Estimation of three-dimensional radar tracking using modified extended kalman filter

Aditya, Prima; Apriliani, Erna; Arif, Didik Khusnul; Baihaqi, Komar

doi:10.1088/1742-6596/974/1/012071

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…The effect can be observed by the signal power received even for a slow-moving Tx. The power received for a Tx with Kalman filters employ a series of measurements observed over time, containing statistical noise and other inaccuracies, and produce estimates of unknown variables that tend to be more accurate than those based on a single measurement alone [53]. The extended Kalman filter [54] can be employed for non-linear state-space models.…”

Section: E Reducing Localization Errors With Kalman Filteringmentioning

confidence: 99%

mmWave V2V Localization in MU-MIMO Hybrid Beamforming

Lota

Kanhere

et al. 2022

IEEE Open J. Veh. Technol.

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Recent trends for vehicular localization in millimetre-wave (mmWave) channels include employing a combination of parameters such as angle of arrival (AOA), angle of departure (AOD), and time of arrival (TOA) of the transmitted/received signals. These parameters are challenging to estimate, which along with the scattering and random nature of mmWave channels, and vehicle mobility lead to errors in localization. To circumvent these challenges, this paper proposes mmWave vehicular localization employing difference of arrival for time and frequency, with multiuser (MU) multiple-input-multiple-output (MIMO) hybrid beamforming; rather than relying on AOD/AOA/TOA estimates. The vehicular localization can exploit the number of vehicles present, as an increase in the number of vehicles reduces the Cramér-Rao bound (CRB) of error estimation. At 10 dB signal-to-noise ratio (SNR) both spatial multiplexing and beamforming result in comparable localization errors. At lower SNR values, spatial multiplexing leads to larger errors compared to beamforming due to formation of spurious peaks in the cross ambiguity function. Accuracy of the estimated parameters is improved by employing an extended Kalman filter leading to a root mean square (RMS) localization error of approximately 6.3 meters.

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Section: E Reducing Localization Errors With Kalman Filteringmentioning

confidence: 99%

mmWave V2V Localization in MU-MIMO Hybrid Beamforming

Lota

Kanhere

et al. 2022

IEEE Open J. Veh. Technol.

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show abstract

“…The fusion-EKF is designed based on the Kalman filter (KF). KF typically uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone [22]. The difference between the EKF [23] and traditional KF is that EKF can handle linear equations as well as can adapt to non-linearities.…”

Section: Related Workmentioning

confidence: 99%

Extending Reliability of mmWave Radar Tracking and Detection via Fusion With Camera

Zhang

Cao

2019

IEEE Access

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In this paper, a new radar-camera fusion system is presented. The fusion system takes into consideration the error bounds of the two different coordinate systems from the heterogeneous sensors, and further a new fusion-extended Kalman filter is utilized to adapt to the heterogeneous sensors. Real-world application considerations such as asynchronous sensors, multi-target tracking and association are also studied and illustrated in this paper. Experimental results demonstrated that the proposed fusion system can realize a range accuracy of 0.29m with an angular accuracy of 0.013rad in real-time. Therefore, the proposed fusion system is effective, reliable and computationally efficient for real-time kinematic fusion applications.

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“…Tables 1-2 show the state-of-the-art literature review for MoCap systems. The KF can play important role in marine integrated navigation system and radar tracking system because it helps to decrease error [5][6][7][8]. A very useful technique of wearable sensors can be used to track lower body motion capture by using a cascaded KF based sensor fusion algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Motion Capture Systems Using Optimal Signal Processing Algorithm: A State-of-the-art Literature

Rana¹,

Halim²

2018

ujcn

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Motion capture system is a promising research area but the availability of the state-of-the-art literature review on this topic is limited. This research provides a comprehensive literature review for motion capture systems with Kalman filtering algorithms. The summarized state-of-the-art literature review of motion capturing system is being analyzed considering the advantages and the disadvantages of motion capture systems using Kalman filtering algorithm. Afterwards, the human body incorporating noises is represented by system of equations where the system states are unknown. The Kalman filter is adopted to estimate the human body orientations. Simulation results show that the proposed algorithm can be used to track human body orientation accurately.

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Estimation of three-dimensional radar tracking using modified extended kalman filter

Cited by 11 publications

References 3 publications

mmWave V2V Localization in MU-MIMO Hybrid Beamforming

mmWave V2V Localization in MU-MIMO Hybrid Beamforming

Extending Reliability of mmWave Radar Tracking and Detection via Fusion With Camera

Motion Capture Systems Using Optimal Signal Processing Algorithm: A State-of-the-art Literature

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