Chong Kwan Un

An adaptive array robust to beam pointing error

The authors propose an algorithm that is robust to beam pointing error. In the algorithm, the weight vector of a beam steered adaptive array corresponds to the maximum eigenvector of a data covariance matrix which is filtered by the projection operator composed of the eigenvectors of noise subspace of the covariance matrix in which the desired signal is removed by subtractive preprocessing. The resulting adaptive array becomes robust to beam pointing error. >

A Sequential Estimation Approach for Performance Improvement of Eigenstructure-Based Methods in Array Processing

In this correspondence, we present a simple and noniter- ative approach that lowers noticeably the threshold signal-to-noise ra- tio (SNR) of the eigenstructure-based techniques for estimating the di- rections of arrival (DOAs) of multiple narrow-band sources in passive sensor arrays. This approach removes effectively the spatial interfer- ences among sources through the sequential estimation of the DOAs. We develop a sequential multiple signal classification (MUSIC) algo- rithm by applying the approach to the MUSIC algorithm, although it can be equally well applicable to other eigenstructure-based methods. We also present a recursive computational procedure (RCP) that re- duces significantly the computational complexity of the proposed al- gorithm by transforming the computation of Hermitian forms into that of only inner products of vectors. Computer simulation results that demonstrate the resolution performance of the proposed algorithm are included. Since this algorithm is simple and noniterative, and also pro- vides high resolution, it may be used on its own, or be used to provide good initial estimates for more complex iterative algorithms.

Robust adaptive beamforming based on the eigenstructure method

In a linearly constrained beamformer with imperfect arrays, the authors investigate the cause of cancelling the desired signal from a viewpoint based on the eigenstructure properties of the array covariance matrix. Based on this cause, they propose a robust beamforming algorithm. As an adaptive algorithm of the proposed beamformer, the present a data-domain signal subspace updating algorithm. >

Performance of constant modulus adaptive digital filters for interference cancellation

Abstract The constant modulus algorithm (CMA) updates its weight vector to minimize the modulus variation of the output signal. In this paper, the convergence behavior of the CMA used in interference cancellation application is studied. We first investigate the optimum weight vector that minimizes the performance index of the CMA which is defined as the mean-squared difference between the estimated and true moduli. We then analyze the convergence behavior of the squared output modulus and the performance index. Based on these analysis results, several convergence properties of the CMA are discussed.

Simple computational methods of the AP algorithm for maximum likelihood localization of multiple radiating sources

Two computational algorithms that reduce significantly the computational complexity per iteration of the alternating projection (AP) algorithm are presented. One is a recursive projection algorithm that utilizes the projection matrix updating formula, and the other is a maximum eigenvector approximation algorithm that approximates the Hermitian maximization problem in every iteration as the problem of maximizing the modulus of the projection onto the maximum eigenvector subspace. By transforming the computation of Hermitian forms into that of only inner products of vectors, these algorithms significantly reduce the computational complexity without noticeable loss in the estimation performance and convergence behavior. Computer simulation results that validate this approximation are included. >

Block conjugate gradient algorithms for adaptive filtering

Abstract Two adaptive algorithms based on the conjugate gradient method are presented for finite impulse response (FIR) block adaptive filters. First, the block conjugate gradient (BCG) algorithm is derived from minimization of an estimate of the block mean-square error (BMSE). Using the fast convolution technique, the BCG algorithm is then extended to the frequency-domain BCG (FBCG) algorithm that yields significant computational savings over the BCG algorithm, especially for a large filter-tap order. Through computer simulations, it is shown that although the adaptation accuracy of the BCG or the FBCG is nearly equal to that of the optimum block adaptive (OBA) algorithm, its convergence property is superior to that of the OBA algorithm under any input conditions. Moreover, it is also shown that their convergence rate is as fast as the recursive least-squares (RLS) algorithms for a relatively small eigenvalue spread.

An optimum block adaptive algorithm based on the nested iteration technique

Abstract As a version of the optimum block adaptive (OBA) algorithm, a nested optimum block adaptive (NOBA) algorithm is proposed for finite impulse response (FIR) block adaptive filters. In this paper, we introduce a new updating procedure called the nested iteration technique that updates the filter tap weights several times rather than only once for each data block, as in the OBA algorithm. Thus; the proposed algorithm achieves faster convergence speed although its computational load is higher than the OBA algorithm. The NOBA algorithm is formulated by minimizing an estimate of the block mean-square error (BMSE) as an objective function. Through computer simulations, it is shown that the proposed algorithm is superior to the normalized least mean-square (NLMS) algorithm in convergence rate regardless of stationarity, whereas the OBA algorithm is inferior to the NLMS algorithm. It is also shown that the tracking property of the NOBA algorithm is better than that of the OBA algorithm, and it is almost comparable to that of the NLMS algorithm.

Recursive modified Gram-Schmidt algorithm for linear phase filtering

Abstract In this paper, we present a recursive modified Gram-Schmidt (RMGS) algorithm for least-squares (LS) linear phase filters to allow for the tracking of time-varying parameters. We examine both exponentially windowed and sliding window covariance cases, including the prewindowed case as a special case of the exponentially windowed one. We describe quantitatively the performance characteristics of the RMGS filters for the problem of linear phase system identification when the unknown system parameters vary with time.

Parallel modified spatial smoothing algorithm for coherent interference cancellation

Abstract In array processing, the spatial smoothing technique and its variations are known to be effective in combatting coherent interferences. However, they are disadvantageous in that they either reduce the effective array aperture or require the formation of covariance matrices which causes numerical difficulties when finite-precision computations are involved and given array data are ill-conditioned. In this paper, we present a data-domain spatial preprocessing algorithm, by which the effective array aperture is expanded without forming covariance matrices. Also, we propose a parallel spatial smoothing technique in which spatial subarray data are rearranged before processing. The incorporation of the data-domain spatial preprocessing algorithm and the parallel spatial smoothing technique is simple, and constitutes a parallel modified spatial smoothing technique which is a parallel implementation method of the modified spatial smoothing technique. The proposed parallel modified spatial smoothing method is highly fast, numerically stable, and capable of nulling out coherent interferences. Since the proposed method can readily be combined with least-squares solving systems using orthogonal transformations, one can take full advantages of systolic/wavefront arrays to get high throughput.

An optimum block adaptive shifting algorithm using the Toeplitz preconditioner

Abstract We present a new block adaptive algorithm as a variant of the Toeplitz-preconditioned optimum block adaptive (TOBA) algorithm. The proposed algorithm is formulated by combining the TOBA algorithm with a data-reusing scheme that is realized by processing blocks of data in an overlapping manner, as in the optimum block adaptive shifting (OBAS) algorithm. Simulation results show that the proposed algorithm is superior to the OBAS and TOBA algorithms in both convergence rate and tracking property of input signal conditioning.