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The subject of this communication is the compensation of nonlinearities in digital radio links, where the major source of nonlinearity is caused by the High Power Amplifier (HPA), typically working close to its saturation point because of energy constraints. This paper deals with the design of CMAC-based predistorters for application in digital transmission over nonlinear channels with memory. A novel...
In many signal processing applications, one has to solve an overdetermined system of linear equations Ax ≈ b, while minimizing the errors on A and b. The Total Least Squares (TLS) method calculates corrections ΔA and Δb such that (A + ΔA)x = b + Δb and ||[ΔA Δb]||F is minimal. The resulting parameter vector x is ä Maximum Likelihood (ML) estimate when the noise on the different entries of [A b] is...
In this contribution a new robust technique for adjusting the step size of the Least Mean Squares (LMS) adaptive algorithm is introduced. The proposed method exhibits faster convergence, enhanced tracking ability and lower steady state excess error compared to the fixed step size LMS and other previously developed variable step size algorithms, while retaining much of the LMS computational simplicity...
A hyperstable ALE for tracking complex cisoids is presented. The ALE incorporates an adaptive IIR filter, with the convergence of the filter being conditional on the overall system being ‘passive’. The passivity of the system depends on the location of the input cisoid frequencies, and it is shown that for the case of upto two cisoids, the system is passive for all distinct frequencies. For the case...
This paper lies in the lineage of recent works studying the asymptotic behaviour of robust-scatter estimators in the case where the number of observations and the dimension of the population covariance matrix grow at infinity with the same pace. In particular, we analyze the fluctuations of bilinear forms of the robust shrinkage estimator of covariance matrix. We show that this result can be leveraged...
This paper presents an investigation into the detection and classification of drum sounds in polyphonic music and drum loops using non-negative matrix deconvolution (NMD) and the Itakura Saito divergence. The Itakura Saito divergence has recently been proposed as especially appropriate for decomposing audio spectra due to the fact that it is scale invariant, but it has not yet been widely adopted...
In this paper, we analyze a new class of iterative re-weighted least squares (IRLS) algorithms and their effectiveness in signal recovery from incomplete and inaccurate linear measurements. These methods can be interpreted as the constrained maximum likelihood estimation under a two-state Gaussian scale mixture assumption on the signal. We show that this class of algorithms, which performs exact recovery...
The approximation of linear time-invariant (LTI) systems by sampling series is an important topic in signal processing. However, the convergence of the approximation series is not guaranteed: there exist stable LTI systems and bandlimited input signals such that the approximation series diverges, regardless of the oversampling factor and the sampling pattern. Recently, it has been shown that this...
This paper describes a new strategy for the blind equalization so that the blind Constant Module Algorithm (CMA) can be smoothly switched to the decision- directed (DD) equalization. First, we propose a combination approach by running the CMA and DD equalization simultaneously to obtain a smooth switch between them. We then describe an “anchoring process” to eliminate the effect from the CMA at the...
In some acoustic echo cancellation scenarios, such as an automatic gain adjustment application, near-end noise may be continuously present. In this case a double-talk detector cannot be applied and the adaptive algorithm should behave in a robust way w.r.t. the disturbing near-end signal. From linear estimation theory it is known that the variance of the room impulse response estimate may be decreased...
A new frequency-domain direct adaptive approach is proposed for general multichannel active noise control (ANC) when both of the primary and secondary path channels are uncertain and changeable. To reduce the cancelling errors, two kinds of virtual error vectors are introduced and are forced to zero by adjusting three adaptive FIR filter matrices in an online manner, by which the convergence of the...
In this paper we propose a novel adaptive filtering algorithm. Using the Set Theoretic Estimation framework, the algorithm exploits the information given by the power spectral density of the noise extracted from the periodogram of filtering error. With this information new appropriate sets are built and projections onto them are computed. The simulations results show that the algorithm has excellent...
This paper presents a novel stereophonic acoustic echo canceling scheme. The proposed scheme is based on the ideas of “simultaneous use of two different states of inputs [Yukawa & Yamada, IEICE 2004]” and “an accelerating weight technique named POWER [Yukawa & Yamada, EUSIPCO 2004]”. The two states generate two solution sets, of which the intersection is expected to be fairly small and to...
An extension of Independent Component Analysis (ICA) to the situation when the mixture of signals is contaminated by multiplicative noise is proposed in this paper. The ICA methods search for the most independent output after a linear transformation of the data vector. If the ICA model is followed by these data, the result of this search is the inverse of the unknown mixture. On the other hand, if...
The class of LMS algorithms employing a general error nonlinearity is considered. The calculus of variations is employed to obtain the optimum error nonlinearity for an independent and identically distributed input. The nonlinearity represents a unifying view of error nonlinearities in LMS adaptation. In particular, it subsumes two recently developed optimum nonlinearities for arbitrary and Gaussian...
A problem of dynamical identification of inputs of systems described by ordinary differential equations is considered. Solving algorithms based on the method of control of a model are suggested.
This paper faces the state estimation problem for a nonlinear system, using an Extended Kalman Filter (EKF) that receives measurements taken by a set of wireless sensors.
An arbitrary-order finite-time-convergent exact robust differentiator is constructed based on higher-order sliding mode (HOSM) technique. Being used in a feedback together with previously proposed HOSM controllers, it produces a universal controller, formulated in input-output terms only, which causes the output of any uncertain smooth SISO minimum-phase dynamic system with known relative degree to...
We consider the problem of estimating the position of an object moving in the space by observing its image with the aid of a CCD camera. The problem can be converted into the observation of a dynamical system with nonlinearities. A new method is proposed to identify the obtained dynamical system. The attraction of the new method is that the algorithm is very simple and easy to be implemented, and...
We present a novel class of adaptive algorithms for the blind separation of non-Gaussian mutually independent source signals that can be modeled as independent identically distributed (i.i.d.) discrete random processes. The signals are assumed to be transmitted through a mxp narrow-band (instantaneous linear mixture) channel. The original algorithm, called the Multi-User Kur-tosis (MUK) algorithm...
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