Detection and estimation theory pdf
Underwater Acoustics and Signal Processing pp Cite as. This paper has two aspects: one is tutorial in nature and its objective is to present in a concise way the fundamental ideas of detection and estimation theory which are necessary to easily undestand the matter presented in the following part; the second is more a presentation of new material in the field of adaptive detection, and particularly of signal detection in noise with fluctuating power. In 1 was discussed the concept of optimality for an adaptive detection system and particularly its application to the detection of a deterministic signal in spherically invariant noise. In 2 the concept of Noise Alone Reference NAE already used in spatial signal processing was introduced in order to present a geometrical interpretation of the classical matched filter using a phase of estimation. Moreover some adaptive detectors were suggested without effective calculation or simulations concerning their performances.
Journal of Statistical Physics. A review.
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Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data. An estimator attempts to approximate the unknown parameters using the measurements. In estimation theory, two approaches are generally considered. For example, it is desired to estimate the proportion of a population of voters who will vote for a particular candidate. That proportion is the parameter sought; the estimate is based on a small random sample of voters.