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

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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Attitude Measurement for High-Spinning Projectile with a Hollow MEMS IMU Consisting of Multiple Accelerometers and Gyros

Sensors ◽

10.3390/s19081799 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1799 ◽

Cited By ~ 7

Author(s):

Fuchao Liu ◽

Zhong Su ◽

Hui Zhao ◽

Qing Li ◽

Chao Li

Keyword(s):

Physical Simulation ◽

Unscented Kalman Filter ◽

Angular Rate ◽

Hollow Structure ◽

Least Square ◽

Statistical Characteristics ◽

Estimation Accuracy ◽

Spinning Projectile ◽

Solution Accuracy ◽

Mems Imu

A low cost, high precision hollow structure MEMS IMU has been developed to measure the roll angular rate of a high-spinning projectile. The hollow MEMS IMU is realized by designing the scheme of non-centroid configuration of multiple accelerometers. Two dual-axis accelerometers are respectively mounted on the pitch axis and the yaw axis away from the center of mass of the high-spinning projectile. Three single-axis gyros are mounted orthogonal to each other to measure the angular rates, respectively. The roll gyro is not only used to judge the spinning direction, but also to measure and compensate for the low rotation speed of the high-spinning projectile. In order to improve the measurement accuracy of the sensor, the sensor output error is modeled and calibrated by the least square method. By analyzing the influence of noise statistical characteristics on angular rate solution accuracy, an adaptive unscented Kalman filter (AUKF) algorithm is proposed, which has a higher estimation accuracy than UKF algorithm. The feasibility of the method is verified by numerical simulation. By using the MEMS IMU device to build a semi-physical simulation platform, the solution accuracy of the angular rate is analyzed by simulating different rotation speeds of the projectile. Finally, the flight test is carried out on the rocket projectile with the hollow MEMS IMU. The test results show that the hollow MEMS IMU is reasonable and feasible, and it can calculate the roll angular rate in real time. Therefore, the hollow MEMS IMU designed in this paper has certain engineering application value for high-spinning projectiles.

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A System-of-Systems Perspective on Frequency Estimation: Time-Frequency Distribution of Multiple LFM Signals

10.5772/intechopen.95894 ◽

2021 ◽

Author(s):

Ruolun Liu ◽

Xueqin Zhang ◽

Rui Huang

Keyword(s):

Frequency Distribution ◽

Impulse Response ◽

Frequency Estimation ◽

System Of Systems ◽

Signal Frequency ◽

Accurate Estimation ◽

Time Frequency ◽

Systems Perspective ◽

Short Time ◽

Time Frequency Distribution

This chapter provides a System-of-Systems (SoS) perspective on a study of frequency estimation of signals with a focus on Linear Frequency Modulation (LFM) signals. This chapter describes an SoS approach for frequency estimation using Chirplet Transform (CT), Hough Transform (HT), and the Short Time Fourier Transform (STFT) with filtering viewpoint. The filtering viewpoint employs the filter impulse response length to obtain the best time-frequency concentration for accurate estimation of a signal frequency. The optimum impulse response length can be found by varying the length of the filter impulse response and observe the changing in the time-frequency distribution (TFD). The chapter shows that when the length of the impulse response becomes longer, the time-frequency concentration in TFD increases first and then decreases.

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Adaptive Signal Frequency Estimation

Herald of the Bauman Moscow State Technical University Series Instrument Engineering ◽

10.18698/0236-3933-2019-6-41-49 ◽

2019 ◽

pp. 41-49

Author(s):

V.V. Chudnikov ◽

B.I. Shakhtarin

Keyword(s):

Signal Sequence ◽

Frequency Estimation ◽

Harmonic Signal ◽

Descent Method ◽

Estimation Methods ◽

Signal Frequency ◽

Accurate Estimation ◽

Frequency Error ◽

Gradient Descent Method ◽

Harmonic Components

The paper introduces an adaptive algorithm for estimating the frequencies of harmonic components in the signal against the background of additive white noise. This method is iterative, which distinguishes it from the periodogram and parametric spectral estimation methods. The key feature of the algorithm is that it gives a reasonably accurate estimation only for the preset number of harmonic components included in the signal under study. In the original discrete signal, a frequency search was performed at each time sample using the gradient descent method. Frequency estimation is made when the frequency error value tends to a certain value. The search is based on the representation of the value of the current sample of the harmonic signal of a known frequency through the two previous values. Knowing the number of components included in the original signal sequence, it is possible to form the resulting sequence containing only residual noise samples. A mathematical model of the algorithm is given, its work is simulated for different conditions of application, the accuracy of the algorithm, i.e., frequency estimation, and the number of iterations for various signal-to-noise ratios are shown.

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

Metrology and Measurement Systems ◽

10.2478/mms-2014-0036 ◽

2014 ◽

Vol 21 (3) ◽

pp. 423-432 ◽

Cited By ~ 26

Author(s):

Józef Borkowski ◽

Dariusz Kania ◽

Janusz Mroczka

Keyword(s):

Time Window ◽

Frequency Estimation ◽

Estimation Error ◽

Estimation Method ◽

Quantization Noise ◽

Estimation Accuracy ◽

Signal Frequency ◽

Practical Applications ◽

Dominant Component ◽

Signal Value

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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Analisis Ketepatan Pengukur Tegangan True RMS Jala-Jala Listrik Berbasis Mikrokontroler ATmega 328P

AVITEC ◽

10.28989/avitec.v2i2.734 ◽

2020 ◽

Vol 2 (2) ◽

Author(s):

Farobi Widia Nanda ◽

Freddy Kurniawan ◽

Paulus Setiawan

Keyword(s):

Input Signal ◽

Input Voltage ◽

Measuring System ◽

Signal Frequency ◽

Average Error ◽

Test Results ◽

Integer Variables ◽

Input Signal Frequency ◽

Wide Range ◽

Narrow Frequency Range

The analog AC-voltmeter usually can only measure the ideal-sinusoid voltage with narrow frequency range. Meanwhile, in fact the grid voltage is often not in the form of an ideal sinusoidal. To be able to measure a non-sinusoidal AC voltage with a wide range of frequency, a true-RMS voltmeter is needed. The research designed a true RMS measuring system using an ATmega 328P microcontroller. The input voltage is converted to pulse using Schmit triger and fed to the microcontroller’s external interrupt pin to calculate the input signal frequency. Meanwhile the microcontroller’s ADC sampled the input signal with a frequency of 128 times the signal’s frequency. RMS voltage calculations are performed using arithmetic operations for 16 and 32 bit integer variables. The test results show that the system can measure voltages with zero errors from 100 to 275 volts with a frequency of 50 Hz. The system can also measure voltages with zero errors at 220 volt with frequencies from 40 Hz to 150 Hz. However, this system can still be used to measure voltages ranging from 25 volts to 300 volts at frequencies from 35 Hz to 195 Hz with an average error of 0.21%. During RMS voltage calculation, the microcontroller’s CPU usage was 13.35%, so that this system can be further developed.

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Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O2 Battery

Energies ◽

10.3390/en14020324 ◽

2021 ◽

Vol 14 (2) ◽

pp. 324

Author(s):

Haobin Jiang ◽

Xijia Chen ◽

Yifu Liu ◽

Qian Zhao ◽

Huanhuan Li ◽

...

Keyword(s):

Stress Test ◽

Robustness Analysis ◽

Open Circuit ◽

Combined Cycle ◽

State Of Charge ◽

Constant Current ◽

Estimation Accuracy ◽

Accurate Estimation ◽

Battery Management System ◽

Liquid Dynamics

Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas–liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O2 battery. The extended Kalman Filter (EKF) algorithm is elaborated to couple the offline model and online model to achieve the goal of quickly eliminating initial errors in the online SOC estimation. An implementation of the hybrid pulse power characterization test is performed to identify the offline parameters and determine the open-circuit voltage vs. SOC curve. Apart from the standard cycles including Constant Current cycle, Federal Urban Driving Schedule cycle, Urban Dynamometer Driving Schedule cycle and Dynamic Stress Test cycle, a combined cycle is constructed for experimental validation. Furthermore, the study of the effect of sampling time on estimation accuracy and the robustness analysis of the initial value are carried out. The results demonstrate that the proposed method realizes the accurate estimation of SOC with a maximum mean absolute error at 0.50% in five working conditions and shows strong robustness against the sparse sampling and input error.

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Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort

Energies ◽

10.3390/en14030696 ◽

2021 ◽

Vol 14 (3) ◽

pp. 696

Author(s):

Eun Ji Choi ◽

Jin Woo Moon ◽

Ji-hoon Han ◽

Yongseok Yoo

Keyword(s):

Neural Network ◽

Thermal Comfort ◽

Deep Neural Network ◽

Mean Squared Error ◽

Thermal Environment ◽

The Body ◽

Estimation Accuracy ◽

Accurate Estimation ◽

Body Parts ◽

Joint Location

The type of occupant activities is a significantly important factor to determine indoor thermal comfort; thus, an accurate method to estimate occupant activity needs to be developed. The purpose of this study was to develop a deep neural network (DNN) model for estimating the joint location of diverse human activities, which will be used to provide a comfortable thermal environment. The DNN model was trained with images to estimate 14 joints of a person performing 10 common indoor activities. The DNN contained numerous shortcut connections for efficient training and had two stages of sequential and parallel layers for accurate joint localization. Estimation accuracy was quantified using the mean squared error (MSE) for the estimated joints and the percentage of correct parts (PCP) for the body parts. The results show that the joint MSEs for the head and neck were lowest, and the PCP was highest for the torso. The PCP for individual activities ranged from 0.71 to 0.92, while typing and standing in a relaxed manner were the activities with the highest PCP. Estimation accuracy was higher for relatively still activities and lower for activities involving wide-ranging arm or leg motion. This study thus highlights the potential for the accurate estimation of occupant indoor activities by proposing a novel DNN model. This approach holds significant promise for finding the actual type of occupant activities and for use in target indoor applications related to thermal comfort in buildings.

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Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

Journal of Sensors ◽

10.1155/2017/8542153 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 5

Author(s):

Siwen Guo ◽

Jin Wu ◽

Zuocai Wang ◽

Jide Qian

Keyword(s):

Kalman Filter ◽

Process Model ◽

Attitude Determination ◽

Angular Rate ◽

Estimation Algorithm ◽

Measurement Model ◽

Estimation Accuracy ◽

Orientation Estimation ◽

Kinematic Equation ◽

Sensor Orientation

Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained.

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Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

Mobile Information Systems ◽

10.1155/2016/2398464 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Qi An ◽

Zi-shu He ◽

Hui-yong Li ◽

Yong-hua Li

Keyword(s):

Frequency Estimation ◽

Signal To Noise Ratio ◽

Frequency Offset ◽

Estimation Accuracy ◽

Modulation Classification ◽

Electronic Warfare ◽

Wireless Communication Network ◽

Shift Keying ◽

Analytical Formulas ◽

Fractional Function

Promptitude and accuracy of signals’ non-data-aided (NDA) identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK) signals. The periodicity of signal’s phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT) of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR) conditions. The influence on estimation range brought by the maximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power ofQ. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of10-4even when the SNR is as low as-15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.

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Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211072401 ◽

2022 ◽

pp. 095440702110724

Author(s):

Chenyu Zhou ◽

Liangyao Yu ◽

Yong Li ◽

Jian Song

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Stability Control ◽

Estimation Accuracy ◽

Accurate Estimation ◽

Commercial Vehicles ◽

Sideslip Angle ◽

The Neural Network ◽

Wide Range ◽

The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

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