By studyingthe shortage of the traditional fixed step size least mean square (LMS) algorithm. This paper builds a nonlinear function relationship between μ and the error signal by reviewing the existing algorithm and...
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ISBN:
(纸本)9781479939046
By studyingthe shortage of the traditional fixed step size least mean square (LMS) algorithm. This paper builds a nonlinear function relationship between μ and the error signal by reviewing the existing algorithm and presents a novel variable step size LMS adaptive filtering algorithm by improving Sigmoid function based on translation transformation. The selective of parameters and the performance of convergence are discussed. Theoretical analysis and simulation results show that the proposed variable step size LMS algorithm has better performance. Comparing with some existing algorithms, the algorithm improves their convergence performance.
In some practical applications, the two-dimensional (2-D) direction-of-arrivals (DOAs) of incident signals should be estimated adaptively or the time-varying 2-D DOAs should be tracked promptly from the noisy array da...
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In some practical applications, the two-dimensional (2-D) direction-of-arrivals (DOAs) of incident signals should be estimated adaptively or the time-varying 2-D DOAs should be tracked promptly from the noisy array data, and multipath propagation is usually encountered due to various reflections, where the incident signals are caused to be coherent (i.e., fully correlated). In this paper, we propose a new computationally efficient subspace-based adaptivealgorithm for 2-D DOA tracking of multiple coherent incident signals by using two parallel uniform linear arrays (ULAs). In the proposed algorithm, the computationally expensive eigendecomposition and the pair-matching of estimated 2-D DOAs are avoided, and the association of estimated 2-D DOAs at two successive time instants is accomplished by employing the Luenberger observer and dynamic model of direction trajectories. The effectiveness of the proposed algorithm are verified through numerical examples.
The urban traffic usually has the characteristics of time-variation and nonlinearity, real-time and accurate traffic flow forecasting has become an important component of the Intelligent Transportation System(ITS). Th...
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The urban traffic usually has the characteristics of time-variation and nonlinearity, real-time and accurate traffic flow forecasting has become an important component of the Intelligent Transportation System(ITS). The paper gives a brief introduction of the basic theory of Kalman filter, and establishes the traffic flow forecasting model on the basis of the adaptive Kalman filter, while the traditional Kalman filtering model has the shortcomings of lower forecasting accuracy and easily running into filtering divergence. The Sage&Husa adaptive filtering algorithm will appropriately estimate and correct the unknown or uncertain noise covariance, so as to improve the dynamic characteristics of the model. The simulation results demonstrate that the adaptive Kalman filtering forecasting model has stronger tracking capability and higher forecasting precision, which is applicable to the traffic flow forecasting.
In this article, we proposed a robust adaptivealgorithm for PN code acquisition and beamforming in direct sequence spread spectrum (DSSS) system with antenna arrays. Two adaptive filters are employed in the DSSS syst...
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ISBN:
(纸本)9781629931357
In this article, we proposed a robust adaptivealgorithm for PN code acquisition and beamforming in direct sequence spread spectrum (DSSS) system with antenna arrays. Two adaptive filters are employed in the DSSS system, one is a spatial filter acting as a beamformer and the other is a temporal filter acting as a PN code-delay estimator. The cost function of LMS adaptivealgorithm is given by constraining both the spatial filter weight and the temporal filter weight. A robust and fast adaptivealgorithm with two different iterative step size is presented in the following. Computer simulations show that the proposed algorithm becomes more robust and faster, especially in DSSS system with a large spread spectrum gain.
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