Array covariance error measurement in adaptive source parameter estimation
- 2 January 2003
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
The small error approximation is used to derive a linear relationship between the source parameters (i.e. power levels and directions of arrival) and a measurement of the covariance error matrix, defined as the difference between a nonparametric consistent estimate of the spectral density matrix and a covariance model from the scenario parameters. The resulting framework allows the design of a Kalman-like algorithm which provides a simultaneous and adaptive estimation of the source parameter, no matter what the source waveform or modulation. Good performance is expected, mainly in the presence of sensors malfunctioning, low signal-to-noise ratio, etc.Keywords
This publication has 4 references indexed in Scilit:
- Maximum likelihood bearing estimation by quasi-Newton method using a uniform linear arrayPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- Exact maximum likelihood parameter estimation of superimposed exponential signals in noiseIEEE Transactions on Acoustics, Speech, and Signal Processing, 1986
- Optimum localization of multiple sources by passive arraysIEEE Transactions on Acoustics, Speech, and Signal Processing, 1983
- Some new signal processors for arrays of sensorsIEEE Transactions on Information Theory, 1980