Fundamentals Of Statistical Signal Processing, ...

Fundamentals of Statistical Signal Processing, ...

We have reviewed the fundamentals of discrete-time signals and systems, including the definitions, terminologies, transformation tools, and parametric models. We then reviewed random variables and random processes, including statistical characterization, second-order and higher-order statistics for both real and complex cases. Finally, estimation theory was introduced that includes the problem, properties of estimators and several representatives of estimation methods.

A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. KEY TOPICS: Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes numerous examples as well as applications to real- world problems. MARKETS: For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals -- radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc.

• Choose appropriate statistical tools to solve signal processing problems;

• Analyze real data using a mathematical computing language;

• Interpret spectral content of signals;

• Develop appropriate models for observed signals;

• Assess / Evaluate advantages and limitations of different statistical tools for a given signal processing problem;

• Implement numerical methods for processing signals.

Eventually I'd take more advanced books to have solid understanding of DSP, including statistical signal analysis such as power spectra, cross-spectra, coherence, autocorrelation, and cross-correlation and ultimately I'd be interested in signal, sound, noise, or speech detection.

1990 SPIE. All rights reserved. Target detection in clutter depends sensitively on the spatial structure of the latter. In particular, it is the ratio of the target size to the clutter inhomogeneity scale which is of crucial importance. Indeed, looking for the leopard in the background of leopard skin is a difficult task. Hence quantifying thermal clutter is essential to the development of successful detection algorithms and signature analysis. This paper describes an attempt at clutter characterization along with several applications using calibrated thermal imagery collected by the Keweenaw Research Center. The key idea is to combine spatial and intensity statistics of the clutter into one number in order to characterize intensity variations over the length scale imposed by the target. Furthermore, when properly normalized, this parameter appears independent of temporal meteorological variation, thereby constituting a background scene invariant. This measure has a basis in analysis of variance and is related to digital signal processing fundamentals. Statistical analysis of thermal images is presented with promising results.