Influence of A/D Quantization in an Interpolated DFT Based System of Power Control with A Small Delay

Abstract Fast and accurate grid signal frequency estimation is a very important issue in the control of renewable energy systems. Important factors that influence the estimation accuracy include the A/D converter parameters in the inverter control system. This paper presents the influence of the number of A/D converter bits b, the phase shift of the grid signal relative to the time window, the width of the time window relative to the grid signal period (expressed as a cycle in range (CiR) parameter) and the number of N samples obtained in this window with the A/D converter on the developed estimation method results. An increase in the number b by 8 decreases the estimation error by approximately 256 times. The largest estimation error occurs when the signal module maximum is in the time window center (for small values of CiR) or when the signal value is zero in the time window center (for large values of CiR). In practical applications, the dominant component of the frequency estimation error is the error caused by the quantization noise, and its range is from approximately 8×10-10 to 6×10-4.

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Extraction and Filter Algorithm of Roll Angular Rate for High Spinning Projectiles

Mathematical Problems in Engineering ◽

10.1155/2019/3181727 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15

Author(s):

Hui Zhao ◽

Zhong Su ◽

Fuchao Liu ◽

Chao Li ◽

Qing Li ◽

...

Keyword(s):

Measurement Problem ◽

Frequency Estimation ◽

Angular Rate ◽

Estimation Accuracy ◽

Signal Frequency ◽

Average Error ◽

Accurate Estimation ◽

Test Results ◽

Narrowband Signal ◽

Spinning Projectile

The accurate measurement of roll angular rate for high spinning projectile has long been a challenging problem. Aiming to obtain the accurate roll angular rate of high spinning projectile, a novel extraction and filter algorithm, BSCZT-KF, is proposed in this paper. Firstly, a compound angular motion model of high spinning projectile is established. According to the model, we translate the roll angular rate measurement problem into a frequency estimation problem. Then the improved CZT algorithm, BSCZT, was employed to realize an accurate estimation of the narrowband signal frequency. Combined with the peak detection method, the BSCZT-KF algorithm is presented to further enhance the frequency estimation accuracy and the real-time performance. Finally, two sets of actual flight tests were conducted to verify the effectiveness and accuracy of the algorithm. The test results show that the average error of estimated roll angular rate is about 0.095% of the maximum of roll angular rate. Compared with the existing methods, the BSCZT-KF has the highest frequency estimation accuracy for narrowband signal.

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Digital camera-based spectral estimation in open environment based on imaging condition correction

Color and Imaging Conference ◽

10.2352/issn.2169-2629.2020.28.55 ◽

2020 ◽

Vol 2020 (28) ◽

pp. 347-350

Author(s):

Jinxing Liang ◽

Kaida Xiao

Keyword(s):

Spectral Estimation ◽

Estimation Method ◽

Digital Camera ◽

Estimation Accuracy ◽

Adaptive Model ◽

Imaging Condition ◽

Practical Applications ◽

Open Environment ◽

Imaging Conditions ◽

Current Stage

Digital camera-based spectral estimation in open environment is a challenge in current stage. Although some methods have been proposed in recent years, the methods do not consider the exposure inconsistency between camera spectral characterization and spectral estimation applications, that makes the proposed method cannot for practical applications. We proposed here a spectral estimation method based on imaging condition correction of which can deal with the problem exist in current methods. Using the whiteboard and raw camera response, the imaging conditions of open environment is recorded and corrected to the reference imaging conditions, and the surface spectral of object is estimated using the established spectral estimation matrix in the reference imaging conditions. The proposed method in three application models are tested and compared. The result shows that the adaptive model for imaging condition correction gives the best spectral estimation accuracy.

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VIAE-Net: An End-to-End Altitude Estimation through Monocular Vision and Inertial Feature Fusion Neural Networks for UAV Autonomous Landing

Sensors ◽

10.3390/s21186302 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6302

Author(s):

Xupei Zhang ◽

Zhanzhuang He ◽

Zhong Ma ◽

Peng Jun ◽

Kun Yang

Keyword(s):

Neural Network ◽

Network Architecture ◽

Feature Fusion ◽

Estimation Error ◽

Estimation Method ◽

Signal Interference ◽

Estimation Accuracy ◽

Measurement Unit ◽

End To End ◽

Altitude Estimation

Altitude estimation is one of the fundamental tasks of unmanned aerial vehicle (UAV) automatic navigation, where it aims to accurately and robustly estimate the relative altitude between the UAV and specific areas. However, most methods rely on auxiliary signal reception or expensive equipment, which are not always available, or applicable owing to signal interference, cost or power-consuming limitations in real application scenarios. In addition, fixed-wing UAVs have more complex kinematic models than vertical take-off and landing UAVs. Therefore, an altitude estimation method which can be robustly applied in a GPS denied environment for fixed-wing UAVs must be considered. In this paper, we present a method for high-precision altitude estimation that combines the vision information from a monocular camera and poses information from the inertial measurement unit (IMU) through a novel end-to-end deep neural network architecture. Our method has numerous advantages over existing approaches. First, we utilize the visual-inertial information and physics-based reasoning to build an ideal altitude model that provides general applicability and data efficiency for neural network learning. A further advantage is that we have designed a novel feature fusion module to simplify the tedious manual calibration and synchronization of the camera and IMU, which are required for the standard visual or visual-inertial methods to obtain the data association for altitude estimation modeling. Finally, the proposed method was evaluated, and validated using real flight data obtained during a fixed-wing UAV landing phase. The results show the average estimation error of our method is less than 3% of the actual altitude, which vastly improves the altitude estimation accuracy compared to other visual and visual-inertial based methods.

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Tension Estimation Method for Cable With Damper Using Natural Frequencies

Frontiers in Built Environment ◽

10.3389/fbuil.2021.603857 ◽

2021 ◽

Vol 7 ◽

Author(s):

Aiko Furukawa ◽

Katsuya Hirose ◽

Ryosuke Kobayashi

Keyword(s):

Natural Frequencies ◽

Numerical Models ◽

Estimation Error ◽

Estimation Method ◽

Higher Order ◽

Bending Stiffness ◽

Estimation Accuracy ◽

Test Cases ◽

Numerical Verification ◽

Vibration Method

In the maintenance of cable structures, such as cable-stayed bridges and extra-dosed bridges, it is necessary to estimate the tension acting on the cables. The safety of a cable is confirmed by checking whether the tension acting on the cable is within the allowable value. In current Japanese practice, the tension of a cable is estimated using the vibration method or the higher-order vibration method, which considers the natural frequencies of the cable. However, in recent years, the aerodynamic vibration of cables caused by wind has become a problem owing to the recent increase in the cable length and low damping performance of the cable itself. To suppress the aerodynamic vibration of cables, dampers are installed onto the cables. Because the damper changes the cable’s natural frequencies, the vibration method and higher-order vibration method are inappropriate for measuring the tension of a cable with a damper. In this paper, a new tension estimation method for a cable with a damper is proposed. To model a cable with a tensioned Bernoulli-Euler beam, theoretical equations for estimating the natural frequencies were derived. The proposed method inversely estimates the tension and bending stiffness of the cable and damper parameters, simultaneously, from the natural frequencies. The validity of the proposed method was confirmed by conducting numerical simulations and experiments. In the numerical verification, the performance of the proposed method was investigated using 80 numerical models. In the experimental verification, the estimation accuracy of the proposed method was investigated by considering 16 test cases. Thus, it was confirmed that the tension estimation accuracy was high, whereas the bending stiffness and damper parameter estimation accuracy was unsatisfactory. The tension estimation error was within 10% in all experimental cases, and within 5% if two test cases are excluded. The results obtained by the numerical and experimental verifications confirmed the effectiveness of the proposed method in tension estimation.

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Online Capacity Estimation for Lithium-Ion Batteries Based on Semi-Supervised Convolutional Neural Network

World Electric Vehicle Journal ◽

10.3390/wevj12040256 ◽

2021 ◽

Vol 12 (4) ◽

pp. 256

Author(s):

Yi Wu ◽

Wei Li

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Lithium Ion Batteries ◽

Estimation Method ◽

Lithium Ion ◽

Estimation Methods ◽

Estimation Accuracy ◽

Capacity Estimation ◽

Practical Applications

Accurate capacity estimation can ensure the safe and reliable operation of lithium-ion batteries in practical applications. Recently, deep learning-based capacity estimation methods have demonstrated impressive advances. However, such methods suffer from limited labeled data for training, i.e., the capacity ground-truth of lithium-ion batteries. A capacity estimation method is proposed based on a semi-supervised convolutional neural network (SS-CNN). This method can automatically extract features from battery partial-charge information for capacity estimation. Furthermore, a semi-supervised training strategy is developed to take advantage of the extra unlabeled sample, which can improve the generalization of the model and the accuracy of capacity estimation even in the presence of limited labeled data. Compared with artificial neural networks and convolutional neural networks, the proposed method is demonstrated to improve capacity estimation accuracy.

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Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network

Applied Sciences ◽

10.3390/app11104564 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4564

Author(s):

Yongtao Shui ◽

Yu Wang ◽

Yu Li ◽

Yongzhi Shan ◽

Naigang Cui ◽

...

Keyword(s):

Parameter Estimation ◽

State Estimation ◽

Target Tracking ◽

Estimation Error ◽

Estimation Method ◽

Estimation Accuracy ◽

Consensus Method ◽

False Data Injection ◽

Radar Network ◽

Global Consistency

For target tracking in radar network, any anomaly in a part of the system can quickly spread over the network and lead to tracking failures. False data injection (FDI) attacks can damage the state estimation mechanism by modifying the radar measurements with unknown and time-varying attack variables, therefore making traditional filters inapplicable. To tackle this problem, we propose a novel consensus-based distributed state estimation (DSE) method for target tracking with FDI attacks, which is effective even when all radars are under FDI attacks. First, a real-time residual-based detector is introduced to the DSE framework, which can effectively detect FDI attacks by analyzing the statistical properties of the residual. Secondly, a simple yet effective attack parameter estimation method is proposed to provide attack parameter estimation based on a pseudo-measurement equation, which has the advantage of decoupled estimation of state and attack parameters compared with augmented state filters. Finally, for timely attack mitigation and global consistency achievement, a novel hybrid consensus method is proposed which can compensate for the estimation error caused by FDI attacks and provide estimation accuracy improvement. The simulation results show that the proposed solution is effective and superior to the traditional DSE method for target tracking in the presence of FDI attacks.

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Direct Instantaneous Frequency Rate Estimation to Improve the Carrier Estimation Performance in Mars Entry, Descent, and Landing Flight

Remote Sensing ◽

10.3390/rs10081259 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1259 ◽

Cited By ~ 1

Author(s):

Wanhong Hao ◽

Xiaowei Cui ◽

Jianguang Feng ◽

Guangliang Dong ◽

Zhiyong Zhu

Keyword(s):

Instantaneous Frequency ◽

Search Algorithm ◽

Frequency Estimation ◽

Estimation Method ◽

Current Approach ◽

Estimation Accuracy ◽

Cubic Phase ◽

Estimation Performance ◽

Frequency Rate ◽

Coherent Integration

This paper focuses on the carrier estimation performance improvement in Mars entry, descent, and landing (EDL) flights. Carrier reconstruction could be used for trajectory derivation and Martian atmosphere profile inversion, and is the critical information for mission operations, as it helps determine the flight status of the spacecraft, demodulate the downlink information. The current approach is maximum likelihood estimation based on a two-dimensional (2D) maximum energy search algorithm, which computes the grid energy over all the combinations of frequency cells and frequency rate cells among the search space. Although it has good performance on robust estimation, the frequency estimation accuracy is limited due to the short coherent integration. An instantaneous frequency rate tracking approach based on the cubic phase function (CPF) is proposed that directly estimates the instantaneous frequency rate over the frequency rate cells, followed by the frequency estimation among the frequency cells. A sequential estimation method is introduced to propose the sequential CPF statistics, which uses the a priori Doppler phase information to suppress the noise squaring loss inherent in the standard CPF statistics. Simulations have been made on the released Mars Science Laboratory EDL trajectory for the two approaches, which show that considerable estimation improvement has been achieved for aerobraking flight by the new algorithm.

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Threshold-based evolutionary magnitude estimation for an earthquake early warning system in the Sichuan–Yunnan region, China

Scientific Reports ◽

10.1038/s41598-020-78046-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Yuan Wang ◽

Shanyou Li ◽

Jindong Song

Keyword(s):

Magnitude Estimation ◽

Early Warning ◽

Time Window ◽

Estimation Method ◽

Earthquake Early Warning ◽

P Wave ◽

Estimation Accuracy ◽

Earthquake Hazards ◽

Yunnan Region ◽

Low Magnitude

AbstractThe Sichuan–Yunnan region is one of the most seismically vulnerable areas in China. Accordingly, an earthquake early warning (EEW) system for the region is essential to reduce future earthquake hazards. This research analyses the utility of two early warning parameters (τc and Pd) for magnitude estimation using 273 events that occurred in the Sichuan–Yunnan region during 2007–2015. We find that τc can more reliably predict high-magnitude events during a short P-wave time window (PTW) but produces greater uncertainty in the low-magnitude range, whereas Pd is highly correlated with the event magnitude depending on the selection of an appropriate PTW. Here, we propose a threshold-based evolutionary magnitude estimation method based on a specific combination of τc and Pd that both offers more robust advance magnitude estimates for large earthquakes and ensures the estimation accuracy for low-magnitude events. The advantages of the proposed approach are validated using data from 2016–2017 and the Ms 8.0 Wenchuan earthquake in an offline simulation. The proposed concept provides a useful basis for the future implementation of an EEW system in the Sichuan–Yunnan region.

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Robust Online State of Charge Estimation of Lithium-Ion Battery Pack Based on Error Sensitivity Analysis

Mathematical Problems in Engineering ◽

10.1155/2015/573184 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Ting Zhao ◽

Jiuchun Jiang ◽

Caiping Zhang ◽

Kai Bai ◽

Na Li

Keyword(s):

Lithium Ion Battery ◽

Estimation Error ◽

Estimation Method ◽

Dynamic Test ◽

Lithium Ion ◽

Vital Role ◽

State Of Charge ◽

Battery Pack ◽

Estimation Accuracy ◽

Soc Estimation

Accurate and reliable state of charge (SOC) estimation is a key enabling technique for large format lithium-ion battery pack due to its vital role in battery safety and effective management. This paper tries to make three contributions to existing literatures through robust algorithms. (1) Observer based SOC estimation error model is established, where the crucial parameters on SOC estimation accuracy are determined by quantitative analysis, being a basis for parameters update. (2) The estimation method for a battery pack in which the inconsistency of cells is taken into consideration is proposed, ensuring all batteries’ SOC ranging from 0 to 1, effectively avoiding the battery overcharged/overdischarged. Online estimation of the parameters is also presented in this paper. (3) The SOC estimation accuracy of the battery pack is verified using the hardware-in-loop simulation platform. The experimental results at various dynamic test conditions, temperatures, and initial SOC difference between two cells demonstrate the efficacy of the proposed method.

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An Algorithm for Estimating the Signal Frequency at the Output of a Channel with a Controlled Information Flow under Phase Noise Conditions

Modeling and Analysis of Information Systems ◽

10.18255/1818-1015-2021-4-452-461 ◽

2021 ◽

Vol 28 (4) ◽

pp. 452-461

Author(s):

Leonid Nikolaevich Kazakov ◽

Evgenii Pavlovich Kubyshkin ◽

Ilya Victorovich Lukyanov

Keyword(s):

Phase Noise ◽

Frequency Estimation ◽

Estimation Error ◽

Harmonic Signal ◽

Frequency Synthesizers ◽

Broadband Noise ◽

Signal Frequency ◽

High Signal ◽

Radio Network ◽

Estimation Algorithms

Research in the field of efficient frequency estimation algorithms is of great interest. The reason for this is the redistribution of the role of additive and phase noise in many modern radio-engineering applications. An example is the area of measuring radio devices, which usually operate at high signal-to-noise ratios (SNR). The estimation error is largely determined not by the broadband noise, but by the frequency and phase noise of the local oscillators of the receiving and transmitting devices. In particular, earlier works \\cite{Nikiforov} proposed an efficient computational algorithm for estimating the frequency of a quasi-harmonic signal based on the iterative calculation of the autocorrelation sequence (ACS). In \\cite{Volkov}, this algorithm was improved and its proximity to the Rao-Cramer boundary was shown (the sources of this noise are master oscillators and frequency synthesizers). Possibilities of frequency estimation in radio channels make it possible to significantly expand the functionality of the entire radio network. This can include, for example, the problem of adaptive distribution of information flows of a radio network. This also includes the tasks of synchronization and coherent signal processing. For these reasons, more research is needed on this algorithm, the calculation of theoretical boundaries and their comparison with the simulation results.

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