Tracking a Low-Angle Isolated Target via an Elevation-Angle Estimation Algorithm Based on Extended Kalman Filter with an Array Antenna

In a low-angle tracking situation, estimating the elevation angle is challenging due to the entrance of the multipath signals in the antenna’s main lobe. In this article, we propose two methods based on the extended Kalman filter (EKF) and frequency diversity (FD) process to estimate the elevation angle of a low-angle isolated target. In the first case, a simple weighting of the per-frequency estimates is obtained (termed WFD). Differently, in the second case, a matrix-based elaboration of the per-frequency estimates is proposed (termed MFD). The proposed methods are completely independent of prior knowledge of geometrical information and the physical parameters. The simulation results show that both methods have excellent performance and guarantee accurate elevation angle estimation in different multipath environments and even in very-low SNR conditions. Hence, they are both suitable for low-peak-power radars.

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A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

International Journal of Navigation and Observation ◽

10.1155/2015/503814 ◽

2015 ◽

Vol 2015 ◽

pp. 1-18 ◽

Cited By ~ 11

Author(s):

Heikki Hyyti ◽

Arto Visala

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Low Cost ◽

Direction Cosine ◽

Estimation Algorithm ◽

Calibration Method ◽

Attitude Estimation ◽

Mems Sensors ◽

Acceleration Sensors ◽

Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

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A Fuzzy State-of-Charge Estimation Algorithm Combining Ampere-Hour and an Extended Kalman Filter for Li-Ion Batteries Based on Multi-Model Global Identification

Applied Sciences ◽

10.3390/app8112028 ◽

2018 ◽

Vol 8 (11) ◽

pp. 2028 ◽

Cited By ~ 9

Author(s):

Xin Lai ◽

Dongdong Qiao ◽

Yuejiu Zheng ◽

Long Zhou

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Estimation Algorithm ◽

State Of Charge ◽

Distribution Area ◽

Model Parameters ◽

Model Accuracy ◽

Soc Estimation ◽

Fuzzy Fusion ◽

Fuzzy State

The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.

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A variable structure adaptive extended Kalman filter for vehicle slip angle estimation

International Journal of Vehicle Design ◽

10.1504/ijvd.2011.043263 ◽

2011 ◽

Vol 56 (1/2/3/4) ◽

pp. 161 ◽

Cited By ~ 15

Author(s):

ang Li ◽

Jian Song ◽

Hongzhi Li ◽

Zhang Xiaolong ◽

ang Li ◽

...

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Variable Structure ◽

Slip Angle ◽

Angle Estimation ◽

Adaptive Extended Kalman Filter

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Low Elevation Angle Estimation with Range Super-Resolution in Wideband Radar

Sensors ◽

10.3390/s20113104 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3104 ◽

Cited By ~ 1

Author(s):

Sha Huan ◽

Man Zhang ◽

Gane Dai ◽

Huaguo Gan

Keyword(s):

Grazing Angle ◽

Elevation Angle ◽

Super Resolution ◽

Robust Estimator ◽

Frequency Diversity ◽

Angle Estimation ◽

Structure Relationship ◽

Radar Waveforms ◽

Radar Applications ◽

Wideband Radar

Height detection of a low elevation angle target is crucial in radar applications. Due to the presence of the multiple path reflections, elevation angle estimation is difficult with conventional narrowband radar waveforms. The reflection ground area parameters are especially hard to obtain for modeling. In this paper, we proposed a wideband, low elevation angle estimator based on range super-resolution, achieving a high robustness to variations in reflection coefficients. A relaxation (RELAX) algorithm was applied as the range super-resolution algorithm to separate the direct target echo and the reflected signal thanks to the relatively abundant frequency diversity. The grazing angle was obtained by synthesizing the steering vector of the direct signal and the range structure relationship between the two signal components. Theoretical analysis and simulation results confirmed the improved behavior of the proposed robust estimator in contrast to other conventional algorithms.

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Improved Azimuth/Elevation Angle Estimation Algorithm for Three-Parallel Uniform Linear Arrays

IEEE Antennas and Wireless Propagation Letters ◽

10.1109/lawp.2014.2360419 ◽

2015 ◽

Vol 14 ◽

pp. 329-332 ◽

Cited By ~ 23

Author(s):

Hua Chen ◽

Chunping Hou ◽

Qing Wang ◽

Ling Huang ◽

Weiqing Yan ◽

...

Keyword(s):

Elevation Angle ◽

Estimation Algorithm ◽

Angle Estimation ◽

Linear Arrays

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Fault-tolerant pose and inertial parameters estimation of an uncooperative spacecraft based on dual vector quaternions

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410017751766 ◽

2018 ◽

Vol 233 (4) ◽

pp. 1250-1269 ◽

Cited By ~ 4

Author(s):

Jianping Yuan ◽

Xianghao Hou ◽

Chong Sun ◽

Yu Cheng

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Formation Flying ◽

Fault Tolerant ◽

Estimation Algorithm ◽

Parameters Estimation ◽

Small Satellites ◽

Dual Vector ◽

Inertial Parameters ◽

Space Target

Estimating the parameters of an unknown free-floating tumbling spacecraft is an essential task for the on-orbit servicing missions. This paper proposes a dual vector quaternion based fault-tolerant pose and inertial parameters estimation algorithm of an uncooperative space target using two formation flying small satellites. Firstly, by utilizing the dual vector quaternions to model the kinematics and dynamics of the system, not only the representation of the model is concise and compacted, but also the translational and rotational coupled effects are considered. By using this modeling technique along with the measurements from the on-board vision-based sensors, a dual vector quaternion based extended Kalman filter for each of the two small satellites is designed. Secondly, both of the estimations from each small satellite will be used as inputs of the fault-tolerant algorithm. This algorithm is based on the fault-tolerant federal extended Kalman filter strategy to overcome the estimation errors caused by the faulty measurements, the unknown space environment and the computing errors by setting the appropriate ratios of the two estimations from the first step dual vector quaternions extended Kalman filter. Together with the first and second steps, a novel fault-tolerant dual vector quaternions federal extended Kalman filter using two formation flying small satellites is proposed by this paper to estimate the pose and inertial parameters of a free-floating tumbling space target. By utilizing the estimation algorithm, a good prior knowledge of the unknown space target can be achieved. Finally, the proposed dual vector quaternion federal extended Kalman filter is validated by mathematical simulations to show its robust performances.

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Structural Stiffness Identification Based on the Extended Kalman Filter Research

Abstract and Applied Analysis ◽

10.1155/2014/103102 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8

Author(s):

Fenggang Wang ◽

Xianzhang Ling ◽

Xun Xu ◽

Feng Zhang

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

State Equation ◽

The State ◽

Physical Parameters ◽

Structural Stiffness ◽

Structural Dynamic ◽

Discrete State ◽

Response Acquisition ◽

Shear Structure

For the response acquisition of the structure section measuring points, the method of identifying the structural stiffness parameters is developed by using the extended Kalman filter. The state equation of structural system parameter is a nonlinear equation. Dispersing the structural dynamic equation by using Newmark-βmethod, the state transition matrix of discrete state equation is deduced and the solution of discrete state equation is simplified. The numerical simulation shows that the error of structural recognition doesnot exceed 5% when the noise level is 3%. It meets the requirements of the error limit of the engineering structure, which indicates that the derivation described in this paper has the robustness for the structural stiffness recognition. Shear structure parameter identification examples illustrate its applicability, and the method can also be used to identify physical parameters of large structure.

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