RLS Adaptive Filtering Algorithms Based on Parallel Computations

The paper presents a family of the sliding window RLS adaptive filtering algorithms with the regularization of adaptive filter correlation matrix. The algorithms are developed in forms, fitted to the implementation by means of parallel computations. The family includes RLS and fast RLS algorithms based on generalized matrix inversion lemma, fast RLS algorithms based on square root free inverse QR decomposition and linearly constrained RLS algorithms. The considered algorithms are mathematically identical to the appropriate algorithms with sequential computations. The computation procedures of the developed algorithms are presented. The results of the algorithm simulation are presented as well

Joint Use of Constant Modulus and Least Squares Criteria in Linearly-Constrained Communication Arrays

This paper considers the application of the linear constraints and RLS inverse QR decomposition in adaptive arrays based on constant modulus criterion. The computational procedures of adaptive algorithms are presented. Linearly constrained least squares adaptive arrays, constant modulus adaptive arrays and linearly constrained constant modulus adaptive arrays are compared via simulation. It is demonstrated, that a constant phase shift in the array output signal, caused by desired signal orientation and array weights, is compensated in a simple way in linearly constrained constant modulus adaptive arrays

Lattice RLS for Nonstationary Signal Processing

The paper presents the modification of lattice RLS adaptive filtering algorithms for the case of nonstationary signal processing. The modification includes the using of sliding window and dynamic regularization in the adaptive filter correlation matrix estimation. The algorithms can be implemented by means of sequential or parallel computations. Based on sequential computations, 30 regularized prewindowed, sliding widow and regularized sliding window lattice RLS algorithms were developed. A family of the same algorithms with parallel computations includes 21 units. Lattice algorithms are developed for the application in single-channel adaptive filters with complex-valued weights. Some computation procedures of the algorithms are listed. The results of the algorithm simulation are also presented

Antenna array calibration algorithm without access to channel signals

This paper describes the algorithm of antenna array (AA) calibration, which estimates and compensates phase lags, caused by non-identical electrical characteristics of array channels. The algorithm does not require the access to the channel signals or the channels disabling. It uses only the array output power measurements under the specific channel phase perturbations. The algorithm accuracy equals the phase shifter quantization step, i.e. it is twice less than phase shifter accuracy itself and does not depend on the number of array channels. The algorithm accuracy is compared with two similar calibration algorithms, known from publications. The compared algorithms accuracy depends on the number of array channels and is much less than the proposed algorithm. Thus, the new algorithm can be widely used for the efficient AA calibration, signal source angular position estimation and tracking by a calibrated or a non-calibrated AA with any aperture shape: linear, flat or conformal, with an arbitrary distance between neighbor AA elements and with an arbitrary antenna selected as a reference one