The recently proposed spatial filtering, which is developed to effectively decorrelate coherent signals, is analyzed. It has been shown that any set of distinct preliminary estimates of directions of arrival (DOA'...
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The recently proposed spatial filtering, which is developed to effectively decorrelate coherent signals, is analyzed. It has been shown that any set of distinct preliminary estimates of directions of arrival (DOA's) can be used to obtain a full rank source covariance matrix. In addition, a particular signal enhancement approach is developed to minimize the effects of the sensor noise. Statistical analysis of the spatial filtering and its enhanced version are also studied using the Monte Carlo method.
An efficient approach for the computation of the optimum convergence factor for the LMS/Newton algorithm applied to a transversal FIR structure is proposed. The approach leads to a variable step size algorithm that re...
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An efficient approach for the computation of the optimum convergence factor for the LMS/Newton algorithm applied to a transversal FIR structure is proposed. The approach leads to a variable step size algorithm that results in a dramatic reduction in convergence time. The algorithm is evaluated in system identification applications where two alternative implementations of the adaptive filter are considered: the conventional transversal FIR realization and adaptive filtering in subbands.
In order to address the performance degradation of the geometric algebra LMS algorithms (GA-LMS) in the face of non-Gaussian disturbances, this brief proposes a robust adaptive filtering algorithm based on hybrid maxi...
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In order to address the performance degradation of the geometric algebra LMS algorithms (GA-LMS) in the face of non-Gaussian disturbances, this brief proposes a robust adaptive filtering algorithm based on hybrid maximum geometric algebra correntropy (HMGACC) perspective. Specially, we define the hybrid geometric algebra correntropy (HGAC) cost function, which can increase the flexibility of the correntropy. Secondly, we introduce a proportionality parameter to make the HGAC algorithm more general, which can be changed into a traditional single kernel GAC algorithm by changing the proportionality parameter. Furthermore, the addition of the proportionality parameter also allows for a non-fixed form of step-size, i.e., the step-size of the proposed algorithm in this brief is a hybrid step-size, which will be more useful for us to improve the performance of the algorithm. Finally, in the experimental simulations, the proposed algorithm can achieve better convergence and steady-state performance in comparison with other competing algorithms under different non-Gaussian disturbances, which confirms the feasibility of the proposed method.
In this correspondence, a modified Kalman filtering algorithm is described. The key point of the new algorithm is a model mismatch function, which accounts for deviation of the model from the ideal condition of orthog...
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In this correspondence, a modified Kalman filtering algorithm is described. The key point of the new algorithm is a model mismatch function, which accounts for deviation of the model from the ideal condition of orthogonality between the innovations process and past observations.
Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that ...
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Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At ***, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.
Possible convergence points of the generalized least squares adaptive notch filtering algorithm are analytically derived for the multiple sinusoid case, showing explicitly the dependence of the asymptotic bias on the ...
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Possible convergence points of the generalized least squares adaptive notch filtering algorithm are analytically derived for the multiple sinusoid case, showing explicitly the dependence of the asymptotic bias on the pole contraction factor, signal-to-noise ratio and the true model parameters. The results for symmetric and nonsymmetric parameterization are compared.
The convergence of an adaptive filtering vector is studied, when it is governed by the mean-square-error gradient algorithm with constant step size. We consider the mean-square deviation between the optimal filter and...
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The convergence of an adaptive filtering vector is studied, when it is governed by the mean-square-error gradient algorithm with constant step size. We consider the mean-square deviation between the optimal filter and the actual one during the steady state. This quantity is known to be essentially proportional to the step size of the algorithm. However, previous analyses were either heuristic, or based upon the assumption that successive observations were independent, which is far from being realistic. Actually, in most applications, two successive observation vectors share a large number of components and thus they are strongly correlated. In this work, we deal with the case of correlated observations and prove that the mean-square deviation is actually of the same order (or less) than the step size of the algorithm. This result is proved without any boundedness or barrier assumption for the algorithm, as it has been done previously in the literature to ensure the nondivergence. Our assumptions are reduced to the finite strong-memory assumption and the finite-moments assumption for the observation. They are satisfied in a very wide class of practical applications.
A new FIR adaptive filtering algorithm, using quantized gradients with a variable convergence factor that minimizes an exponentially weighted sum square error, is proposed. The proposed algorithm converges about as fa...
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A new FIR adaptive filtering algorithm, using quantized gradients with a variable convergence factor that minimizes an exponentially weighted sum square error, is proposed. The proposed algorithm converges about as fast as the optimum block adaptive shifting algorithm does. However, when effective reusing data length due to the exponential weighting is a power-of-two number, the proposed algorithm requires a much smaller number of multiplications and divisions.
The use of UD factorization in adaptive RLS algorithms is interesting for its numeric robustness and because no square-root operations at all are involved. In this correspondence, we describe a square root free fast R...
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The use of UD factorization in adaptive RLS algorithms is interesting for its numeric robustness and because no square-root operations at all are involved. In this correspondence, we describe a square root free fast RLS algorithm based on the UD factorization of the autocorrelation matrix. Numerous finite precision simulations tend to indicate that this algorithm is numerically stable. The algorithm requires O(N) operations, where N is the linear filter order.
An algorithm for efficiently adjusting the coefficients of equation-error infinite impluse response (HR) adaptive filters is described. Unlike the RLS algorithm, the proposed algorithm yields unbiased filter coefficie...
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An algorithm for efficiently adjusting the coefficients of equation-error infinite impluse response (HR) adaptive filters is described. Unlike the RLS algorithm, the proposed algorithm yields unbiased filter coefficients. Simulations involving the identification of unknown pole-zero systems demonstrate the algorithm's improved performance over the equation-error RLS algorithm.
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