Random Drift Error Research Articles

Demonstration of photonics-based D-band integrated localization and communication

The terahertz spectrum has the ability to provide high-speed communication and millimeter-level resolution. As a result, terahertz-integrated sensing and communication (ISAC) has been identified as a key enabler for 6G wireless networks. This work discusses a photonics-based D-band communication system for integrated high-resolution localization and high-speed wireless communication. Our empirical results show that a communication rate of 5 Gbps over a distance of 1.5 m and location identification of the target with millimeter-level (<4mm) range resolution can be conducted simultaneously using the same signal. We also show that the error due to the thickness of the beam splitter can be eliminated, while the quantization error and the random drift errors are the limiting factors of the resolution achieved. This experimental demonstration using D-band communication indicates that terahertz ISAC can be realized for 6G networks while considering the underlying system restrictions (e.g., bandwidth limit and lens diameter).

Large changes in detected selection signatures after a selection limit in mice bred for voluntary wheel-running behavior.

In various organisms, sequencing of selectively bred lines at apparent selection limits has demonstrated that genetic variation can remain at many loci, implying that evolution at the genetic level may continue even if the population mean phenotype remains constant. We compared selection signatures at generations 22 and 61 of the "High Runner" mouse experiment, which includes 4 replicate lines bred for voluntary wheel-running behavior (HR) and 4 non-selected control (C) lines. Previously, we reported multiple regions of differentiation between the HR and C lines, based on whole-genome sequence data for 10 mice from each line at generation 61, which was >31 generations after selection limits had been reached in all HR lines. Here, we analyzed pooled sequencing data from ~20 mice for each of the 8 lines at generation 22, around when HR lines were reaching limits. Differentiation analyses of allele frequencies at ~4.4 million SNP loci used the regularized T-test and detected 258 differentiated regions with FDR = 0.01. Comparable analyses involving pooling generation 61 individual mouse genotypes into allele frequencies by line produced only 11 such regions, with almost no overlap among the largest and most statistically significant peaks between the two generations. These results implicate a sort of "genetic churn" that continues at loci relevant for running. Simulations indicate that loss of statistical power due to random genetic drift and sampling error are insufficient to explain the differences in selection signatures. The 13 differentiated regions at generation 22 with strict culling measures include 79 genes related to a wide variety of functions. Gene ontology identified pathways related to olfaction and vomeronasal pathways as being overrepresented, consistent with generation 61 analyses, despite those specific regions differing between generations. Genes Dspp and Rbm24 are also identified as potentially explaining known bone and skeletal muscle differences, respectively, between the linetypes.

Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN

Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s2 to −1.2646 × 10−7 m/s2, while the variance decreases from 0.0133 m/s2 to 1.0975 × 10−5 m/s2. In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals.

Online diagnosis algorithm for random drift error in fiber optic current sensor

Focusing on the problem of random drift error in Fiber Optic Current Sensor (FOCS), a random drift error extraction algorithm of FOCS based on optimal wavelet packet and Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) is proposed. Firstly, the characteristic of FOCS signal is analyzed, and the wavelet packet multi-resolution error signals are extracted. Secondly, since the time domain span of the random drift error is large and the short-term characteristics are not obvious, the output signal of the random drift error is extracted by the span interval. The low-frequency mean sequence is constructed by selecting mean parameters according to relevant evaluation indicators. Thirdly, segmentation sequence to construct signal database. The error signal prediction model based on LSTM is constructed by using historical data. Finally, current working state of the device is judged by the model prediction signal and SVM, then, random drift error prediction function is achieved. Experiment results show that the wavelet packet decomposition can extract equipment fault signal effectively. The prediction effect of LSTM model is better than BP neural network model. The LSTM-SVM model can be used to realize the state monitoring and fault warning for the random drift error of FOCS.

Evaluation of Matured Larval Weight and Larval Duration of New Breeding Lines of Bombyx mori L

We planned to conduct this study with the main aim to develop bivoltine breeds for our tropical climatic conditions by using silkworm breeds with known genetic backgrounds (KA, NB18 and PM) in various hybrid combinations and incorporating them over generations, followed by backcrossing and adequate selection of different generations with the objective of profitability and productivity. The isolated Bivoltin lines (R1 and R2) were reared with their parental races at different times of the year to evaluate their stability in the expression of commercial traits. For the present breeding program, the purebred Bivoltine Kalimpong-A (KA), which spin white oval cocoons, New Bivoltine18 (NB18) white cocoons with rotating dumbbells and Multivoltine Pure Mysore (PM), the yellow pointed cocoons of the mulberry silkworm Bombyx mori L., Selected. One-way and three-way crosses were made using the above three breeds. The first single cross comprised KA females and PM males. The second unique cross comprised NB18 females and PM males. Selection was performed at the egg, larva, pupal, and cocoon stages over the course to determine the desired traits. The offspring of F from the respective crosses were backcrossed with their respective bivoltine males to improve commercial traits. Heterosis in the F1 generations of crosses, including NB18 and PM, was determined by the mean score of the parents (MPV) and the best score of the parents (BPV). A significant test for heterosis was performed using a standard ANOVA table. Based on the results of our study, it was found that the performance of the characters, viz. The weight of mature larvae and the duration of the larvae over generations do not simply increase or decrease regularly, but fluctuate irregularly. The reason for this variation may be due to random genetic drift, sampling errors in estimating generational means, selection pressures, and environmental factors. Therefore, inbreeding variations due to random drift and sampling errors could be reduced by increasing the number sampled and selected.

Modeling Inertial Sensor Errors Using Allan Variance (Dept.E)

Inertial navigation systems (INS) can provide high-accuracy position, velocity, and attitude information over short time periods. However, their accuracy rapidly degrades with time due to inertial sensor errors. To damp down the error growth, the INS sensor errors should be properly estimate and compensated before the inertial data are involved in the navigation computation. Therefore, appropriate modeling of the INS sensor errors is a necessity. Allan Variance (AV) is a simple and efficient method for modeling and verifying these errors by representing the root mean square (RMS) random drift error as a function of averaging time. Allan variance can provide information on the types and magnitude of the various error terms. This paper uses the AV technique to analyze and model different types of random errors residing in the measurements of Micro Electro Mechanical System (MEMS) based inertial sensors. The derived stochastic error model will be further included in the INS error model for intergrade navigation system, once the correctness of the model is verified. Finally , the paper presents the test results and model validation.

Adaptive Kalman filter based on random-weighting estimation for denoising the fiber-optic gyroscope drift signal.

Reducing and suppressing the random noise and drift error is a critical task in an interferometric fiber-optic gyroscope (IFOG). In this paper, an improved adaptive Kalman filter (KF) based on innovation and random-weighting estimation (RWE) is proposed to denoise IFOG signals in both static and dynamic conditions. The covariance matrix of the innovation sequence is estimated using the random-weighted-average window. The KF gain is then adaptively updated by the estimated covariance matrix. To decrease the inertia of KF response in the dynamic condition, the covariance matrix of process noise is adjusted when discontinuous IFOG signals are detected by the innovation-based chi-square test method. The proposed algorithm is applied for denoising IFOG static and dynamic signals. Allan variance is used to evaluate the denoise performance for static signals. In the dynamic condition, root-mean-square error is considered as the performance indicator. Quantitative results reveal that the proposed algorithm is competitive for denoising IFOG signals when compared with conventional KF, RWE-based gain-adjusted adaptive KF, and RWE-based moving average double-factor adaptive KF.

A Gyroscope Signal Denoising Method Based on Empirical Mode Decomposition and Signal Reconstruction.

To suppress the random drift error of a gyroscope signal, this paper proposes a novel denoising method, which is based on processing the intrinsic mode functions (IMFs) obtained by empirical mode decomposition (EMD). Considering that a gyroscope signal contains colored noise in addition to Gaussian white noise, fractal Gaussian noise (FGN) was introduced to quantify the noise in the gyroscope data. The proposed denoising method combines the FGN energy model and the modified method of Hausdorff distance (HD) to adaptively divide the IMFs into three categories (pure noise, pure information, and mixed components of noise and information). Then, the information IMFs and the mixed components after thresholding were selected to give the optimal signal reconstruction. Static and dynamic signal tests of the fiber optic gyroscope (FOG) were carried out to illustrate the performance of the proposed method, and compared with other traditional EMD denoising methods, such as the Euclidean norm measure method (EMD--norm) and the sliding average filtering method (EMD-SA). The results of the analysis of both the static and dynamic signal tests indicate the effectiveness of the proposed method.

Using AKF-PSR to Compensate Random Drift Errors of Low-Cost MEMS Gyroscopes

The random drift of a micro-electromechanical system (MEMS) gyroscope seriously affects its measurement accuracy. To model and compensate its random drift, the time series analysis method has widely been deployed, which, however, requires a large amount of data for pre-processing analysis and is unsuitable for real-time applications. This paper proposes a new random drift compensation method based on the adaptive Kalman filter (AKF) and phase space reconstruction (PSR). AKF is first designed to compensate the random drift of the low-cost MEMS gyroscope. The phase variables are then used as phase vectors via PSR. Experiments show that the proposed AKF-PSR method can effectively compensate the random drift of the gyroscope, and the standard deviation is reduced by half.

A novel edge-enabled SLAM solution using projected depth image information

Environmental mapping is the key step for mobile robots to perform tasks independently and perfectly. In recent years, visual SLAM, laser-based SLAM and simultaneous localization and mapping (SLAM) have aroused the interest of many people. Unfortunately, those technologies are not widely used, limited by the computational complexity, data processing and very low and predictable latency. This paper had mainly completed the following work and edge-enabled computing-based edge computing (Shi and Dustdar in Computer 49(5):78–81, 2016) is used as a solution to accelerate calculation. First of all, this research design works with inertial unit mobile robotic navigation systems, and all sensors are connected in edge layers in the framework of edge computing and explore the accelerometer, electronic compass, and gyroscope data. The accelerometer data are integrated using the Kalman filter data fusion algorithm to filter the random drift error caused by the gyroscope and the electronic compass. The state of the machine is determined by calculation of the corresponding attitude angle and position information. Second, a low-cost distance sensor is used to detect the depth and upload to the other fog node for computation. Next, the 3D point coordinate information is projected onto the two-dimensional coordinate extraction feature point to establish the feature map. Third, the extended Kalman filter SLAM is used to achieve simultaneous positioning and mapping. Finally, the method is validated in the experiment, proving that the method is feasible. The main improvement in this article is as follows: First, the multi-sensor data fusion algorithm is used to reduce the positioning error. Second, we use low-cost distance sensors to measure the depth of the model environment and reduce the cost. Third, we would take advantage of translating the three-dimensional depth information into a flat two-dimensional projection information to reduce the calculation of load and computing time. Fourth, our computation is distributed in different layers and focuses on edge-enabled platform to decrease the latency and redundancy.

An EMD-MRLS de-noising method for fiber optic gyro Signal

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems (INS), a novel de-noising method is proposed in this paper, which based on an improved empirical mode decomposition (EMD) and modified recursive least square (RLS). Referred to as the EMD-MRLS method, it is developed to decompose the FOG outputs into a number of intrinsic mode functions (IMFs) after which mode manipulations are divided into three parts, noise IMFs, mixed IMFs, and information IMFs by two index parameters based on mahalanobis distance (MD). A modified RLS algorithm is then employed to process the mixed IMFs, from which the refined IMFs components are reconstructed to produce the final de-noising results. Other traditional methods, such as RLS, and EMD are investigated to provide a comparison with the proposed one through both simulated signals and experimental FOG outputs. Compared with the EMD method, the results show that the error mean is reduced by 27.01%, and the horizontal position error is reduced by 106.75m, when the INS lasts for 1000s.

An Autonomous Vehicle Navigation System Based on Inertial and Visual Sensors.

The strapdown inertial navigation system (SINS) is widely used in autonomous vehicles. However, the random drift error of gyroscope leads to serious accumulated navigation errors during long continuous operation of SINS alone. In this paper, we propose to combine the Inertial Measurement Unit (IMU) data with the line feature parameters from a camera to improve the navigation accuracy. The proposed method can also maintain the autonomy of the navigation system. Experimental results show that the proposed inertial-visual navigation system can mitigate the SINS drift and improve the accuracy, stability, and reliability of the navigation system.

UAV attitude estimation based on the dual filtering methods

PurposeA toy UAV performs tumbling, rolling, racing and other complex activities. It is based on low-cost hardware and hence requires a better algorithm to estimate the attitudes more accurately with low power consumption. The proposed technique based on optimized Madgwick filter and moving average filter (MAF) ensures improved convergence speed in estimating the attitude, achieves higher accuracy and provides robustness and stability of the toy UAV. The paper aims to discuss this issue.Design/methodology/approachTraditional methods are prone to problems such as slow convergence speed and errors in calculation of the attitude angles. These errors cause the vehicle to drift and tremble, thus affecting the overall stability of the vehicle. The proposed method combines the features of optimized Madgwick filter and MAF to provide better accuracy, achieved through the fusion of gyroscope and accelerometer data, and zero correction to eliminate the random drift error of the gyroscope and removal of high-frequency interference by MAF of the accelerometer data. The experimental results on actual flight data showed that the method was better than the conventional Madgwick and Mahony complementary filters.FindingsThe performance of the proposed method was analyzed by estimating the pitch and roll angles under the static and dynamic condition of the toy UAV. The results were compared with two traditional methods: Madgwick and Mahony complement filter. In the static condition, the variance and average error while estimating the attitudes was comparatively lower than the traditional method. For the dynamic conditions, the convergence time to achieve a prescribed swing angle was again lower than the traditional method. From these two experiments, it can be seen that the proposed method provides better attitude estimation at lower computation time.Originality/valueThe proposed method combines the optimized Madgwick filter and MAF to accuracy estimate the attitude of toy UAV. The algorithm mainly suits the toy UAVs which are based on low-cost hardware and require better control systems to ensure stability of the vehicle. The experimental results on real flight data illustrate that the method not only improves the convergence speed in estimating the attitude angle for large maneuvers of the toy UAV, but also achieves higher accuracy in the attitude estimation, thus ensuring the robustness and stability of the UAV.

Research on the modeling of random drift error and filtering technology of low cost mems gyroscope

Research on the modeling of random drift error and filtering technology of low cost mems gyroscope

Research on Time-series Modeling and Filtering Methods for MEMS Gyroscope Random Drift Error

The precision of MEMS gyroscope is reduced by random drift error. This paper applied time series analysis to model random drift error of MEMS gyroscope. Based on the model established, Kalman filter was employed to compensate for the error. To overcome the disadvantages of conventional Kalman filter, Sage-Husa adaptive filtering algorithm was utilized to improve the accuracy of filtering results and the orthogonal property of innovation in the process of filtering was utilized to deal with outliers. The results showed that, compared with conventional Kalman filter, the modified filter can not only enhance filter accuracy, but also resist to outliers and this assured the stability of filtering thus improving the performance of gyroscopes.

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

Adaptive sampling strong tracking scaled unscented Kalman filter for denoising the fibre optic gyroscope drift signal

The interferometric fibre optic gyroscope (IFOG) is a kernel component of strap down inertial navigation system (SINS) for providing angular rotation of any moving object. The behaviour of SINS degrades because of noise and random drift errors of the IFOG sensor. This study proposes a hybrid of adaptive sampling strong tracking algorithm (ASSTA) and scaled unscented Kalman filter algorithm for denoising the IFOG signal. In this algorithm, the state error covariance (P) is updated by using a suboptimal fading factor based on the innovation sequence followed by the ASSTA method. The proposed algorithm is applied for denoising the IFOG signal under static and dynamic environment to crush the random drift errors and noises. Allan variance analysis is used for analysing the efficiency of algorithms. Simulation results depict that the suggested algorithm is suitable for reducing drifts of the gyro signal.

Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope.

In order to improve the atom spin gyroscope's operational accuracy and compensate the random error caused by the nonlinear and weak-stability characteristic of the random atomic spin gyroscope (ASG) drift, the hybrid random drift error model based on autoregressive (AR) and genetic programming (GP) + genetic algorithm (GA) technique is established. The time series of random ASG drift is taken as the study object. The time series of random ASG drift is acquired by analyzing and preprocessing the measured data of ASG. The linear section model is established based on AR technique. After that, the nonlinear section model is built based on GP technique and GA is used to optimize the coefficients of the mathematic expression acquired by GP in order to obtain a more accurate model. The simulation result indicates that this hybrid model can effectively reflect the characteristics of the ASG's random drift. The square error of the ASG's random drift is reduced by 92.40%. Comparing with the AR technique and the GP + GA technique, the random drift is reduced by 9.34% and 5.06%, respectively. The hybrid modeling method can effectively compensate the ASG's random drift and improve the stability of the system.

Modeling and Optimizing of Random Gyro Drift Based on AR and GP

Gyroscope is the key component in an Inertial Navigation System (INS). It depends on the precision of the INS. The nonlinear random drift error model based on autoregressive (AR) and genetic programming (GP) was established. The linear model is established based on AR technique. After that, the nonlinear model is built based on GP technique. The result indicates that the square error of the random gyro drift is reduced by 74.5%. The hybrid modeling method can effectively compensate the random gyro drift and improve the stability of the system.

A Novel Filtering Method for the Random Drift of MEMS Gyroscope

In engineering application, the nonlinearity effect of the environment noise is inconsistent with the successive starting state of MEMS gyroscope which will induce the random drifts. It manifests as the weak nonlinearity, non stability and slow time varying which cannot be compensated by the conventional method. In order to overcome the problems of the great random drift error model established based on the time series for MEMS gyroscope and the non Gaussian noise, the method of Iteration Unscented Kalman Particle Filter (IUKPF) is proposed in this paper. This method is based on the Particle Filter combing the Unscented Transformation (UT) with Iteration Kalman Filter (IKF), and it solved the instability of the precision for the conventional filtering methods and the degradation for the weight of the particle filter. The filtering result shows that the method of IUKPF can effectively restrain the random drift error under nonlinear and non Gaussian noise. The standard deviation for the output noise of MEMS gyroscope has decreased 81.9% by IUKPF which verifies the efficiency and superiority of this method.