'home-grown' factors? We examine the indigenous capital asset pricing model, the indigenous Fama-French three-factor model, and extensions to the latter, and find them all wanting. We find evidence of domestic market segmentation in Australia. For the smallest firms, all the models we study fail. For the largest Australian firms, we find that the US Fama-French three factors (downloaded from French's website: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ ) provide a successful model of Australian returns. It is as if the largest firms in the Australian market are simply part of the larger US market. Copyright (c) The Authors Journal compilation (c) 2006 AFAANZ.

Our note examines the momentum effect in Australia using the J-month/K-month methodology of Jegadeesh and Titman (1993, 2001). Our sample consists of stocks listed on the Australian stock exchange from January 1980 to December 2001. We do not find evidence for a momentum effect in Australia during this period. Rather, we find evidence of significantly positive returns for ‘loser’ portfolios in July-the first month of the Australian financial year.