Highlights of Statistical Signal and Array

1 Background and Overview (section written by Alfred Hero) 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 multi-dimensional 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 diierences 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. Broadly stated, the Statistical Signal and Array Processing (SSAP) area is concerned with reliable estimation, detection and classiication of signals which are subject to random uctuations. Opening a recent issue of the IEEE Transactions on Signal Processing to a SSAP paper the reader will probably see one or more of the following: (1) description of a mathematical and statistical model for measured data, including models for sensor, signal, and noise; (2) careful statistical analysis of the fundamental limitations of the data including deriving benchmarks on performance, e.g. the Cram er-Rao, Ziv-Zakai, Barankin, Rate Distortion, Chernov, or other lower bounds on average estimator/detector error; (3) development of mathematically optimal or suboptimal esti-mation/detection algorithms; (4) asymptotic analysis of error performance establishing that the proposed algorithm comes close to reaching a benchmark derived in (2); (5) simulations or experiments which compare algorithm performance to the lower bound and to other competing algorithms. Depending on the speciic application, a SSAP algorithm may also have to be adaptive to changing signal and noise environments, This requires incorporating exible statistical models, implementing low-complexity real-time estimation and ltering algorithms, and on-line performance monitoring. Until recently the statistical signal and array processing area was covered by the SSAP Technical Committee which grew out of the Spectrum Estimation and Modeling Technical Committee (discontinued in 1991). At ICASSP-98 in Seattle, an administrative restructuring took place which eliminated the SSAP, Digital Signal Processing (DSP), and Underwater Acoustics Signal Processing (UASP) Technical Committees, replacing them by three new committees: Signal Processing The