Target Position Estimation Algorithm under Corrupted Measurement Data for Radar Network Systems

2012 ◽  
Vol E95-A (1) ◽  
pp. 317-321 ◽  
Author(s):  
Hiroyuki HATANO ◽  
Tomoharu MIZUTANI ◽  
Kazuya SUGIYAMA ◽  
Yoshihiko KUWAHARA
Keyword(s):  
Measurement Data ◽  
Target Position ◽  
Estimation Algorithm ◽  
Position Estimation ◽  
Network Systems ◽  
Radar Network

Error Reduction by Reflected Signals in Automotive Radar Network Systems

2015 ◽  
Vol E98.A (2) ◽  
pp. 597-605
Author(s):  
Hiroyuki HATANO ◽  
Masahiro FUJII ◽  
Atsushi ITO ◽  
Yu WATANABE ◽  
Yusuke YOSHIDA ◽  
...  
Keyword(s):  
Error Reduction ◽  
Automotive Radar ◽  
Network Systems ◽  
Radar Network

scholarly journals Calibration Method of Array Errors for Wideband MIMO Imaging Radar Based on Multiple Prominent Targets

2021 ◽  
Vol 13 (15) ◽  
pp. 2997
Author(s):  
Zheng Zhao ◽  
Weiming Tian ◽  
Yunkai Deng ◽  
Cheng Hu ◽  
Tao Zeng
Keyword(s):  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Target Position ◽  
Position Estimation ◽  
Least Square ◽  
Antenna Structure ◽  
Imaging Radar ◽  
Position Errors ◽  
Calibration Methods ◽  
Array Errors

Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets.

scholarly journals GPS Receiver Position Augmentation Using Correntropy Kalman Filter in Low Latitude Terrain

2022 ◽  
Author(s):  
Sirish Kumar Pagoti ◽  
Bala Sai Srilatha Indira Dutt Vemuri ◽  
Ganesh Laveti
Keyword(s):  
Kalman Filter ◽  
Indian Subcontinent ◽  
Estimation Algorithm ◽  
Position Estimation ◽  
Gps Receiver ◽  
Dual Frequency ◽  
Low Latitude ◽  
Position Accuracy ◽  
Aircraft Landing ◽  
Gps Accuracy

If any Global Positioning System (GPS) receiver is operated in low latitude regions or urban canyons, the visibility further reduces. These system constraints lead to many challenges in providing precise GPS position accuracy over the Indian subcontinent. As a result, the standalone GPS accuracy does not meet the aircraft landing requirements, such as Category I (CAT-I) Precision Approaches. However, the required accuracy can be achieved by augmenting the GPS. Among all these issues, the predominant factors that significantly influence the receiver position accuracy are selecting a user/receiver position estimation algorithm. In this article, a novel method is proposed based on correntropy and designated as Correntropy Kalman Filter (CKF) for precise GPS applications and GPS Aided Geosynchronous equatorial orbit Augmented Navigation (GAGAN) based aircraft landings over the low latitude Indian subcontinent. The real-world GPS data collected from a dual-frequency GPS receiver located in the southern region of the Indian subcontinent (IISc), Bangalore with Lat/Long: 13.021°N/ 77.5°E) is used for the performance evaluation of the proposed algorithm. Results prove that the proposed CKF algorithm exhibits significant improvement (up to 34%) in position estimation compared to the traditional Kalman Filter.

A Rapid Position Estimation Algorithm Inspired of Compound Eyes

2014 ◽  
Vol 43 (6) ◽  
pp. 612001
Author(s):  
邢强 XING Qiang ◽  
戴振东 DAI Zhendong ◽  
王浩 WANG Hao
Keyword(s):  
Estimation Algorithm ◽  
Position Estimation ◽  
Compound Eyes

scholarly journals Position Estimation of a PMSM in an Electric Propulsion Ship System Based on High-Frequency Injection

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 276 ◽  
Author(s):  
Hongfen Bai
Keyword(s):  
Permanent Magnet ◽  
High Frequency ◽  
Electric Propulsion ◽  
Permanent Magnet Synchronous Motor ◽  
Estimation Algorithm ◽  
Synchronous Motor ◽  
Position Estimation ◽  
Second Order ◽  
Application Range ◽  
Rotor Position

To improve the operating performance of electric propulsion ships, the permanent magnet synchronous motor is commonly used as the propulsion motor. Additionally, position estimation without sensors can further improve the application range of the propulsion motor and the estimated results can represent the redundancy of measured values from mechanical sensors. In this paper, the high-frequency (HF) injection algorithm combined with the second-order generalized integrator (SOGI) is presented on the basis of analyzing the structure of the electric propulsion ship and the vector control of the motors. The position and rotor speed were estimated accurately by the approximate calculation of q-axis currents directly related to the rotor position. Moreover, the harmonics in the estimated position were effectively reduced by the introduction of the second-order generalized integrator. Then, the rotor position estimation algorithm was verified in MATLAB/Simulink by choosing different low speeds including speed reversal, increasing speed, and increasing load torque. Finally, the correctness of the proposed improved high-frequency injection algorithm based on the second-order generalized integrator was verified by the experimental propulsion permanent magnet synchronous motor (PMSM) system at low speed.

scholarly journals Robust and Fast State Estimation for Poorly-Observable Low Voltage Distribution Networks Based on the Kalman Filter Algorithm

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4457 ◽  
Author(s):  
Antončič ◽  
Papič ◽  
Blažič
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Low Voltage ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Measurement Data ◽  
Estimation Algorithm ◽  
Distribution Networks ◽  
Voltage Distribution ◽  
State Estimator

This paper presents a novel approach for the state estimation of poorly-observable low voltage distribution networks, characterized by intermittent and erroneous measurements. The developed state estimation algorithm is based on the Extended Kalman filter, where we have modified the execution of the filtering process. Namely, we have fixed the Kalman gain and Jacobian matrices to constant matrices; their values change only after a larger disturbance in the network. This allows for a fast and robust estimation of the network state. The performance of the proposed state-estimation algorithm is validated by means of simulations of an actual low-voltage network with actual field measurement data. Two different cases are presented. The results of the developed state estimator are compared to a classical estimator based on the weighted least squares method. The comparison shows that the developed state estimator outperforms the classical one in terms of calculation speed and, in case of spurious measurements errors, also in terms of accuracy.

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