Multistage Nested Wiener Filter Research Articles

An improved reduced-rank STAP interference suppression method in design of GNSS receivers

In this paper, we address the problem of interference cancellation in global positioning system (GPS) receiver using an adaptive reduced-rank space-time adaptive processing (STAP) interference suppression method based on multistage nested Wiener filter (MSNWF). This improved adaptive reduced-rank method is based on the improved Gerschgorin radii algorithm. First, the cross-correlations between the desired signals of the adjacent stages of MSNWF are directly used to calculate the Gerschgorin radii instead of employing the eigenvalue decomposition of the covariance matrix. Then, a heuristic decision rule based on Gerschgorin disk estimator (GDE) is employed to estimate the rank of interference subspace. Thus optimal rank can be adaptively selected without increasing the computation. Analysis and results of the experiment show that the proposed method further reduces the computation of STAP and enhances its real-time property with the interference suppression performance unaffected.

Adaptive diagonal loading STAP arithmetic based on multistage nested Wiener filter

Adaptive diagonal loading STAP arithmetic based on multistage nested Wiener filter

A Krylov subspace based low-rank channel estimation in OFDM systems

We investigate a low-rank minimum mean-square error (MMSE) channel estimator in orthogonal frequency division multiplexing (OFDM) systems. The proposed estimator is derived by using the multi-stage nested Wiener filter (MSNWF) identified in the literature as a Krylov subspace approach for rank reduction. We describe the low-rank MMSE expressions for exploiting the time correlation function (TCF) of the channel path gains. The Krylov subspace technique requires neither eigenvalue decomposition (EVD) nor the inverse of the covariance matrices for parameter estimation. We show that the Krylov channel estimator can perform as well as the EVD estimator with a much smaller rank. Simulation results obtained confirm the superiority of the proposed Krylov low-rank channel estimator in approaching near full-rank MSE performance.

Reduced-Rank Space-Time Processing for Anti-Jamming GPS Receivers

In a jamming environment with multiple wideband and narrowband jammers, global positioning system (GPS) receivers can use space-time processing to efficiently suppress the jamming. However, the computational complexity of space-time algorithms restricts their application in practical GPS receivers. This paper describes a reduced-rank multi-stage nested Wiener filter (MSNWF) based on subspace decomposition and Wiener filter (WF) to eliminate the effect of jamming in anti-jamming GPS receivers. A general sidelobe canceller (GSC) structure that is equivalent to the MSNWF is used to facilitate calculation of the optimal weights for the space-time processing. Simulation results demonstrate the satisfactory performance of the MSNWF to cancel jamming and the significant reduction in computational complexity by the reduced-rank processing. The technique offers a feasible space-time processing solution for anti-jamming GPS receivers.

Data record-based criteria for the selection of an auxiliary vector estimator of the mmse/mvdr filter

When the auxiliary vector (AV) filter generation algorithm utilizes sample average estimated input data statistics, it provides a sequence of estimates of the ideal minimum mean-square error or minimum-variance distortionless-response filter for the given signal processing/receiver design application. Evidently, early nonasymptotic elements of the sequence offer favorable bias/variance balance characteristics and outperform in mean-square filter estimation error the unbiased sample matrix inversion (SMI) estimator as well as the (constraint) least-mean square, recursive least-squares, "multistage nested Wiener filter", and diagonally-loaded SMI filter estimators. Selecting the most successful (in some appropriate sense) AV filter estimator in the sequence for a given data record is a critical problem that has not been addressed so far. We deal exactly with this problem and we propose two data-driven selection criteria. The first criterion minimizes the cross-validated sample average variance of the AV filter output and can be applied to general filter estimation problems; the second criterion maximizes the estimated J-divergence of the AV filter output conditional distributions and is tailored to binary phase-shift-keying-type detection problems.

Reduced-Rank Chip-Level MMSE Equalization for the 3G CDMA Forward Link with Code-Multiplexed Pilot

This paper deals with synchronous direct-sequence code-division multiple access (CDMA) transmission using orthogonal channel codes in frequency selective multipath, motivated by the forward link in 3G CDMA systems. The chip-level minimum mean square error (MMSE) estimate of the (multiuser) synchronous sum signal transmitted by the base, followed by a correlate and sum, has been shown to perform very well in saturated systems compared to a Rake receiver. In this paper, we present the reduced-rank, chip-level MMSE estimation based on the multistage nested Wiener filter (MSNWF). We show that, for the case of a known channel, only a small number of stages of the MSNWF is needed to achieve near full-rank MSE performance over a practical single-to-noise ratio (SNR) range. This holds true even for an edge-of-cell scenario, where two base stations are contributing near equal-power signals, as well as for the single base station case. We then utilize the code-multiplexed pilot channel to train the MSNWF coefficients and show that adaptive MSNWF operating in a very low rank subspace performs slightly better than full-rank recursive least square (RLS) and significantly better than least mean square (LMS). An important advantage of the MSNWF is that it can be implemented in a lattice structure, which involves significantly less computation than RLS. We also present structured MMSE equalizers that exploit the estimate of the multipath arrival times and the underlying channel structure to project the data vector onto a much lower dimensional subspace. Specifically, due to the sparseness of high-speed CDMA multipath channels, the channel vector lies in the subspace spanned by a small number of columns of the pulse shaping filter convolution matrix. We demonstrate that the performance of these structured low-rank equalizers is much superior to unstructured equalizers in terms of convergence speed and error rates.