scholarly journals A Computationally Efficient Iterative Algorithm for Estimating the Parameter of Chirp Signal Model

2014 ◽  
Vol 2014 ◽  
pp. 1-14
Author(s):  
Jiawen Bian ◽  
Jing Xing ◽  
Zhihui Liu ◽  
Lihua Fu ◽  
Hongwei Li
Keyword(s):  
Iterative Algorithm ◽  
Signal To Noise Ratio ◽  
Asymptotic Variance ◽  
Chirp Signal ◽  
Computationally Efficient ◽  
Signal Model ◽  
Simulation Experiments ◽  
Differential Signal ◽  
Mean Squared Errors ◽  
Frequency Rate

The parameter estimation of Chirp signal model in additive noises when all the noises are independently and identically distributed (i.i.d.) has been considered. A novel iterative algorithm is proposed to estimate the frequency rate of the considered model by constructing the iterative statistics with one-lag and multilag differential signals. It is observed that the estimator for the iterative algorithm is consistent and works quite well in terms of biases and mean squared errors. Moreover, the convergence rate of the estimator is improved fromOp(N-1)of the initial estimator toOp(N-3/2)for one-lag differential signal condition and fromOp(N-2)of the initial estimator toOp(N-5/2)for multilag differential signal condition, respectively, by only three iterations. The range of the lag is discussed and the optimal lag is obtained for the multilag differential signal condition when the lag is of orderN. The estimator of frequency rate with optimal lag is very close to Cramer-Rao lower bound (CRLB) as well as the asymptotic variance of least-squares estimator (LSE) at moderate signal-to-noise ratio (SNR). Finally, simulation experiments are performed to verify the effectiveness of the algorithm.

scholarly journals A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

2021 ◽  
Vol 11 (2) ◽  
pp. 673
Author(s):  
Guangli Ben ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Xin Zhang
Keyword(s):  
Maximum Likelihood ◽  
Local Search ◽  
Signal To Noise Ratio ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Estimation Of Parameters ◽  
Parameter Estimator ◽  
Denoising Method ◽  
Time Frequency ◽  
Approximate Estimation

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

scholarly journals Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

2021 ◽  
Author(s):  
Di Zhao ◽  
Weijie Tan ◽  
Zhongliang Deng ◽  
Gang Li
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Methods ◽  
Circular Array ◽  
Single Measurement ◽  
Signal Model ◽  
Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

Chirp Signal Model

2020 ◽  
pp. 179-216
Author(s):  
Swagata Nandi ◽  
Debasis Kundu
Keyword(s):  
Chirp Signal ◽  
Signal Model

scholarly journals Robust Geotechnical Design for Soil Slopes considering Uncertain Parameters

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xuejun Zhou ◽  
Wenxiong Huang ◽  
Jie Li ◽  
Ding Chen
Keyword(s):  
Slope Failure ◽  
Signal To Noise Ratio ◽  
System Response ◽  
Soil Parameters ◽  
Main Concern ◽  
Computationally Efficient ◽  
Knee Point ◽  
Soil Slopes ◽  
Geotechnical Design ◽  
Slope Design

Designing the geometry of soil slope is an effective treatment for preventing slope failure. How to deal with the uncertainties involved in soil parameters in geotechnical design is a main concern of geotechnical engineers. In this study, a robust geotechnical design for soil slopes (RGDS) approach was proposed, in which the Uncertainty Theory was introduced to describe explicitly the uncertainties involved in soil parameters. The uncertain reliability is often used to describe the risk of slope failure. The design robustness describing the insensitivity between the variation in the system response and the variation of input uncertain soil parameters was evaluated by the signal-to-noise ratio. The objectives of this design are to maximize the design robustness, minimize the excavation cost, and guarantee the safety (maximize the uncertain reliability). Therefore, the RGDS was formulated as a multiobjective optimization, and the optimal design can be determined based on the concepts of Pareto front and knee point. The proposed RGDS approach was illustrated through a numerical case of a two-layer slope design. The numerical results indicate that the RGDS approach is not only more intuitive and easier to follow but also more computationally efficient.

scholarly journals Fusion of External and Internal Prior Information for the Removal of Gaussian Noise in Images

2020 ◽  
Vol 6 (10) ◽  
pp. 103
Author(s):  
Ali S. Awad
Keyword(s):  
Gaussian Noise ◽  
Prior Information ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
The Other ◽  
Noisy Image ◽  
Simulation Experiments ◽  
Two Dimensional Image ◽  
Pixel Location ◽  
Image Pixels

In this paper, a new method for the removal of Gaussian noise based on two types of prior information is described. The first type of prior information is internal, based on the similarities between the pixels in the noisy image, and the other is external, based on the index or pixel location in the image. The proposed method focuses on leveraging these two types of prior information to obtain tangible results. To this end, very similar patches are collected from the noisy image. This is done by sorting the image pixels in ascending order and then placing them in consecutive rows in a new two-dimensional image. Henceforth, a principal component analysis is applied on the patch matrix to help remove the small noisy components. Since the restored pixels are similar or close in values to those in the clean image, it is preferable to arrange them using indices similar to those of the clean pixels. Simulation experiments show that outstanding results are achieved, compared to other known methods, either in terms of image visual quality or peak signal to noise ratio. Specifically, once the proper indices are used, the proposed method achieves PSNR value better than the other well-known methods by >1.5 dB in all the simulation experiments.

Optical Path Analysis of the Optical Fiber Bundle Hydrogen Sensor

2011 ◽  
Vol 130-134 ◽  
pp. 4102-4105
Author(s):  
Lu Jun Cui ◽  
Hui Chao Shang ◽  
Gang Zhang ◽  
You Ping Chen
Keyword(s):  
Optical Fiber ◽  
Signal To Noise Ratio ◽  
Internal Noise ◽  
Hydrogen Sensor ◽  
Optical Path ◽  
Simulation Experiments ◽  
Approximate Equality ◽  
Optical Paths ◽  
Optical Fiber Bundle ◽  
Peak Value

The present work investigates reflectivity and optimal reflective distance of optical fiber hydrogen sensor in over 0～4000um range. The approximate equality of reflective distance in two optical paths increases signal to noise ratio for optical hydrogen sensor, the fabrication of optical path could eliminate the internal noise and external interferences, and provides higher stability for hydrogen sensor. Through a series of simulation experiments it was found that different reflective distances determine the sensitivity and amplitude response of hydrogen sensor. When the reflective distance was about 1mm in optical path, the sensitivity of optical hydrogen sensor could reach the peak value.

The Comparison between Chaos Algorithm and Music Algorithm Applied in Weak Signal Detection

2013 ◽  
Vol 427-429 ◽  
pp. 1552-1556
Author(s):  
Chen Zhang ◽  
Zhen Bin Gao ◽  
Jing Chun Li ◽  
Biao Huang
Keyword(s):  
Signal Detection ◽  
Array Signal Processing ◽  
Signal To Noise Ratio ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Music Algorithm ◽  
Verilog Hdl ◽  
Simulation Experiments ◽  
Chaos Algorithm ◽  
Core Part

Chaos algorithm is essential in weak signal detection because of its sensibility to weak signals and immunity to noise. This paper applies subspace algorithm which originates from array signal processing to weak signal detection field because of its lower signal to noise ratio. Firstly, the article introduces the principles of two algorithms, then analyses simulation experiments results of real signal data. After that, a conclusion for two algorithms comparison by estimation of computation cost, complexity of implementation and hardware resources occupied is drawn. At the end, the writer designs a duffing chaos module which is the core part of chaotic detection with verilog-hdl.

Subspace-based estimation of time of arrival and Doppler shift for a signal of known waveform

2009 ◽  
Vol 1 (3) ◽  
pp. 209-214
Author(s):  
V.V. Latyshev
Keyword(s):  
Doppler Shift ◽  
Signal To Noise Ratio ◽  
Time Of Arrival ◽  
Computationally Efficient ◽  
Signal To Noise ◽  
Sufficient Statistics ◽  
Ml Estimation ◽  
Generalized Eigenvectors ◽  
Low Dimensional ◽  
Estimation Of Time

The subspace-based technique is used for the estimation of the time of arrival and Doppler shift of a signal of known waveform. The tool to find required subspaces is a special orthogonal decomposition of received data. It allows one to concentrate Fisher information on the desired parameter in just a few of the first terms of the decomposition. This approach offers a low-dimensional vector of sufficient statistics. It leads to computationally efficient Bayesian estimation. Besides, it results in expansion of the signal-to-noise ratio (SNR) range for effective maximum likelihood (ML) estimation. Finally, we can obtain independent time arrival and Doppler shift estimations based on generalized eigenvectors.

scholarly journals Sparse Sonomyography-based Estimation of Isometric Force: A Comparison of Methods and Features

2021 ◽  
Author(s):  
Anne Tryphosa Kamatham ◽  
Meena Alzamani ◽  
Allison Dockum ◽  
Siddhartha Sikdar ◽  
Biswarup Mukherjee
Keyword(s):  
Isometric Force ◽  
Signal To Noise Ratio ◽  
Gaussian Process Regression ◽  
Feature Space ◽  
Speckle Noise ◽  
Neural Activation ◽  
Computationally Efficient ◽  
Noninvasive Methods ◽  
Efficient Operation ◽  
Force Prediction

Noninvasive methods for estimation of joint and muscle forces have widespread clinical and research applications. Surface electromyography or sEMG provides a measure of the neural activation of muscles which can be used to estimate the force produced by the muscle. However, sEMG based measures of force suffer from poor signal-to-noise ratio and limited spatiotemporal specificity. In this paper, we propose an ultrasound imaging or sonomyography-based approach for estimating continuous isometric force from a sparse set of ultrasound scanlines. Our approach isolates anatomically relevant features from A-mode ultrasound signals, greatly reducing the dimensionality of the feature space and the computational complexity involved in traditional ultrasound-based methods. We evaluate the performance of four regression methodologies for force prediction using the reduced feature set. We also evaluate the feasibility of a practical wearable sonomyography-based system by simulating the effect of transducer placement and varying the number of transducers used in force prediction. Our results demonstrate that Gaussian process regression models outperform other regression methods in predicting continuous force levels from just four equispaced transducers and are tolerant to speckle noise. These findings will aid in the design of wearable sonomyography-based force prediction systems with robust, computationally efficient operation.

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