Cagatay Candan

Covariance matrix estimation of compound-Gaussian vectors with texture-correlation (spatial correlation for the adaptive radar detectors) is examined. The texture parameters are treated as hidden random parameters whose statistical description is given by a Markov chain. States of the chain represent the value of texture coefficient and the transition probabilities establish the correlation in the texture sequence. An Expectation-Maximization (EM) method based covariance matrix estimation solution is given for both noiseless and noisy snapshots. An extension to the practically important case of persymmetric covariance matrices is developed and possible extensions to other structured covariance matrices are described. The numerical results indicate that the benefit of utilizing spatial correlation in the covariance matrix estimation can be significant especially when the total number of snapshots in the secondary data is small. From applications viewpoint, the suggested model is well suited for the adaptive target detection in sea-clutter where some spatial correlation between range cells has been experimentally observed. The performance improvements of the suggested approach for small number of snapshots can be particularly important in this application area.

Dirac delta functions are introduced to students of signal processing in their sophomore year. Quite understandably, Dirac delta functions, which should be more aptly called generalized functions or distributions, cannot be comprehensively given to a young audience at the beginning of their engineering education. Instead, a simplified and abridged definition is presented, and the implications of the definition in signal processing problems are illustrated through numerous examples, following the footsteps of Oppenheim et al. [1], [2].

Conventional Mills Cross architecture suffers from poor direction-of-arrival (DOA) angle estimation accuracy in the dimension that the transmitter is aligned. To improve the estimation accuracy, a space-time coded, multiple-input multipleoutput (MIMO) direction finding method with complementary codes is presented. The performance of the suggested MIMO Mills Cross architecture has been evaluated by underwater field experiments. Field experiments confirm the feasibility of the proposed approach and illustrate the performance gains. Since the proposed approach does not require any changes in the conventional Mills Cross hardware, the performance improvement can also be realized by a software reconfiguration for existing legacy systems.