scholarly journals Robust SCKF Filtering Method for MINS/GPS In-Motion Alignment

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2597
Author(s):  
Huanrui Zhang ◽  
Xiaoyue Zhang
Keyword(s):  
Physical Simulation ◽  
Estimation Error ◽  
Random Noise ◽  
Error Variance ◽  
Residual Error ◽  
Shaking Table ◽  
Variance Matrix ◽  
System Noise ◽  
Cubature Kalman Filter ◽  
Fading Factor

This paper presents a novel multiple strong tracking adaptive square-root cubature Kalman filter (MSTASCKF) based on the frame of the Sage–Husa filter, employing the multi-fading factor which could automatically adjust the Q value according to the rapidly changing noise in the flight process. This filter can estimate the system noise in real-time during the filtering process and adjust the system noise variance matrix Q so that the filtering accuracy is not significantly reduced with the noise. At the same time, the residual error in the filtering process is used as a measure of the filtering effect, and a multiple fading factor is introduced to adjust the posterior error variance matrix in the filtering process, so that the residual error is always orthogonal and the stability of the filtering is maintained. Finally, a vibration test is designed which simulates the random noise of the short-range guided weapon in flight through the shaking table and adds the noise to the present simulation trajectory for semi-physical simulation. The simulation results show that the proposed filter can significantly reduce the attitude estimation error caused by random vibration.

scholarly journals An analytical approach to determine the optimal duration of continuous glucose monitoring data required to reliably estimate time in hypoglycemia

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Nunzio Camerlingo ◽  
Martina Vettoretti ◽  
Andrea Facchinetti ◽  
Giovanni Sparacino ◽  
Julia K. Mader ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Estimation Error ◽  
Glucose Monitoring ◽  
Population Data ◽  
Random Variable ◽  
Error Variance ◽  
Patient Specific ◽  
Diabetes Research ◽  
Time In Range

Abstract Diabetes is a chronic metabolic disease that causes blood glucose (BG) concentration to make dangerous excursions outside its physiological range. Measuring the fraction of time spent by BG outside this range, and, specifically, the time-below-range (TBR), is a clinically common way to quantify the effectiveness of therapies. TBR is estimated from data recorded by continuous glucose monitoring (CGM) sensors, but the duration of CGM recording guaranteeing a reliable indicator is under debate in the literature. Here we framed the problem as random variable estimation problem and studied the convergence of the estimator, deriving a formula that links the TBR estimation error variance with the CGM recording length. Validation is performed on CGM data of 148 subjects with type-1-diabetes. First, we show the ability of the formula to predict the uncertainty of the TBR estimate in a single patient, using patient-specific parameters; then, we prove its applicability on population data, without the need of parameters individualization. The approach can be straightforwardly extended to other similar metrics, such as time-in-range and time-above-range, widely adopted by clinicians. This strengthens its potential utility in diabetes research, e.g., in the design of those clinical trials where minimal CGM monitoring duration is crucial in cost-effectiveness terms.

Gravity gradiometer survey errors

Geophysics ◽  
1988 ◽  
Vol 53 (10) ◽  
pp. 1355-1361 ◽  
Author(s):  
Steven J. Brzezowski ◽  
Warren G. Heller
Keyword(s):  
Total Error ◽  
Estimation Algorithm ◽  
Error Variance ◽  
Downward Continuation ◽  
Gravity Gradiometer ◽  
Limited Data ◽  
Gravity Disturbance ◽  
System Noise ◽  
Disturbance Vector ◽  
Moving Base

Gradiometer system noise, sampling effects, downward continuation, and limited data extent are the important contributors to moving‐base gravity gradiometer survey error. We apply a two‐dimensional frequency‐domain approach in simulations of several sets of airborne survey conditions to assess the significance of the first two sources. A special error allocation technique is used to account for the downward continuation and limited extent effects. These two sources cannot be modeled adequately as measurement noise in a linear error estimation algorithm. For a typical characterization of the Earth’s gravity field, our modeling indicates that limited data extent generally contributes about one‐half of the total error variance associated with recovery of the gravity disturbance vector at the Earth’s surface; gradiometer system noise typically contributes about one‐third. However, sampling effects are also very important (and are controlled through the survey track spacing). A 5 km track spacing provides a reasonable tradeoff between survey cost and errors due to track spacing. Furthermore, our results indicate that a moving‐base gravity gradiometer system can recover each component of the gravity disturbance vector with an rms accuracy better than 1.0 mGal.

scholarly journals A comparison of empirical BLUP with different considerations of residual error variance for genotype evaluation of multi-location trials

2019 ◽  
Vol 17 (1) ◽  
pp. e0701
Author(s):  
Renhe Zhang ◽  
Xiyuan Hu
Keyword(s):  
Mixed Model ◽  
Type I Error ◽  
Block Design ◽  
Model Fitting ◽  
Covariance Structure ◽  
Error Variance ◽  
Error Rates ◽  
Residual Error ◽  
Type I ◽  
The North

AbstractThe empirical best linear unbiased prediction (eBLUP) is usually based on the assumption that the residual error variance (REV) is homogenous. This may be unrealistic, and therefore limits the accuracy of genotype evaluations for multi-location trials, where the REV often varies across locations. The objective of this contribution was to investigate the direct implications of the eBLUP with different considerations about REV based on the mixed model for evaluation of genotype simple effects (i.e. genotype effects at individual locations). A series of 14 multi-location trials from a rape-breeding program in the north of China were simultaneously analyzed from 2012 to 2014 using a randomized complete block design at each location. The results showed that the model with heterogeneous REV was more appropriate than the one with homogeneous REV in all of the trials according to model fitting statistics. Whether the REV differences across locations were accounted for in the analysis procedure influenced the variance estimate of related random effects and testing of the variance of genotype-location (G-L) interactions. Ignoring REV differences by use of the eBLUP could result not only in an inflation or deflation of statistical Type I error rates for pair-wise testing but also in an inaccurate ranking of genotype simple effects for these trials. Therefore, it is suggested that in application of the eBLUP for evaluation of genotype simple effects in multi-location trials, the heterogeneity of REV should be accounted for based on mixed model approaches with appropriate variance-covariance structure.

scholarly journals Impact of Uncertain Channel Estimation and Outdated Feedback on the Adaptive M-PSK Modulation

2014 ◽  
Vol 10 (1) ◽  
pp. 51
Author(s):  
Mohamed L. Ammari ◽  
Francois Gagnon
Keyword(s):  
Channel Estimation ◽  
Estimation Error ◽  
Error Variance ◽  
Channel Estimation Error ◽  
Closed Form Expression ◽  
Modulation Scheme ◽  
Form Expression ◽  
Pilot Symbols ◽  
Modulation Techniques ◽  
Modulation Level

This paper investigates an adaptive M-ary phaseshiftkeying (M-PSK) modulation scheme over Rayleigh flatfading channels. The data rate is adapted according to thechannel state. At the receiver, the fading is estimated using pilot symbols. To cancel the channel impact, we correct the received signal by dividing it by the estimated value of the fading. So, we propose to adjust the modulation level by examining the statistics of the corrected signal. In contrast to the previous works on the adaptive M-PSK modulation techniques, our modulation switching protocol takes into account the channel estimation error variance. Moreover, we derive a new closed-form expression for the average bit error rate of the considered system.

scholarly journals Machine Learning for Modeling the Singular Multi-Pantograph Equations

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1041
Author(s):  
Amirhosein Mosavi ◽  
Manouchehr Shokri ◽  
Zulkefli Mansor ◽  
Sultan Noman Qasem ◽  
Shahab S. Band ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fuzzy Logic ◽  
Intelligent Systems ◽  
Estimation Error ◽  
Computational Cost ◽  
Accurate Solution ◽  
Machine Learning Algorithms ◽  
Novel Approach ◽  
Cubature Kalman Filter

In this study, a new approach to basis of intelligent systems and machine learning algorithms is introduced for solving singular multi-pantograph differential equations (SMDEs). For the first time, a type-2 fuzzy logic based approach is formulated to find an approximated solution. The rules of the suggested type-2 fuzzy logic system (T2-FLS) are optimized by the square root cubature Kalman filter (SCKF) such that the proposed fineness function to be minimized. Furthermore, the stability and boundedness of the estimation error is proved by novel approach on basis of Lyapunov theorem. The accuracy and robustness of the suggested algorithm is verified by several statistical examinations. It is shown that the suggested method results in an accurate solution with rapid convergence and a lower computational cost.

Assessing estimation error in a tracking error variance minimisation framework

1998 ◽  
Vol 6 (1-2) ◽  
pp. 175-192
Author(s):  
David M. Walsh ◽  
Kathleen D. Walsh ◽  
John P. Evans
Keyword(s):  
Estimation Error ◽  
Tracking Error ◽  
Error Variance

Adaptive estimation algorithm of boost-phase trajectory using binary asynchronous observation

2016 ◽  
Vol 230 (14) ◽  
pp. 2661-2672 ◽  
Author(s):  
Nan Wu ◽  
Lei Chen ◽  
Yongjun Lei ◽  
Fankun Meng
Keyword(s):  
State Estimation ◽  
Phase Trajectory ◽  
Prior Information ◽  
Estimation Error ◽  
The State ◽  
Estimation Accuracy ◽  
Unknown Input ◽  
Noise Variance ◽  
Process Noise ◽  
Variance Matrix

A kind of adaptive filter algorithm based on the estimation of the unknown input is proposed for studying the adaptive adjustment of process noise variance of boost phase trajectory. Polynomial model is used as the motion model of the boost trajectory, truncation error is regarded as an equivalent to the process noise and the unknown input and process noise variance matrix is constructed from the estimation value of unknown input according to the quantitative relationship among the unknown input, the state estimation error, and optimal process noise variance. The simulation results show that in the absence of prior information, the unknown input is estimated effectively in terms of magnitude, a positive definite matrix of process noise covariance which is close to the optimal value is constructed real-timely, and the state estimation error approximates the error lower bound of the optimal estimation. The estimation accuracy of the proposed algorithm is similar to that of the current statistical model algorithm using accurate prior information.

Ultra-tight GPS/IMU Integration based Long-Range Rocket Projectile Navigation

2016 ◽  
Vol 66 (1) ◽  
pp. 64 ◽  
Author(s):  
Handong Zhao ◽  
Zhipeng Li
Keyword(s):  
Kalman Filter ◽  
Long Range ◽  
High Performance ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Position Information ◽  
Cubature Kalman Filter ◽  
Carrier Phase Tracking ◽  
Output Information ◽  
Navigation Accuracy

<p>Accurate navigation is important for long-range rocket projectile’s precise striking. For getting a stable and high-performance navigation result, a ultra-tight global position system (GPS), inertial measuring unit integration (IMU)-based navigation approach is proposed. In this study, high-accuracy position information output from IMU in a short time to assist the carrier phase tracking in the GPS receiver, and then fused the output information of IMU and GPS based on federated filter. Meanwhile, introduced the cubature kalman filter as the local filter to replace the unscented kalman filter, and improved it with strong tracking principle, then, improved the federated filter with vector sharing theory. Lastly simulation was carried out based on the real ballistic data, from the estimation error statistic figure. The navigation accuracy of the proposed method is higher than traditional method.</p><p><strong>Defence Science Journal, Vol. 66, No. 1, January 2016, pp. 64-70, DOI: http://dx.doi.org/10.14429/dsj.66.8326</strong></p>

scholarly journals Genetic and Environmental Stability of Neuroticism From Adolescence to Adulthood

2015 ◽  
Vol 18 (6) ◽  
pp. 746-754 ◽  
Author(s):  
Michel G. Nivard ◽  
Christel M. Middeldorp ◽  
Conor V. Dolan ◽  
Dorret I. Boomsma
Keyword(s):  
Rank Order ◽  
Environmental Influences ◽  
Genetic Correlations ◽  
Error Variance ◽  
Environmental Stability ◽  
Environmental Variance ◽  
Twin Design ◽  
Environmental Correlations ◽  
Variance Matrix ◽  
Simplex Model

Longitudinal studies of neuroticism have shown that, on average, neuroticism scores decrease from adolescence to adulthood. The heritability of neuroticism is estimated between 0.30 and 0.60 and does not seem to vary greatly as a function of age. Shared environmental effects are rarely reported. Less is known about the role of genetic and environmental influences on the rank order stability of neuroticism in the period from adolescence to adulthood. We studied the stability of neuroticism in a cohort sequential (classical) twin design, from adolescence (age 14 years) to young adulthood (age 32 years). A genetic simplex model that was fitted to the longitudinal neuroticism data showed that the genetic stability of neuroticism was relatively high (genetic correlations between adjacent age bins >0.9), and increased from adolescence to adulthood. Environmental stability was appreciably lower (environmental correlations between adjacent age bins were between 0.3 and 0.6). This low stability was largely due to age-specific environmental variance, which was dominated by measurement error. This attenuated the age-to-age environmental correlations. We constructed an environmental covariance matrix corrected for this error, under the strong assumption that all age-specific environmental variance is error variance. The environmental (co)variance matrix corrected for attenuation revealed highly stable environmental influences on neuroticism (correlations between adjacent age bins were between 0.7 and 0.9). Our results indicate that both genetic and environmental influences have enduring effects on individual differences in neuroticism.

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