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In this paper, the problem of estimating complex-valued proportional covariance matrices is addressed. The obtained estimate generalizes the fixed point estimate to scaled packets of data, and is hence called the generalized fixed point estimate (GFPE). The statistical properties (bias, consistency, and asymptotical distributions) of the estimate are presented, and verified through simulations. As an example of application, a radar detection problem is considered. The GFPE is used in the well-known adaptive normalized matched filter (ANMF), and the obtained empirical probability of false alarms threshold curve is compared to the theoretical one. The ANMF is also used to compare the GFPE to the fixed point estimate. [ABSTRACT FROM PUBLISHER]

When using multichannel synthetic aperture radar (MSAR) to perform moving target detection, the coherence between the received signals is closely linked to the detection capacities of the system. In airborne MSAR context, because of the platform movement, the antenna patterns, as well as the phase shifts between the channels, evolve in different ways during the integration time. In this paper, these dissimilarities are shown to cause a degradation of coherence, and hence of detection capacities. A method to enhance the coherence is then developed. This method is based on the estimation of the steering vector. It is applied on two sets of real data, and the obtained results validate the method. [ABSTRACT FROM PUBLISHER]