Visa Koivunen

Householder Multistage Wiener Filter for Space-Time Navigation Receivers

Space-time (ST) processing in navigation receivers is attractive technology because it provides a sufficient number of degrees of freedom for cancelling a large number of wideband and narrowband jammers simultaneously. Low complexity reduced-rank receiver structures are particularly appealing because the number of filter coefficients can be much larger than the rank of the ST signal model. This paper proposes a new Householder-based multistage Wiener filter (HMSWF) structure for estimating the coefficients of an ST navigation receiver. Both floating point and fixed point arithmetic implementations are considered. The computational complexity of the HMSWF compares favorably with the other MSWF methods reported in literature. Furthermore, the use of Householder transformations at each stage ensures unitary blocking and numerically robust behavior even in finite-precision implementations. Simulations studies verify that the proposed ST navigation receiver based on the HMSWF outperforms other MSWF methods in the antijamming (A,J) task.

Stochastic maximum likelihood method for propagation parameter estimation

We will derive a stochastic maximum likelihood method for estimating spatio-temporal channel parameters. Such estimators are needed in propagation studies where extensive channel measurements and sounding are required. These are seminal tasks in the process of developing advanced channel models. The proposed method employs angular Von Mises distribution model which is appropriate for directional data typically observed in channel measurement campaigns. The signal model is stochastic. The performance of the proposed method is compared to SAGE algorithm where the signal model is deterministic. The computational complexity of the proposed method is lower and channel parameters are estimated with higher fidelity because the underlying distribution model is well-suited for directional data.

Affine equivariant multivariate rank methods

The classical multivariate statistical methods (MANOVA, principal component analysis, multivariate multiple regression, canonical correlation, factor analysis, etc.) assume that the data come from a multivariate normal distribution and the derivations are based on the sample covariance matrix. The conventional sample covariance matrix and consequently the standard multivariate techniques based on it are, however, highly sensitive to outlying observations. In the paper a new, more robust and highly efficient, approach based on an affine equivariant rank covariance matrix is proposed and outlined. Affine equivariant multivariate rank concept is based on the multivariate Oja (Statist. Probab. Lett. 1 (1983) 327) median.

Stochastic Maximum-Likelihood Method for MIMO Propagation Parameter Estimation

In this paper, we derive a stochastic maximum-likelihood (ML) method for estimating spatio-temporal parameters for multiple-input multiple-output (MIMO) channels. Such estimators are needed in propagation studies where extensive channel measurements and sounding are required. These are seminal tasks in the process of developing advanced channel models. The proposed method employs an angular von Mises distribution model which is appropriate for angular data observed in channel measurement campaigns. The signal model is stochastic, and consequentially the method is particularly useful for estimation of the diffuse scattering component. This approach leads to lower complexity and faster convergence in comparison to deterministic models. These benefits are due to lower dimensionality of the model, leading to a simpler optimization problem. The statistical performance of the estimator is studied by establishing the Crameacuter-Rao lower bound (CRLB) and comparing the variances. The simulations show that the variance of the proposed estimation technique reaches the CRLB for relatively small sample size. The estimator is robust in the sense that meaningful results are obtained when applied to data generated by channel models other than the one used in its derivation

Recursive estimation of time-varying channel and frequency offset in MIMO OFDM systems

In this paper we address the problem of channel and frequency offset estimation for multiple-input multiple-output OFDM systems for mobile users. The proposed method stems from extended Kalman filtering. It is suitable for time and frequency selective channels. The algorithm performs channel and offset tracking in time-domain followed by equalization in frequency domain. Simulation results demonstrating high fidelity tracking capability are presented using realistic channel model in typical urban scenarios.

Robust subspace DOA estimation for wireless communications

This paper is concerned with array signal processing in non-Gaussian noise typical in urban and indoor radio channels. Robust and fully nonparametric high resolution algorithms for direction of arrival (DOA) estimation are presented. The algorithms are based on multivariate spatial sign and rank concepts. The performance of the algorithms is studied using simulations. The results show that almost optimal performance is obtained in wide variety of noise conditions.