Optimization of Threshold Values for Estimators Based on Single-Bit Quantized Sensors Using Genetic Algorithms

2002 ◽  
Author(s):  
Nishant Unnikrishnan ◽  
Ajay Mahajan ◽  
Antonios Mengoulis ◽  
R. Viswanathan
Keyword(s):  
Genetic Algorithms ◽  
Parameter Estimation ◽  
Data Storage ◽  
Dynamic Range ◽  
Mean Squared Error ◽  
High Reliability ◽  
Sensor Data ◽  
Threshold Values ◽  
Distributed Sensors ◽  
Range Resolution

The paper considers the problem of signal parameter estimation using a collection of distributed sensors called a sensor pack. Each sensor quantizes its data to one-bit information and sends it to a fusion processor for the estimation of the parameter. Estimation of a constant signal in additive noise is considered. Estimators are formulated based on one-bit sensor data and their mean squared error (MSE) performances are evaluated through simulation studies. It is shown that selecting certain threshold values for quantizing the sensor outputs can lower the MSE. Genetic algorithms are used to find the optimal threshold values for the sensors. Results from this study show that robust estimation of parameter is possible by using a moderately large number of one-bit quantized sensor data. This work has significance in applications that demand high reliability in sensor networks in spite of sensor failures, limited sensor dynamic range, resolution, bandwidth for data transmission or even data storage.

scholarly journals Marginal quantile regression for longitudinal data analysis in the presence of time-dependent covariates

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
I-Chen Chen ◽  
Philip M. Westgate
Keyword(s):  
Parameter Estimation ◽  
Longitudinal Data ◽  
Quantile Regression ◽  
Mean Squared Error ◽  
Time Dependent ◽  
Regression Methods ◽  
Squared Error ◽  
Highly Correlated ◽  
Time Dependent Covariates ◽  
Independence Structure

AbstractWhen observations are correlated, modeling the within-subject correlation structure using quantile regression for longitudinal data can be difficult unless a working independence structure is utilized. Although this approach ensures consistent estimators of the regression coefficients, it may result in less efficient regression parameter estimation when data are highly correlated. Therefore, several marginal quantile regression methods have been proposed to improve parameter estimation. In a longitudinal study some of the covariates may change their values over time, and the topic of time-dependent covariate has not been explored in the marginal quantile literature. As a result, we propose an approach for marginal quantile regression in the presence of time-dependent covariates, which includes a strategy to select a working type of time-dependency. In this manuscript, we demonstrate that our proposed method has the potential to improve power relative to the independence estimating equations approach due to the reduction of mean squared error.

scholarly journals A More Reliable Orbit Initialization Method for LEO Precise Orbit Determination Using GNSS

2020 ◽  
Vol 12 (21) ◽  
pp. 3646
Author(s):  
Xuewen Gong ◽  
Jizhang Sang ◽  
Fuhong Wang ◽  
Xingxing Li
Keyword(s):  
Parameter Estimation ◽  
Orbit Determination ◽  
High Reliability ◽  
Precise Orbit Determination ◽  
Direct Approach ◽  
Design Matrix ◽  
Reference Orbit ◽  
Stability Analyses ◽  
Precise Orbit ◽  
Orbit Parameter

Precise orbit determination (POD) using GNSS has been rapidly developed and is the mainstream technology for the navigation of low Earth orbit (LEO) satellites. The initialization of orbit parameters is a key prerequisite for LEO POD processing. For a LEO satellite equipped with a GNSS receiver, sufficient discrete kinematic positions can be obtained easily by processing space-borne GNSS data, and its orbit parameters can thus be estimated directly in iterative manner. This method of direct iterative estimation is called as the direct approach, which is generally considered highly reliable, but in practical applications it has risk of failure. Stability analyses demonstrate that the direct approach is sensitive to oversized errors in the starting velocity vector at the reference time, which may lead to large errors in design matrix because the reference orbit may be significantly distorted, and eventually cause the divergence of the orbit parameter estimation. In view of this, a more reliable method, termed the progressive approach, is presented in this paper. Instead of estimating the orbit parameters directly, it first fits the discrete kinematic positions to a reference ephemeris in the form of the GNSS broadcast ephemeris, which construct a reference orbit that is smooth and close to the true orbit. Based on the reference orbit, the starting orbit parameters are computed in sufficient accuracy, and then the final orbit parameters are estimated with a high accuracy by using discrete kinematic positions as measurements. The stability analyses show that the design matrix errors are reduced in the progressive approach, which would assure more robust orbit parameter estimation than the direct estimation approach. Various orbit initialization experiments are performed on the KOMPSAT-5 and FY3C satellites. The results have fully verified the high reliability of the proposed progressive approach.

Experimental and kinetic study for lead removal via photosynthetic consortia using genetic algorithms to parameter estimation

2017 ◽  
Vol 25 (22) ◽  
pp. 21286-21295 ◽  
Author(s):  
Dulce Jazmín Hernández-Melchor ◽  
Pablo A. López-Pérez ◽  
Sergio Carrillo-Vargas ◽  
Alvaro Alberto-Murrieta ◽  
Evanibaldo González-Gómez ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Parameter Estimation ◽  
Kinetic Study ◽  
Lead Removal

scholarly journals A mean-squared-error condition for weighting ionospheric delays in GNSS baselines

2021 ◽  
Vol 95 (11) ◽  
Author(s):  
P. J. G. Teunissen ◽  
A. Khodabandeh
Keyword(s):  
Parameter Estimation ◽  
Mean Squared Error ◽  
Present Contribution ◽  
Squared Error ◽  
Fixed Solution ◽  
Error Condition

AbstractAlthough ionosphere-weighted GNSS parameter estimation is a popular technique for strengthening estimator performance in the presence of ionospheric delays, no provable rules yet exist that specify the needed weighting in dependence on ionospheric circumstances. The goal of the present contribution is therefore to develop and present the ionospheric conditions that need to be satisfied in order for the ionosphere-weighted solution to be mean squared error (MSE) superior to the ionosphere-float solution. When satisfied, the presented conditions guarantee from an MSE performance view, when (a) the ionosphere-fixed solution can be used, (b) the ionosphere-float solution must be used, or (c) an ionosphere-weighted solution can be used.

scholarly journals Parameter Estimation in a Three-dimensional Wind Field Model Using Genetic Algorithms

2002 ◽  
pp. 950-959 ◽  
Author(s):  
Eduardo Rodríguez ◽  
Gustavo Montero ◽  
Rafael Montenegro ◽  
José María Escobar ◽  
José María González-Yuste
Keyword(s):  
Genetic Algorithms ◽  
Parameter Estimation ◽  
Wind Field ◽  
Field Model ◽  
Three Dimensional ◽  
Wind Field Model

Ultra-Wideband Chaos Life-Detection Radar with Sinusoidal Wave Modulation

2017 ◽  
Vol 27 (13) ◽  
pp. 1730046 ◽  
Author(s):  
Hang Xu ◽  
Ying Li ◽  
Jianguo Zhang ◽  
Hong Han ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Vital Signs ◽  
Radar System ◽  
Ultra Wideband ◽  
Thick Wall ◽  
Sinusoidal Wave ◽  
Echo Signal ◽  
Range Resolution ◽  
Life Detection ◽  
Lock In

We propose and experimentally demonstrate an ultra-wideband (UWB) chaos life-detection radar. The proposed radar transmits a wideband chaotic-pulse-position modulation (CPPM) signal modulated by a single-tone sinusoidal wave. A narrow-band split ring sensor is used to collect the reflected sinusoidal wave, and a lock-in amplifier is utilized to identify frequencies of respiration and heartbeat by detecting the phase change of the sinusoidal echo signal. Meanwhile, human location is realized by correlating the CPPM echo signal with its delayed duplicate and combining the synthetic aperture technology. Experimental results demonstrate that the human target can be located accurately and his vital signs can be detected in a large dynamic range through a 20-cm-thick wall using our radar system. The down-range resolution is 15[Formula: see text]cm, benefiting from the 1-GHz bandwidth of the CPPM signal. The dynamic range for human location is 50[Formula: see text]dB, and the dynamic ranges for heartbeat and respiration detection respectively are 20[Formula: see text]dB and 60[Formula: see text]dB in our radar system. In addition, the bandwidth of the CPPM signal can be adjusted from 620[Formula: see text]MHz to 1.56[Formula: see text]GHz to adapt to different requirements.

Sign in / Sign up

Export Citation Format

Share Document