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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ISBN:
(纸本)9781467321976;9781467321969
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.
Invariable step size based least-mean-square error (ISS-LMS) was considered as a very simple adaptive filtering algorithm and hence it has been widely utilized in many applications, such as adaptive channel estimation...
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ISBN:
(纸本)9784907764487
Invariable step size based least-mean-square error (ISS-LMS) was considered as a very simple adaptive filtering algorithm and hence it has been widely utilized in many applications, such as adaptive channel estimation. It is well known that the convergence speed of ISS-LMS is fixed by the initial step-size. In the channel estimation scenarios, it is very hard to make tradeoff between convergence speed and estimation performance. In this paper, we propose an iteration-promoting variable step size based least-mean-square error (IPVSS-LMS) algorithm to control the convergence speed as well as to improve the estimation performance. Simulation results show that the proposed algorithm can achieve better estimation performance (3dB) than previous ISS-LMS while without sacrificing convergence speed as well as computational complexity.
By studyingthe shortage of the traditional fixed step size least mean square (LMS) algorithm. This paper builds a nonlinear function relationship between mu and the error signal by reviewing the existing algorithm and...
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ISBN:
(纸本)9781479939039
By studyingthe shortage of the traditional fixed step size least mean square (LMS) algorithm. This paper builds a nonlinear function relationship between mu 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.
Electro-magnetic acoustic detection technique has become a new development trend of nondestructive testing because of its high detection efficiency, accurate detection results, etc, so it now has been widely adopted i...
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ISBN:
(纸本)9780819479402
Electro-magnetic acoustic detection technique has become a new development trend of nondestructive testing because of its high detection efficiency, accurate detection results, etc, so it now has been widely adopted in the railway department of our country. When the signal is detected using electro-magnetic acoustic detection technique, the influence of the poor condition of wheel surface, the existing electromagnetic interference and other factors will enable different levels of noise to exist in the detected signal, which will affect the signal quality, thereby reducing the detection accuracy. After introducing the structure and principle of electro-magnetic acoustic detection system, this paper has put forward two noise suppression algorithms for the noise problem of the detection signal, namely, phase difference algorithm and adaptive filtering algorithm. On the premise of reserving the necessary signal waveform of system, the algorithms can effectively suppress the noise of a detected signal, improve the quality of a data waveform, and obtain good detection results. The paper also compares two algorithms and points out that the better detection accuracy can be obtained if combining the two algorithms. This work has certain inspiration to raise the accuracy of electro-magnetic acoustic detection results.
A spatial active noise control (ANC) method based on the individual kernel interpolation of primary and secondary sound fields is proposed. Spatial ANC is aimed at cancelling unwanted primary noise within a continuous...
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ISBN:
(纸本)9781665405409
A spatial active noise control (ANC) method based on the individual kernel interpolation of primary and secondary sound fields is proposed. Spatial ANC is aimed at cancelling unwanted primary noise within a continuous region by using multiple secondary sources and microphones. A method based on the kernel interpolation of a sound field makes it possible to attenuate noise over the target region with flexible array geometry. Furthermore, by using the kernel function with directional weighting, prior information on primary noise source directions can be taken into consideration. However, whereas the sound field to be interpolated is a superposition of primary and secondary sound fields, the directional weight for the primary noise source was applied to the total sound field in previous work;therefore, the performance improvement was limited. We propose a method of individually interpolating the primary and secondary sound fields and formulate a normalized least-mean-square algorithm based on this interpolation method. Experimental results indicate that the proposed method outperforms the method based on total kernel interpolation.
In this paper an active random noise control using adaptive learning rate neural networks with an immune feedback law is presented. The adaptive learning rate strategy increases the learning rate by a small constant i...
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ISBN:
(纸本)0819460745
In this paper an active random noise control using adaptive learning rate neural networks with an immune feedback law is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. In the proposed method, because of the immune feedback law change a learning rate of the neural networks individually and adaptively, it is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks with the immune feedback law. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.
The problem of providing the fastest two-way communication between two or several points on the Earth's surface is considered. The transport delay and the cost of such a connection should be minimal. To solve this...
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ISBN:
(纸本)9781538624746
The problem of providing the fastest two-way communication between two or several points on the Earth's surface is considered. The transport delay and the cost of such a connection should be minimal. To solve this problem as an intermediate point to be used a group of small and cheap enough satellites moving at low orbit. This article discusses the problem of gathering a group in orbit, stabilizing it, measuring relative distances and orientations. To reduce the cost of the system, it is expected to use the cheapest and therefore not very accurate, relative coordinate measures. To ensure acceptable accuracy, it is proposed to use a specially developed adaptive filtering algorithm based on the analysis of the spectral density of the updating sequence. The estimates of the navigation parameters obtained in this way are used to form the optimal terminal control of the motion by the center of mass of the active satellite. Miniature engines create the control forces. The theoretical results are confirmed by simulation.
In this paper, we propose a novel adaptive filtering algorithm named adaptive parallel variable-metric projection (APVP) algorithm, which includes the proportionate normalized least mean square (PNLMS) algorithm as it...
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ISBN:
(纸本)1424407281
In this paper, we propose a novel adaptive filtering algorithm named adaptive parallel variable-metric projection (APVP) algorithm, which includes the proportionate normalized least mean square (PNLMS) algorithm as its special example. The proposed algorithm is based on parallel projection (onto multiple closed convex sets) with time-varying metrics. A convergence analysis of the proposed algorithm is presented with the aid of the adaptive projected subgradient method. Numerical examples demonstrate that the proposed algorithm realizes echo cancellation superior to the conventional algorithms.
The real-timely estimation of the SOC (state of charge) is the key technology in Li-ion battery management system. In this paper, to overcome the error of the SOC estimation of Extended Kalman filter (EKF), a new esti...
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ISBN:
(纸本)9783037858615
The real-timely estimation of the SOC (state of charge) is the key technology in Li-ion battery management system. In this paper, to overcome the error of the SOC estimation of Extended Kalman filter (EKF), a new estimation method based on modified-strong tracking filter (MSTF) is applied to SOC estimation of Li-ion battery, based on the second-order RC equivalent circuit model. Experiments are made to compare the new filter with the EKE and Coulomb counting approach (Ah). The simulation results demonstrate that the new filter algorithm MSTF used in this paper has higher filtering accuracy under the same conditions.
A continuous mixed p-norm adaptivealgorithm with reweighted L0-norm constraint (RL0-CMPN) is proposed for sparse system identification. The RL0-CMPN algorithm makes full use of the advantages of the different norm. T...
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ISBN:
(纸本)9781538608432
A continuous mixed p-norm adaptivealgorithm with reweighted L0-norm constraint (RL0-CMPN) is proposed for sparse system identification. The RL0-CMPN algorithm makes full use of the advantages of the different norm. This algorithm can solve large coefficient update spread problem and reduce the slow-down effect. Besides, it is a continuous mixed p-norm adaptivealgorithm. The computation complexity of the algorithm is discussed. Finally, the algorithm is compared with some exist adaptive filtering algorithms in different signal-to-noise ratio (SNR). Theoretical analysis combined with experimental simulations show that the algorithm can achieve better tracking speed, lower steady state error and anti-noise performance.
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