Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Matrix computations (3rd ed.)
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Maximum likelihood array processing in spatially correlated noisefields using parameterized signals
IEEE Transactions on Signal Processing
Joint estimation of time delays and directions of arrival ofmultiple reflections of a known signal
IEEE Transactions on Signal Processing
Joint angle and delay estimation using shift-invariance techniques
IEEE Transactions on Signal Processing
Estimation of multipath parameters in wireless communications
IEEE Transactions on Signal Processing
EURASIP Journal on Advances in Signal Processing
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The estimation of the delays and angles of arrival of several superimposed signal replicas requires a high computational burden, which is reduced in practice by employing sub-optimal estimators or by exploiting a specific structure of the problem. In this paper, we propose to reduce the computational burden by looking beforehand for a data representation of small size, which is obtained from an a priori distribution of the parameters. This distribution, being different to the distribution of the parameters in the estimation problem itself, summarises the information about their range of variation. The data reduction can be regarded as the result of applying a Karhunen-Loève expansion.Focusing on the delay parameterisation, we show how this data reduction can be performed efficiently. We present the adaptation of the TLS-ESPRIT algorithm for delay estimation, and of the deterministic Maximum Likelihood estimator to this data reduction. In order to calculate the latter estimator, we discuss the application of Newton-type methods.