PREDICTION ERROR AND ADAPTIVE MAXIMUM-LIKELIHOOD PROCESSING
Abstract
Adaptive multichannel prediction-error filtering is compared to conventional optimum Wiener filtering for 10 types of array data. Adaptive maximum-likelihood signal extraction is compared to Wiener filtering for three sets of data; the three sets are composed of actual signal, artificial signal with varying magnitude and velocity, and a composite of noise data. Comparison of the two methods is based on total mean-square-error and the distribution of the error power with frequency. Online adaptive processing will solve problems with slowly time-varying noise fields such as UBO road noise. The adaptive method is also simpler and more economical than the Wiener method as an off-line filter design procedure for array data known to be approximately time stationary. The two methods will produce essentially equivalent filters with respect to total mean-square-error; however, relatively large differences in the actual filter response characteristics are possible.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 28, 1968
- Accession Number
- AD0832007
Entities
People
- Aaron H. Booker
- Ronald J. Holyer
Organizations
- Texas Instruments