Improvement of DOD/DOA Estimation for Bistatic MIMO Radar

Paik, Ji Woong; Lee, Joon-Ho

doi:10.1007/s42405-019-00173-6

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…With the continuous development of flight technology, the speed maneuvering performance of aircraft is constantly improved, which poses a severe challenge to the detection and tracking performance of warning radar [1][2][3]. Radar has a long detection range, has high data accuracy, and is not affected by environmental factors such as illumination [4,5]. It is widely used in map location, obstacle detection, target recognition, and tracking.…”

Section: Introductionmentioning

confidence: 99%

An Improved Unscented Kalman Filter Algorithm for Radar Azimuth Mutation

You

Liu

Shang

et al. 2020

International Journal of Aerospace Engineering

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An improved UKF (Unscented Kalman Filter) algorithm is proposed to solve the problem of radar azimuth mutation. Since the radar azimuth angle will restart to count after each revolution of the radar, and when the aircraft just passes the abrupt angle change, the radar observation measurement will have a sudden change, which has serious consequences and is solved by the proposed novel UKF based on SVD. In order to improve the tracking accuracy and stability of the radar tracking system further, the SVD-MUKF (Singular Value Decomposition-based Memory Unscented Kalman Filter) based on multiple memory fading is constructed. Furthermore, several simulation results show that the SVD-MUKF algorithm proposed in this paper is better than the SVD-UKF (Singular Value Decomposition of Unscented Kalman Filter) algorithm and classical UKF algorithm in accuracy and stability. Last but not the least, the SVD-MUKF can achieve stable tracking of targets even in the case of angle mutation.

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