Mono-pulse radar angle estimation algorithm under low signal-to-noise ratio

Dong, Wenhao; Wang, Jingyi; Song, Zhiyong

doi:10.1088/1742-6596/1914/1/012042

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…In this case, considering the object as an extended target with a size, shape and orientation can improve the accuracy of its tracking. In the context of the present high-precision sensors, researches have studied target extensions, providing a well-established and widely used ETT framework under the assumption of an ellipsoidal target shape, where an approximate Bayesian solution to the extended target tracking problem is proposed [14][15][16][17][18]. Ellipsoidal object extension is modeled using a random matrix and is considered as an additional state variable to be estimated.…”

Section: Extended Target Tracking Algorithmmentioning

confidence: 99%

Research on Extended Target-Tracking Algorithms of Sea Surface Navigation Radar

Tian

Zhang

2023

Electronics

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To solve the problem of false tracks generated by breakdowns and clutter in point-target tracking in polar coordinates, a fusion tracking algorithm based on a converted measurement Kalman filter and random matrix expansion is proposed. The converted measurement Kalman filter (CMKF) transforms the polar coordinate data of the target at the current time into Cartesian coordinates without bias. Based on linear measurements and states, the position of the extended target and the group target was predicted and updated by using a random matrix, and its track was drawn by combining the nearest neighbors to realize the tracking of the size, shape and azimuth of the extended target. Compared with point-target tracking, the accuracy of extended multi-target tracking was increased by 45.8% based on data measured using NAVICO navigation radar aboard ships at sea. The experimental results showed that the improved method in this paper could effectively reduce the interference of clutter on target tracking and provide more information about the target motion features.

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Section: Extended Target Tracking Algorithmmentioning

confidence: 99%

Research on Extended Target-Tracking Algorithms of Sea Surface Navigation Radar

Tian

Zhang

2023

Electronics

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“…In [7][8][9][10][11][12][13], a performance analysis considering the deviation of the estimated angle of the target expressed as measurement noise and a performance analysis by internal error were conducted. In addition, various monopulse algorithms have been proposed to improve the angle estimation accuracy in the presence of noise and interfering signals.…”

Section: Introductionmentioning

confidence: 99%

“…The new monopulse algorithm proposed in [12] acquires the target angle without distorting the beam shape. In addition, in order for the monopulse radar seeker to increase the angle measurement accuracy in the low signalto-noise ratio (SNR) environment, the authors of [13] proposed an estimation algorithm based on the Bayesian framework.…”

Section: Introductionmentioning

confidence: 99%

Numerical Integration-Based Performance Analysis of Amplitude-Comparison Monopulse Algorithm in Correlated Noise

Ham

Lee

2022

Electronics

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In this paper, the performance analysis of the amplitude-comparison monopulse (ACM) algorithm under a correlated noise effect is dealt with. The noise received by a monopulse antenna is caused by various sources, such as jamming, multipath, clutter, and thermal noise. The noise variables caused by these noise sources may be correlated with each other when received by the antenna elements. We explicitly analyzed the angle estimation performance of the monopulse algorithm when a correlated noise is received by deriving the probability density function (PDF) of the channel noise variables. In this process, correlation coefficients between noise variables received by antenna elements are defined, and variance and correlation coefficients of channel noise variables are derived. The performance of the angle estimation is analyzed by calculating the root mean square error (RMSE) for various variances and correlation coefficients of the received noise variables. The expectation operation required for calculating the RMSE is performed via numerical integration. Consequently, the analytically derived RMSE results show excellent agreements with the Monte Carlo simulation-based RMSE result, and it is confirmed that the RMSE decreases as the correlation coefficient between the received noise variables increases. When the SNR is high and on-axis, the RMSE decreases by 20% whenever the correlation coefficient between the reception noise variables increases by 0.2.

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Mono-pulse radar angle estimation algorithm under low signal-to-noise ratio

Cited by 2 publications

References 4 publications

Research on Extended Target-Tracking Algorithms of Sea Surface Navigation Radar

Research on Extended Target-Tracking Algorithms of Sea Surface Navigation Radar

Numerical Integration-Based Performance Analysis of Amplitude-Comparison Monopulse Algorithm in Correlated Noise

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