scholarly journals A Noise Robust Micro-Range Estimation Method for Precession Cone-Shaped Targets

2021 ◽  
Vol 13 (9) ◽  
pp. 1820
Author(s):  
Zhenyu Zhuo ◽  
Yu Zhou ◽  
Lan Du ◽  
Ke Ren ◽  
Yi Li
Keyword(s):  
Kalman Filter ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Motion Feature ◽  
Low Snr ◽  
Noise Covariance ◽  
Modified Kalman Filter ◽  
Noise Robust ◽  
Ransac Algorithm

The estimation of micro-Range (m-R) is important for micro-motion feature extraction and imaging, which provides significant supports for the classification of a precession cone-shaped target. Under low signal-to-noise ratio (SNR) circumstances, the modified Kalman filter (MKF) will obtain broken segments rather than complete m-R tracks due to missing trajectories, and the performance of the MKF is restricted by unknown noise covariance. To solve these problems, a noise-robust m-R estimation method, which combines the adaptive Kalman filter (AKF) and the random sample consensus (RANSAC) algorithm, is proposed in this paper. The AKF, where the noise covariance is not required for the estimation of the state vector, is applied to associate m-R trajectories for higher estimation accuracy and lower wrong association probability. Due to missing trajectories, several associated segments which are parts of the m-R tracks can be obtained by the AKF. Then, the RANSAC algorithm is utilized to associate the segments and the complete m-R tracks can be obtained. Compared with the MKF, the proposed method can obtain complete m-R tracks instead of several segments, and avoids the influence of unknown noise covariance under low SNR circumstances. Experimental results based on electromagnetic simulation data demonstrate that the proposed method is more precise and robust compared with traditional methods.

scholarly journals Local Azimuth Ambiguity-to-Signal Ratio Estimation Method Based on the Doppler Power Spectrum in SAR Images

2019 ◽  
Vol 11 (7) ◽  
pp. 857
Author(s):  
Hui Meng ◽  
Jinsong Chong ◽  
Yuhang Wang ◽  
Yan Li ◽  
Zhuofan Yan
Keyword(s):  
Power Spectrum ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Doppler Spectrum ◽  
Sar Images ◽  
Signal Ratio ◽  
Low Snr ◽  
Estimation Algorithms ◽  
Doppler Power

In synthetic aperture radar (SAR) images, azimuth ambiguity is one of the important factors that affect image quality. Generally, the azimuth ambiguity-to-signal ratio (AASR) is a measure of the azimuth ambiguity of SAR images. For the low signal-to-noise ratio (SNR) ocean areas, it is difficult to accurately estimate the local AASR using traditional estimation algorithms. In order to solve this problem, a local AASR estimation method based on the Doppler power spectrum in SAR images is proposed in this paper by analyzing the composition of the local Doppler spectrum of SAR images. The method not only has higher estimation accuracy under low SNR, but also overcomes the limitations of traditional algorithms on SAR images when estimating AASR. The feasibility and accuracy of the proposed method are verified by simulation experiments and spaceborne SAR data.

scholarly journals Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

2016 ◽  
Vol 2016 ◽  
pp. 1-24 ◽  
Author(s):  
Romy Budhi Widodo ◽  
Chikamune Wada
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Compensation Model ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The One ◽  
The Right ◽  
External Acceleration

Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1) the KF-based model without compensating for external acceleration, (2) the proposed KF-based model which employs the external acceleration compensation model, (3) the two-step KF using weighted-based switching approach, (4) the KF-based model which uses thethreshold-basedapproach, and (5) the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

scholarly journals Machine Learning Based Track Classification and Estimation using Kalman Filter

2020 ◽  
Vol 9 (1) ◽  
pp. 1700-1704
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Velocity Model ◽  
Supervised Machine Learning ◽  
Multiple Target ◽  
Low Snr

Classification of target from a mixture of multiple target information is quite challenging. In This paper we have used supervised Machine learning algorithm namely Linear Regression to classify the received data which is a mixture of target-return with the noise and clutter. Target state is estimated from the classified data using Kalman filter. Linear Kalman filter with constant velocity model is used in this paper. Minimum Mean Square Error (MMSE) analysis is used to measure the performance of the estimated track at various Signal to Noise Ratio (SNR) levels. The results state that the error is high for Low SNR, for High SNR the error is Low

scholarly journals Instantaneous Frequency Estimation Based on Modified Kalman Filter for Cone-Shaped Target

2020 ◽  
Vol 12 (17) ◽  
pp. 2766
Author(s):  
Ke Ren ◽  
Lan Du ◽  
Xiaofei Lu ◽  
Zhenyu Zhuo ◽  
Lu Li
Keyword(s):  
Kalman Filter ◽  
Survival Rate ◽  
Instantaneous Frequency ◽  
Estimation Method ◽  
Consensus Algorithm ◽  
Instantaneous Frequency Estimation ◽  
Time Frequency ◽  
Multicomponent Signals ◽  
Modified Kalman Filter ◽  
Trajectory Correction

The instantaneous frequency (IF) is a vital parameter for the analysis of non-stationary multicomponent signals, and plays an important role in space cone-shaped target recognition. For a cone-shaped target, IF estimation is not a trivial issue due to the proximity of the energy of the IF components, the intersections among different IF components, and the existence of noise. Compared with the general parameterized time-frequency (GPTF), the traditional Kalman filter can perform better when the energy of different signal components is close. Nevertheless, the traditional Kalman filter usually makes association mistakes at the intersections of IF components and is sensitive to the noise. In this paper, a novel IF estimation method based on modified Kalman filter (MKF) is proposed, in which the MKF is used to associate the intersecting IF trajectories obtained by the synchroextracting transform (SET). The core of MKF is the introduction of trajectory correction strategy in which a trajectory survival rate is defined to judge the occurrence of association mistakes. When the trajectory survival rate is below the predetermined threshold, it means that an association mistakes occurs, and then the new trajectories generated by the random sample consensus algorithm are used to correct the wrong associations timely. The trajectory correction strategy can effectively obviate the association mistakes caused by the intersections of IF components and the noise. The windowing technique is also used in the trajectory correction strategy to improve computational speed. The experimental results based on the electromagnetic computation data show that the proposed method is more robust and precise than the traditional Kalman filter. Moreover, the proposed method has great performance advantages compared with other methods (i.e., the multiridge detection, the ant colony optimization, and the GPTF methods) especially in the case of low signal noise ratio (SNR).

A New Friction Estimation Approach for Precision Positioner

2007 ◽  
Author(s):  
Wen Wang ◽  
Xinxin Li ◽  
Zichen Chen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Taylor Series ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Low Velocity ◽  
Friction Estimation ◽  
Simulation Results ◽  
Long Stroke

Precision positioner has been significantly developed as the rapid growth of MEMS and IC industries. As for short-stroke position, the loss of friction can be avoided by using flexible hinges. Long-stroke postioner, however, in which moved-to-be mass always stands on the guide-way part, a main source of friction, makes friction unavoidable. Friction estimation is based on certain filters, such as Extended Kalman filter (EKF). However, estimation accuracy of Kalman filter, especially at low-velocity movement, is not very well. To solve this problem, the paper proposes an estimation method based on DD2 to make an accurate estimation. And the result shows this method is promising in real-time friction estimation. After background introduction, in section 2, the relation of EKF and Taylor series and EKF implementation are reviewed and its limitations are noted as well. A briefly introduction to DD2 is given in Section 3 and friction estimation case comparing the simulation results of DD2 estimation with that of EKF described in Section 4, respectively. At last, conclusions are summarized.

scholarly journals Receding Horizon Least Squares Estimator with Application to Estimation of Process and Measurement Noise Covariances

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Vladimir Shin ◽  
Rebbecca T. Y. Thien ◽  
Yoonsoo Kim
Keyword(s):  
Least Squares ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Receding Horizon ◽  
Covariance Estimator ◽  
Noise Covariance ◽  
Experimental Errors ◽  
And Performance ◽  
Theoretical Results ◽  
Selection Of

This paper presents a noise covariance estimation method for dynamical models with rectangular noise gain matrices. A novel receding horizon least squares criterion to achieve high estimation accuracy and stability under environmental uncertainties and experimental errors is proposed. The solution to the optimization problem for the proposed criterion gives equations for a novel covariance estimator. The estimator uses a set of recent information with appropriately chosen horizon conditions. Of special interest is a constant rectangular noise gain matrices for which the key theoretical results are obtained. They include derivation of a recursive form for the receding horizon covariance estimator and iteration procedure for selection of the best horizon length. Efficiency of the covariance estimator is demonstrated through its implementation and performance on dynamical systems with an arbitrary number of process and measurement noises.

Proportional Derivative Controller Using Discrete Kalman Filter Estimation Method for Spacecraft Attitude Control

2015 ◽  
Vol 789-790 ◽  
pp. 923-926 ◽  
Author(s):  
Mohamad Fakhari Mehrjardi ◽  
Hilmi Sanusi ◽  
Mohd Alauddin Mohd Ali ◽  
Montadar Abas Taher
Keyword(s):  
Kalman Filter ◽  
Attitude Control ◽  
Estimation Method ◽  
Angular Acceleration ◽  
Gravity Gradient ◽  
Noise Covariance ◽  
Proportional Derivative Controller ◽  
Typical Satellite ◽  
Noisy Measurements ◽  
Data Updating

This paper deals with the objective of controlling a satellite by driving a six-state discrete Kalman Filter to estimate angular rates of satellite base on control sensor noisy data. A typical satellite is assumed in a special orbit and orbital angular velocity and orbital angular acceleration are established. For completion of simulation linear dynamics model of satellites and environment disturbances model such as solar pressure and gravity gradient torque is derived as well. The simulation is progressed at discrete ten second which assumed as data updating rate from sensor. The noisy measurements are produced by sensor and these data is sent to the discrete Kalman Filter part to estimate the attitude and attitude rate. A right balance for Plant noise covariance matrix is determined and also results show that the rate estimates are appropriate for space missions.

scholarly journals DOA estimation method of spatial spectrum product for distributed coprime linear array

2021 ◽  
Vol 39 (5) ◽  
pp. 1130-1138
Author(s):  
Na WANG ◽  
Xuanzhi ZHAO ◽  
Zengli LIU ◽  
Jingjing ZHANG
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Spatial Spectrum ◽  
Estimation Accuracy ◽  
Low Snr ◽  
Distributed Array ◽  
Coprime Arrays ◽  
Coprime Array ◽  
Mathematical Derivation ◽  
Better Than

Coprime array isAsparse array composed of two uniform linear arrays with different spacing. When the two subarrays are inAnon-coherent distributed configuration, the direction of arrival (DOA) method based on the covariance analysis of the complete coprime array is no longer effective. According to the essential attribute that the distance between the elements of two subarrays can eliminate the angle ambiguity, based on the mathematical derivation, Aspatial spectral product DOA estimation method for incoherent distributed coprime arrays is proposed. Firstly, the spatial spectrum of each subarray is calculated by using the snapshot data of each subarray, and then the DOA estimation is realized by multiplying the spatial spectrum of each subarray. The simulation results show that the estimation accuracy and angle resolution of the present method are better than those of the traditional ambiguity resolution methods, and the estimation performance is good in the mutual coupling and low SNR environment, with the good adaptability and stability. Moreover, by using the flexibility of distributed array, the matching error is effectively solved through the rotation angle.

scholarly journals Improvement of Torque Estimation for Series Viscoelastic Actuator Based on Dual Extended Kalman Filter

Actuators ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 258
Author(s):  
Hui Wei ◽  
Kui Xiang ◽  
Haibo Chen ◽  
Biwei Tang ◽  
Muye Pang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Viscoelastic Model ◽  
Estimation Method ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Motor Current ◽  
Output Torque ◽  
Torque Estimation ◽  
Viscoelastic Element

Adding damping such as viscoelastic element in series elastic actuators (SEA) can improve the force control bandwidth of the system and suppression of high frequency oscillations induced by the environment. Thanks to such advantages, series viscoelastic actuators (SVA) have recently gained increasing research interests from the community of robotic device design. Due to the inconvenience of mounting torque sensors, employing the viscoelastic elements to directly estimate the output torque is of great significance regarding the real-world applications of SVA. However, the nonlinearity and time-varying properties of viscoelastic materials would degrade the torque estimation accuracy. In such a case, it is paramount to simultaneously estimate the output torque state and viscoelastic model coefficients in order to enhance the torque estimation accuracy. To this end, this paper first completed the design of a rubber-based SVA device and used the Zenner linear viscoelastic model to model the viscoelastic element of the rubber. Subsequently, this paper proposed a dual extended Kalman filter- (DEFK) based torque estimation method to estimate the output torque and viscoelastic model coefficients simultaneously. The noisy observations of two Kalman filters were provided by motor current-based estimated torque. Moreover, the dynamic friction of harmonic drive of the designed SVA was modeled and compensated to enhance the reliability of current-based torque estimation. Finally, a number of experiments were carried out on SVA, and the experimental results confirmed the DEFK effectiveness of improving torque estimation accuracy compared to only-used rubber and only-used motor current torque estimation methods. Thus, the proposed method could be considered as an effective alternative approach of torque estimation for SVA.

scholarly journals A Novel ULA-Difference-Coarray-Based DOA Estimation Method for General Coherent Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhang Chen ◽  
Hao Wu ◽  
Yongxiang Liu
Keyword(s):  
Good Accuracy ◽  
Linear Array ◽  
Estimation Method ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Coherent Signals ◽  
Low Snr ◽  
Sample Covariance ◽  
Lower Complexity ◽  
The Difference

In this article, a difference-coarray-based direction of arrival (DOA) method is introduced, which utilizes the uniform linear array (ULA) in a novel fashion to address the problem of DOA estimation for coherent signals. Inspired by the coarray-based estimators employed in cases of sparse arrays, we convert the sample covariance matrix of the observed signals into the difference coarray domain and process the signals using a spatial smoothing technique. The proposed method exhibits good accuracy and robustness in both the uncorrelated and coherent cases. Numerical simulations verify that the ULA difference coarray- (UDC-) based method can achieve good DOA estimation accuracy even when the SNR is very low. In addition, the UDC-based method is insensitive to the number of snapshots. Under extremely challenging conditions, the proposed UDC-ES-DOA method is preferred because of its outstanding robustness, while the UDC-MUSIC method is suitable for most moderate cases of lower complexity. Due to its demonstrated advantages, the proposed method is a promising and competitive solution for practical DOA estimation, especially for low-SNR or snapshot-limited applications.

Sign in / Sign up

Export Citation Format

Share Document