Highlights of Statistical Signal and Array Processing

Many engineering applications require extraction of a signal or parameter of interest from degraded measurements. To accomplish this it is often useful to deploy ne grained statistical models; diverse sensors which acquire extra spatial, temporal, or polarization information; or multidimensional signal representations, e.g. time-frequency or time scale. When applied in combination these approaches can be used to develop highly sensitive signal estimation, detection, or tracking algorithms which can exploit small but persistent di erences between signals, interferences, and noise. Conversely, these approaches can be used to develop algorithms to identify a channel or system producing a signal in additive noise and interference, even when the channel input is unknown but has known statistical properties.