934 research outputs found

    Hirschman optimal transform least mean square adaptive filters.

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    Ionospheric gravity wave measurements with the USU dynasonde

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    A method for the measurement of ionospheric Gravity Wave (GW) using the USU Dynasonde is outlined. This method consists of a series of individual procedures, which includes functions for data acquisition, adaptive scaling, polarization discrimination, interpolation and extrapolation, digital filtering, windowing, spectrum analysis, GW detection, and graphics display. Concepts of system theory are applied to treat the ionosphere as a system. An adaptive ionogram scaling method was developed for automatically extracting ionogram echo traces from noisy raw sounding data. The method uses the well known Least Mean Square (LMS) algorithm to form a stochastic optimal estimate of the echo trace which is then used to control a moving window. The window tracks the echo trace, simultaneously eliminating the noise and interference. Experimental results show that the proposed method functions as designed. Case studies which extract GW from ionosonde measurements were carried out using the techniques described. Geophysically significant events were detected and the resultant processed results are illustrated graphically. This method was also developed for real time implementation in mind

    Constant beamwidth generalised sidelobe canceller

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    In this paper, we proposed a constant beamwidth discrete Fourier transform (DFT) beamformer based on the generalised sidelobe canceller (GSC). Broadband signals are decomposed into frequency bins which are grouped into octaves and tapered individually. The resulting beampattern possesses constant beamwidth across the entire operating spectrum, thus ensuring uniform spatial resolution. Further incorporation of the GSC allows adaptive nulling of interference to coincide with uniform resolution, enhancing the beamformer’s performance. However, modification to the constraint equation of the standard GSC is required to account for the frequency-dependent weighting of sensors

    Transform domain filtering in incremental and diffusion strategies over distributed networks

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    We analyse incremental and diffusion co-operative schemes in which nodes share information to some neighbour nodes in order to estimate desired parameter of interest locally in the presence of noise. Each node works as an adaptive filter and having its own learning ability. In incremental co-operative fashion a node takes information from previous node and after local estimation the information is sent to next node whereas in diffusion the input is taken from various nodes so that after each iteration the behaviour of distributed network is observed. We employ LMS structure for updating the observations. The convergence performance and computational complexity of LMS-filter is very important consideration for the point of view of speed boost and cost reduction. The convergence performance of a filter depends on eigenvalue spread of covariance matrix of input data or in other words inversely proportional to the eigenvalue spread of the input data. If input data is de-correlated the eigenvalue spread is less and if input data is correlated the eigenvalue spread is more. Transform domain filter has data de-correlation properties of transforms like DCT & DFT. The data de-correlation by the unitary transforms is depends on the orthogonal property of individual transform. Hence we get improved convergence performance by applying transform domain to input data followed by power normalization of input data. If the input data is fully de-correlated the covariance matrix of input data is proportional to the identity matrix

    An investigation of delayless subband adaptive filtering for multi-input multi-output active noise control applications

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    The broadband control of noise and vibration using multi-input, multi-output (MIMO) active control systems has a potentially wide variety of applications. However, the performance of MIMO systems is often limited in practice by high computational demand and slow convergence speeds. In the somewhat simpler context of single-input, single- output broadband control, these problems have been overcome through a variety of methods including subband adaptive filtering. This paper presents an extension of the subband adaptive filtering technique to the MIMO active control problem and presents a comprehensive study of both the computational requirements and control performance. The implementation of the MIMO filtered-x LMS algorithm using subband adaptive filtering is described and the details of two specific implementations are presented. The computational demands of the two MIMO subband active control algorithms are then compared to that of the standard full-band algorithm. This comparison shows that as the number of subbands employed in the subband algorithms is increased, the computational demand is significantly reduced compared to the full-band implementation provided that a restructured analysis filter-bank is employed. An analysis of the convergence of the MIMO subband adaptive algorithm is then presented and this demonstrates that although the convergence of the control filter coefficients is dependent on the eigenvalue spread of the subband Hessian matrix, which reduces as the number of subbands is increased, the convergence of the cost function is limited for large numbers of subbands due to the simultaneous increase in the weight stacking distortion. The performance of the two MIMO subband algorithms and the standard full-band algorithm has then been assessed through a series of time-domain simulations of a practical active control system and it has been shown that the subband algorithms are able to achieve a significant increase in the convergence speed compared to the full-band implementatio

    Simulation of Few Mode Fiber Communication System Using Adaptive Recursive least Square Algorithm

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    The constant demand of faster mode of communication has revolutionized the technology related to optical fiber communication system. The large member of global researchers are using space division multiplexing (SDM).The research is motivated by the urgent industrial requirement. This technology has sample of scope of improving the channel space. The few mode fiber (FMF) communication system improvement using adaptive algorithm has few issues which are posing the challenges like intermodal noise due to compiling in a random manner .It has some delay which needs to be taken care of is called as differential mode group delay (DMGD).In this work, Recursive Least Square (RLS) has been promised. This yield the convergence faster but at the cost of complex hardware. The FD-LMS algorithm has been considered as a reference. A step size controlled has been put to work. In the reference work the FD-LMS appears to better than LMS algorithm
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