In an earlier work [1], we used transform methods from the theory of random matrices to analytically compute the asymptotic eigendistribution of the error covariance matrix of the single-measurement rls filter. When w...
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ISBN:
(纸本)9781424423538
In an earlier work [1], we used transform methods from the theory of random matrices to analytically compute the asymptotic eigendistribution of the error covariance matrix of the single-measurement rls filter. When we have a multiplicity of measurements, as happens in extended rls filtering, the analysis is much more complicated. In this paper we study the multiple measurement case and obtain a system of two Coupled equations for the Stieltjes transform of the asymptotic eigendistribution. Numerical solutions of this system very well predict the actual asymptotic eigendistribution for systems with as low as n = 10 - 20 state dimensions.
In recent years, the transmission of acoustic signal underwater is a challenging task. Acoustic is the most preferred signal at low frequency because of low absorption characteristics. Acoustic signals are affected by...
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ISBN:
(纸本)9781467367929
In recent years, the transmission of acoustic signal underwater is a challenging task. Acoustic is the most preferred signal at low frequency because of low absorption characteristics. Acoustic signals are affected by the different background noise which is produced by the breaking waves, rainfall, marine life and flowing wind. Underwater signal transmission is highly affected by wind noise which is predominant at low frequency. To eliminate such type of noises different adaptive filter algorithm are used. In this paper rls algorithm is proposed to improve the SNR of acoustic signal. The SNR calculated for different speed samples of wind shows an average increase of 15 dB as compare to LMS. The proposed method shows that rls algorithm performs better as compared to LMS for the calculation for SNR of the underwater acoustic signal.
The smart antennas are widely used for wireless communication because it has an ability to increase the coverage capacity of a communication system. The main purpose of the smart antenna system is the selection of sma...
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ISBN:
(纸本)9781467371162
The smart antennas are widely used for wireless communication because it has an ability to increase the coverage capacity of a communication system. The main purpose of the smart antenna system is the selection of smart algorithms for adaptive array. On this paper, one of the adaptive beam forming approach used in smart antennas called Recursive Least Square algorithm (rls) is presented. The efficiency of rls algorithm is compared on the basis of normalized array factor and mean square error (MSE) for mobile communication. The performance of rls algorithm is analyzed by using MATLAB software and all the simulated results are presented on this paper.
This paper proposes a simple and efficient method for channel equalization of MIMO-OFDM system with channel estimation. Channel equalization of multiple inputs multiple outputs orthogonal frequency division multiplexi...
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ISBN:
(纸本)9781424413119
This paper proposes a simple and efficient method for channel equalization of MIMO-OFDM system with channel estimation. Channel equalization of multiple inputs multiple outputs orthogonal frequency division multiplexing (MIMO-OFDM) for Rayleigh fading and AWGN channel is simulated. Temporal variations in channel are due to the Doppler spread, a sign of relative motion between transmitter and receiver. This paper evaluates performance of a MIMO OFDM system with different number of transmit and receive antennas, using different modulation schemes and at different values of signal to noise ratio (SNR). Decision feed back equalizer (DFE) is used for equalization.
The self-tuning control assumes that the vibrating system is unknown and the controller procedure has the ability to identify the process and to update the necessary control law. Such an algorithm provides the relevan...
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The self-tuning control assumes that the vibrating system is unknown and the controller procedure has the ability to identify the process and to update the necessary control law. Such an algorithm provides the relevant regulator parameters according to the obtained parametric object model. The algorithm can be described as a combination of the following two procedures: the online identification and the computation of the controller parameters. Nearly all of the identification procedures are related to the Least Squares (LS) estimate of a model output. Classified as an ill-posed problem, it implies that the obtained solution is potentially very sensitive to the data perturbations. In order to avoid such problems, the regularized version of the rls method has been considered in this paper. By solving the linear system of equations with a non-singular Sylvester matrix, the formulas for the unknown coefficients of the considered PID-type controller structure have been obtained. The results of the tests and simulations for the circular plate vibration cancellation have been also included.
A new adaptive algorithm is proposed by introducing some modifications to the recursive least squares (rls) algorithm. Except for the noise variance, the proposed algorithm does not require any statistics or knowledge...
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A new adaptive algorithm is proposed by introducing some modifications to the recursive least squares (rls) algorithm. Except for the noise variance, the proposed algorithm does not require any statistics or knowledge of the desired signal, thus, it is suitable for adaptive filtering for channel estimation in code division multiple access (CDMA) systems in cases where the standard rls approach cannot be used. A theoretical analysis demonstrates the convergence of the proposed algorithm, and simulation results for CDMA channel estimation show that the proposed algorithm outperforms existing channel estimation schemes.
Within the context of linear system identification, when harsh conditions might be involved such as low system excitation and high power noise on measured data, the conventional recursive least squares (rls) algorithm...
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Within the context of linear system identification, when harsh conditions might be involved such as low system excitation and high power noise on measured data, the conventional recursive least squares (rls) algorithm encounters difficulties to converge properly at steady-state. We show with numerical experiments that an adequate adaptive magnification of the Kalman gain vector enables a better accuracy by reducing the estimation bias and divided by a factor of 1.5 at least the steady-state mean squared error on the estimated parameters compared to conventional rls and to other techniques involving modifications on the Kalman gain. From a theoretical point of view the proposed approach enhances a previously existing technique while adding reasonable computational complexity making our approach still valuable for a real-time implementation. In addition we found formally a condition to apply on the Kalman gain magnifying factor so as to guarantee the inverse covariance matrix to always remain positive definite.
The sub-recursive least squares (sub-rls) algorithm estimates the coefficients of adaptive filter under the least squares (LS) criterion, however, does not require the calculation of inverse matrix. The sub-rls algori...
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The sub-recursive least squares (sub-rls) algorithm estimates the coefficients of adaptive filter under the least squares (LS) criterion, however, does not require the calculation of inverse matrix. The sub-rls algorithm, based on the different principle from the rls algorithm, still provides a convergence property similar to that of the rls algorithm. This paper first rewrites the convergence condition of the sub-rls algorithm, and then proves that the convergence property of the sub-rls algorithm successively approximates that of the rls algorithm on the convergence condition.
The purpose of this paper is to develop a novel method based on recursive least squares (rls) adaptive algorithm for progressive image transmission (PIT). The image is divided into non-overlapping blocks. Having an ag...
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The purpose of this paper is to develop a novel method based on recursive least squares (rls) adaptive algorithm for progressive image transmission (PIT). The image is divided into non-overlapping blocks. Having an agreed vector sequence between the transmitter and the receiver, each block is related to a regressive model. Meanwhile, at the transmitter the blocks are estimated using the rls algorithm. The high correlation between error vectors, regarding to the rls execution, causes a very high compression rate in their transmission. The error vectors at the receiver are used to run the rls algorithm and to estimate the image in a same manner. The method is easy to implement with a low computational complexity and achieves high quality, compared to other well-known methods. In comparison with its counterparts, simulation results show how efficient the proposed method is.
This paper proposes a simple and efficient method for channel equalization of MIMO-OFDM system with channel estimation. Channel equalization of multiple inputs multiple outputs orthogonal frequency division multiplexi...
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This paper proposes a simple and efficient method for channel equalization of MIMO-OFDM system with channel estimation. Channel equalization of multiple inputs multiple outputs orthogonal frequency division multiplexing (MIMOOFDM) for Rayleigh fading and AWGN channel is simulated. Temporal variations in channel are due to the Doppler spread, a sign of relative motion between transmitter and receiver. This paper evaluates performance of a MIMO OFDM system with different number of transmit and receive antennas, using different modulation schemes and at different values of signal to noise ratio (SNR). Decision feed back equalizer (DFE) is used for equalization.
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