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Register a new userUse of adaptive filtering techniques and deconvolution to obtain low range sidelobe samples
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Added by
Mohit Kumar
Publication date
2020
fields
Electronic Engineering
and research's language is
English
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In this paper the use of adaptive filtering techniques to obtain better peak sidelobe suppression and integrated sidelobe energy will be discussed with regard to weather radars and obtaining better sensitivity with this technique. The performance of these new coefficient sets obtained with adaptive filter (using RLS optimization) will be discussed and presented. They will also be compared with the existing techniques and their peak sidelobe levels.
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In this paper, we propose a novel waveform design for multi-input multi-output (MIMO) dual-functional radar-communication systems by taking the range sidelobe control into consideration. In particular, we focus on optimizing the weighted summation of communication and radar metrics under per-antenna power budget. While the formulated optimization problem is non-convex, we develop a first-order descent algorithm by exploiting the manifold structure of its feasible region, which finds a near-optimal solution within a low computational overhead. Numerical results show that the proposed waveform design outperforms the conventional techniques by improving the communication rate while reducing the range sidelobe level.
Error entropy is a important nonlinear similarity measure, and it has received increasing attention in many practical applications. The default kernel function of error entropy criterion is Gaussian kernel function, however, which is not always the best choice. In our study, a novel concept, called generalized error entropy, utilizing the generalized Gaussian density (GGD) function as the kernel function is proposed. We further derivate the generalized minimum error entropy (GMEE) criterion, and a novel adaptive filtering called GMEE algorithm is derived by utilizing GMEE criterion. The stability, steady-state performance, and computational complexity of the proposed algorithm are investigated. Some simulation indicate that the GMEE algorithm performs well in Gaussian, sub-Gaussian, and super-Gaussian noises environment, respectively. Finally, the GMEE algorithm is applied to acoustic echo cancelation and performs well.
Pathological Hand Tremor (PHT) is among common symptoms of several neurological movement disorders, which can significantly degrade quality of life of affected individuals. Beside pharmaceutical and surgical therapies, mechatronic technologies have been utilized to control PHTs. Most of these technologies function based on estimation, extraction, and characterization of tremor movement signals. Real-time extraction of tremor signal is of paramount importance because of its application in assistive and rehabilitative devices. In this paper, we propose a novel on-line adaptive method which can adjust the hyper-parameters of the filter to the variable characteristics of the tremor. The proposed WAKE: Wavelet decomposition coupled with Adaptive Kalman filtering technique for pathological tremor Extraction, referred to as the WAKE framework is composed of a new adaptive Kalman filter and a wavelet transform core to provide indirect prediction of the tremor, one sample ahead of time, to be used for its suppression. In this paper, the design, implementation and evaluation of WAKE are given. The performance is evaluated based on three different datasets, the first one is a synthetic dataset, developed in this work, that simulates hand tremor under ten different conditions. The second and third ones are real datasets recorded from patients with PHTs. The results obtained from the proposed WAKE framework demonstrate significant improvements in the estimation accuracy in comparison with two well regarded techniques in the literature.
Short-and-sparse deconvolution (SaSD) is the problem of extracting localized, recurring motifs in signals with spatial or temporal structure. Variants of this problem arise in applications such as image deblurring, microscopy, neural spike sorting, and more. The problem is challenging in both theory and practice, as natural optimization formulations are nonconvex. Moreover, practical deconvolution problems involve smooth motifs (kernels) whose spectra decay rapidly, resulting in poor conditioning and numerical challenges. This paper is motivated by recent theoretical advances, which characterize the optimization landscape of a particular nonconvex formulation of SaSD. This is used to derive a $provable$ algorithm which exactly solves certain non-practical instances of the SaSD problem. We leverage the key ideas from this theory (sphere constraints, data-driven initialization) to develop a $practical$ algorithm, which performs well on data arising from a range of application areas. We highlight key additional challenges posed by the ill-conditioning of real SaSD problems, and suggest heuristics (acceleration, continuation, reweighting) to mitigate them. Experiments demonstrate both the performance and generality of the proposed method.
We obtain thermostatted ring polymer molecular dynamics (TRPMD) from exact quantum dynamics via Matsubara dynamics, a recently-derived form of linearization which conserves the quantum Boltzmann distribution. Performing a contour integral in the complex quantum Boltzmann distribution of Matsubara dynamics, replacement of the imaginary Liouvillian which results with a Fokker-Planck term gives TRPMD. We thereby provide error terms between TRPMD and quantum dynamics and predict the systems in which they are likely to be small. Using a harmonic analysis we show that careful addition of friction causes the correct oscillation frequency of the higher ring-polymer normal modes in a harmonic well, which we illustrate with calculation of the position-squared autocorrelation function. However, no physical friction parameter will produce the correct fluctuation dynamics for a parabolic barrier. The results in this paper are consistent with previous numerical studies and advise the use of TRPMD for the computation of spectra.
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