Adaptive filter theory
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Cramer-Rao bounds for deterministic signals in additive and multiplicative noise
Signal Processing - Special issue on higher order statistics
Maximum-likelihood array processing in non-Gaussian noise with Gaussian mixtures
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
OFDM channel estimation in the presence of interference
IEEE Transactions on Signal Processing
Space-alternating generalized expectation-maximization algorithm
IEEE Transactions on Signal Processing
An adaptive spatial diversity receiver for non-Gaussianinterference and noise
IEEE Transactions on Signal Processing
Maximum likelihood estimation for multivariate observations of Markov sources
IEEE Transactions on Information Theory
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
Gradient-based manipulation of nonparametric entropy estimates
IEEE Transactions on Neural Networks
Optimal noise benefits in Neyman-Pearson and inequality-constrained statistical signal detection
IEEE Transactions on Signal Processing
Noise enhanced hypothesis-testing in the restricted Bayesian framework
IEEE Transactions on Signal Processing
Optimal stochastic signaling for power-constrained binary communications systems
IEEE Transactions on Wireless Communications
IEEE Transactions on Communications
Stochastic signaling in the presence of channel state information uncertainty
Digital Signal Processing
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Extensive work to develop and optimise signal processing for signals that are corrupted by additive Gaussian noise has been done so far mainly because of the central limit theorem and the ease in analytic manipulations. It has been observed that the algorithms designed for Gaussian noise typically perform poor in presence of Gaussian mixture (non-Gaussian) noise. This paper discusses a likelihood based algorithm using kernel density estimates to improve channel estimation over a block in non-Gaussian noise environments. The likelihood pdf is assumed unknown and is estimated by using kernel density estimator at the receiver. A novel technique for channel estimation using a whitening filter for interference limited channels is also proposed in this paper. The performance of the proposed estimator is compared with the Cramer Rao lower bound for associated noise distribution. The simulations for impulsive noise and co-channel interference in presence of Gaussian noise, confirms that a better estimate can be obtained by using the proposed technique as compared to the traditional least-squares-based algorithms in highly non-Gaussian environments.