scholarly journals Quick Estimation of Periodic Signal Parameters from One-bit Measurements

2021 ◽  
Author(s):  
Paolo Carbone
Keyword(s):  
Likelihood Function ◽  
Irrational Number ◽  
Estimation Procedure ◽  
Low Complexity ◽  
Estimation Accuracy ◽  
General Interest ◽  
Periodic Signal ◽  
Information Encoding ◽  
Input Noise ◽  
New Applications

<div> <div> <div> <p>Estimation of periodic signals, based on quantized data, is a topic of general interest in the area of instrumentation and measurement. While several methods are available, new applications require low-power, low-complexity, and adequate estimation accuracy. In this paper, we consider the simplest possible quantization, that is binary quantization, and describe a technique to estimate the parameters of a sampled periodic signal, using a fast algorithm. By neglecting the possibility that the sampling process is triggered by some signal-derived event, sampling is assumed to be asynchronous, that is the ratio between the signal and the sampling periods is defined to be an irrational number. To preserve enough information at the quantizer output, additive Gaussian input noise is assumed as the information encoding mechanism. With respect to published techniques addressing the same problem, the proposed approach does not rely on the numerical estimation of the maximum likelihood function, but provides solutions that are very closed to this estimate. At the same time, since the main estimator is based on matrix inversion, it proves to be less time-consuming than the numerical maximization of the likelihood function, especially when solving problems with a large number of parameters. The estimation procedure is described in detail and validated using both simulation and experimental results. The estimator performance limitations are also highlighted. </p> </div> </div> </div>

scholarly journals Quick Estimation of Periodic Signal Parameters from One-bit Measurements

2021 ◽  
Author(s):  
Paolo Carbone
Keyword(s):  
Likelihood Function ◽  
Irrational Number ◽  
Estimation Procedure ◽  
Low Complexity ◽  
Estimation Accuracy ◽  
General Interest ◽  
Periodic Signal ◽  
Information Encoding ◽  
Input Noise ◽  
New Applications

<div> <div> <div> <p>Estimation of periodic signals, based on quantized data, is a topic of general interest in the area of instrumentation and measurement. While several methods are available, new applications require low-power, low-complexity, and adequate estimation accuracy. In this paper, we consider the simplest possible quantization, that is binary quantization, and describe a technique to estimate the parameters of a sampled periodic signal, using a fast algorithm. By neglecting the possibility that the sampling process is triggered by some signal-derived event, sampling is assumed to be asynchronous, that is the ratio between the signal and the sampling periods is defined to be an irrational number. To preserve enough information at the quantizer output, additive Gaussian input noise is assumed as the information encoding mechanism. With respect to published techniques addressing the same problem, the proposed approach does not rely on the numerical estimation of the maximum likelihood function, but provides solutions that are very closed to this estimate. At the same time, since the main estimator is based on matrix inversion, it proves to be less time-consuming than the numerical maximization of the likelihood function, especially when solving problems with a large number of parameters. The estimation procedure is described in detail and validated using both simulation and experimental results. The estimator performance limitations are also highlighted. </p> </div> </div> </div>

scholarly journals Low-complexity beam-domain channel estimation and power allocation in hybrid architecture massive MIMO systems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiao Chen ◽  
Zaichen Zhang ◽  
Liang Wu ◽  
Jian Dang
Keyword(s):  
Channel Estimation ◽  
Power Allocation ◽  
Spectral Efficiency ◽  
Communication Systems ◽  
Mimo Systems ◽  
Beam Steering ◽  
Estimation Method ◽  
Low Complexity ◽  
Estimation Accuracy ◽  
Hybrid Architecture

Abstract In this journal, we investigate the beam-domain channel estimation and power allocation in hybrid architecture massive multiple-input and multiple-output (MIMO) communication systems. First, we propose a low-complexity channel estimation method, which utilizes the beam steering vectors achieved from the direction-of-arrival (DOA) estimation and beam gains estimated by low-overhead pilots. Based on the estimated beam information, a purely analog precoding strategy is also designed. Then, the optimal power allocation among multiple beams is derived to maximize spectral efficiency. Finally, simulation results show that the proposed schemes can achieve high channel estimation accuracy and spectral efficiency.

A method for direct estimation of interval velocities

Geophysics ◽  
1982 ◽  
Vol 47 (12) ◽  
pp. 1657-1671 ◽  
Author(s):  
Philip S. Schultz
Keyword(s):  
Estimation Procedure ◽  
Estimation Accuracy ◽  
Substantial Contribution ◽  
Interval Velocity ◽  
Direct Estimation ◽  
Enhanced Resolution ◽  
Prior Estimate ◽  
Sensitive Function ◽  
Interval Velocities ◽  
Ray Parameter

The most commonly used method for obtaining interval velocities from seismic data requires a prior estimate of the root‐mean‐square (rms) velocity function. A reduction to interval velocity uses the Dix equation, where the interval velocity in a layer emerges as a sensitive function of the rms velocity picks above and below the layer. Approximations implicit in this method are quite appropriate for deep data, and they do not contribute significantly to errors in the interval velocity estimate. However, when the data are from a shallow depth (vertical two‐way traveltime being less than direct arrival to the farthest geophone), the assumption within the rms approximation that propagation angles are small requires that much of the reflection energy be muted, along with, of course, all the refraction energy. By means of a simple data transformation to the ray parameter domain via the slanted plane‐wave stack, three types of arrivals from any given interface (subcritical and supercritical reflections and critical refractions) become organized into a single elliptical trajectory. Such a trajectory replaces the composite hyperbolic and linear moveouts in the offset domain (for reflections and critical refractions, respectively). In a layered medium, the trajectory of all but the first event becomes distorted from a true ellipse into a pseudo‐ellipse. However, by a computationally simple layer stripping operation involving p‐dependent time shifts, the interval velocity in each layer can be estimated in turn and its distorting effect removed from underlying layers, permitting a direct estimation of interval velocities for all layers. Enhanced resolution and estimation accuracy are achieved because previously neglected wide‐angle arrivals, which do not conform to the rms approximation, make a substantial contribution in the estimation procedure.

Stochastic resonance in an array of integrate-and-fire neurons with threshold

2018 ◽  
Vol 32 (16) ◽  
pp. 1850169 ◽  
Author(s):  
Bingchang Zhou ◽  
Qianqian Qi
Keyword(s):  
Stochastic Resonance ◽  
Additive Noise ◽  
Signal To Noise Ratio ◽  
Array Size ◽  
Periodic Signal ◽  
Signal Parameter ◽  
Input Noise ◽  
Integrate And Fire ◽  
Noisy Input ◽  
Better Than

We investigate the phenomenon of stochastic resonance (SR) in parallel integrate-and-fire neuronal arrays with threshold driven by additive noise or signal-dependent noise (SDN) and a noisy input signal. SR occurs in this system. Whether the system is subject to the additive noise or SDN, the input noise [Formula: see text] weakens the performance of SR but the array size N and signal parameter [Formula: see text] promote the performance of SR. Signal parameter [Formula: see text] promotes the performance of SR for the additive noise, but the peak values of the output signal-to-noise ratio [Formula: see text] first decrease, then increase as [Formula: see text] increases for the SDN. Moreover, when [Formula: see text] tends to infinity, for the SDN, the curve of [Formula: see text] first increases and then decreases, however, for the additive noise, the curve of [Formula: see text] increases to reach a plain. By comparing system performance with the additive noise to one with SDN, we also find that the information transmission of a periodic signal with SDN is significantly better than one with the additive noise in limited array size N.

scholarly journals Maximum Likelihood Estimation for Three-Parameter Weibull Distribution Using Evolutionary Strategy

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Fan Yang ◽  
Hu Ren ◽  
Zhili Hu
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Likelihood Function ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Evolutionary Strategy ◽  
Likelihood Method ◽  
Conventional Algorithm

The maximum likelihood estimation is a widely used approach to the parameter estimation. However, the conventional algorithm makes the estimation procedure of three-parameter Weibull distribution difficult. Therefore, this paper proposes an evolutionary strategy to explore the good solutions based on the maximum likelihood method. The maximizing process of likelihood function is converted to an optimization problem. The evolutionary algorithm is employed to obtain the optimal parameters for the likelihood function. Examples are presented to demonstrate the proposed method. The results show that the proposed method is suitable for the parameter estimation of the three-parameter Weibull distribution.

scholarly journals Attitude Estimation for In-Service Base Station Antenna Using Downlink Channel Fading Statistics

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Cen Ling ◽  
Xuefeng Yin ◽  
Yongyu He ◽  
Silvia Ruiz Boqué
Keyword(s):  
Likelihood Function ◽  
Estimation Method ◽  
Path Loss ◽  
Likelihood Estimation ◽  
Multipath Fading ◽  
Base Station ◽  
Estimation Accuracy ◽  
Channel Fading ◽  
Multiple User ◽  
Fading Statistics

A maximum-likelihood-estimation method is proposed for extracting the attitude of a sectoring base station (BS) antenna by using the received signal strengths observed by multiple user equipments (UEs) in this contribution. This method calculates the likelihood function of the antenna attitude derived by taking into account the multiscale fading statistics, that is, path loss, shadowing, and multipath fading. Depending on whether a calibration result of these fading statistics is available or not, the proposed method can be utilized in either calibration-based estimation (CBE) or calibration-free estimation (CFE) approaches. The performance of both methods is evaluated by Monte-Carlo simulations and real experiments. The results obtained demonstrate that the estimation accuracy of both CBE and CFE approaches increases when the percentage of UEs in the line-of-sight (LoS) condition among all available UEs increases and, moreover, the total number of UEs has no significant impact on the estimation accuracy. Furthermore, the CFE exhibits more robust performance than the CBE particularly in the case where the calibration results involve uncertainties.

scholarly journals Adaptive Fifth-Degree Cubature Information Filter for Multi-Sensor Bearings-Only Tracking

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3241 ◽  
Author(s):  
Haonan Jiang ◽  
Yuanli Cai
Keyword(s):  
Information Filtering ◽  
Estimation Procedure ◽  
Estimation Accuracy ◽  
Selection Strategy ◽  
Bayesian Filtering ◽  
Process Noise ◽  
Covariance Matching ◽  
Information Filter ◽  
Cubature Kalman Filter ◽  
Noise Covariance

Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filtering framework. With a sensor selection strategy developed using observability theory and a recursive process noise covariance estimation procedure derived using the covariance matching principle, the proposed filtering algorithm demonstrates better estimation accuracy and filtering stability. Simulation results validate the superiority of the AFCIF.

scholarly journals Application of Bee Evolutionary Genetic Algorithm to Maximum Likelihood Direction-of-Arrival Estimation

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xinnan Fan ◽  
Linbin Pang ◽  
Pengfei Shi ◽  
Guangzhi Li ◽  
Xuewu Zhang
Keyword(s):  
Genetic Algorithm ◽  
Computational Complexity ◽  
Maximum Likelihood ◽  
Likelihood Function ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Initial Population ◽  
Crossover Operator ◽  
Excellent Performance ◽  
Evolutionary Genetic

The maximum likelihood (ML) method achieves an excellent performance for DOA estimation. However, its computational complexity is too high for a multidimensional nonlinear solution search. To address this issue, an improved bee evolutionary genetic algorithm (IBEGA) is applied to maximize the likelihood function for DOA estimation. First, an opposition-based reinforcement learning method is utilized to achieve a better initial population for the BEGA. Second, an improved arithmetic crossover operator is proposed to improve the global searching performance. The experimental results show that the proposed algorithm can reduce the computational complexity of ML DOA estimation significantly without sacrificing the estimation accuracy.

scholarly journals A Low-Complexity GA-WSF Algorithm for Narrow-Band DOA Estimation

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Haihua Chen ◽  
Jialiang Hu ◽  
Hui Tian ◽  
Shibao Li ◽  
Jianhang Liu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Computational Complexity ◽  
Population Size ◽  
Narrow Band ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Accuracy ◽  
Improved Genetic Algorithm ◽  
Complexity Estimation

This paper proposes a low-complexity estimation algorithm for weighted subspace fitting (WSF) based on the Genetic Algorithm (GA) in the problem of narrow-band direction-of-arrival (DOA) finding. Among various solving techniques for DOA, WSF is one of the highest estimation accuracy algorithms. However, its criteria is a multimodal nonlinear multivariate optimization problem. As a result, the computational complexity of WSF is very high, which prevents its application to real systems. The Genetic Algorithm (GA) is considered as an effective algorithm for finding the global solution of WSF. However, conventional GA usually needs a big population size to cover the whole searching space and a large number of generations for convergence, which means that the computational complexity is still high. To reduce the computational complexity of WSF, this paper proposes an improved Genetic algorithm. Firstly a hypothesis technique is used for a rough DOA estimation for WSF. Then, a dynamic initialization space is formed around this value with an empirical function. Within this space, a smaller population size and smaller amount of generations are required. Consequently, the computational complexity is reduced. Simulation results show the efficiency of the proposed algorithm in comparison to many existing algorithms.

Power-Spectrum Centroid Detection for Doppler Systems Applications

1980 ◽  
Vol 2 (3) ◽  
pp. 232-261 ◽  
Author(s):  
Levy Gerzberg ◽  
James D. Meindl
Keyword(s):  
Power Spectrum ◽  
Additive Noise ◽  
Analysis Method ◽  
Estimation Errors ◽  
Input Noise ◽  
Centroid Estimation ◽  
System Requirements ◽  
Centroid Detection ◽  
New Applications ◽  
Noise Reduction Technique

The theoretical basis for the development of a power-spectrum centroid detector is presented, and known and new applications to centroid detection are described. Based on these applications, the requirements are formulated for a versatile centroid detector which can be employed in various types of ultrasonic doppler systems. An analysis method for centroid estimation is developed and applied to the correlation- and √f-type detectors. Closed-form expressions for estimation errors are derived and used for detector evaluation with and without additive noise at the input. A noise-reduction technique is introduced that enables accurate centroid detection despite the presence of strong input noise. Based on the results of this analysis, theoretical comparisons of the detectors, and system requirements, the √f detector is selected for implementation and use.

Sign in / Sign up

Export Citation Format

Share Document