Near-optimal smoothing using a maximum entropy criterion
- Univ. of Delaware, Newark, DE (United States)
Digital filters have become very common for smoothing and filtering of noisy chemical data. Many papers have appeared on the use of filters for optimizing signal-to-noise or for optimizing signal shape retention during the process of noise removal. In contrast, little work has been aimed at optimizing the filter to retain as much of the information present in the data while removing as much of the noise as possible. This paper reports a simple approach for optimizing digital filters of several types. The optimization criterion used here is based on the informational entropy of the smoothed signal. It is demonstrated here that, for many forms of data, a filter optimized by maximizing the informational entropy, but without knowledge of the true signal shape, performs as well as an equivalent, optimal filter optimized with the aid of prior knowledge of the signal shape. Filters optimized maximizing the informational entropy have applications where accurate reconstruction must be accompanied by significant increases in signal-to-noise ratios of noisy, but unknown data. The maximum entropy optimization process is demonstrated on several different digital filters. 34 refs., 3 figs., 5 tabs.
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- FG02-86ER13542
- OSTI ID:
- 513390
- Journal Information:
- Analytical Chemistry (Washington), Journal Name: Analytical Chemistry (Washington) Journal Issue: 18 Vol. 64; ISSN ANCHAM; ISSN 0003-2700
- Country of Publication:
- United States
- Language:
- English
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