Analysis of Channel Mismatch Error on InSAR Performance

2011 ◽  
Vol 33 (11) ◽  
pp. 2709-2713
Author(s):  
Zhi-hua He ◽  
Feng He ◽  
Hai-feng Huang ◽  
Dian-nong Liang
Keyword(s):  
Mismatch Error ◽  
Channel Mismatch

Researches on Channel Mismatch Effects in Time-Interleaved ADC System

2013 ◽  
Vol 655-657 ◽  
pp. 978-983
Author(s):  
Hui Yong Sun ◽  
Peng Cao
Keyword(s):  
High Speed ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Mismatch Error ◽  
Signal To Noise ◽  
Gain Error ◽  
Channel Mismatch ◽  
High Speed Data Acquisition ◽  
Interleaved Adc ◽  
Time Interleaved

The Time-Interleaved ADC（TIADC) is an effective method for implement ultra high-speed data acquisition. However, the errors of channel mismatch are seriously degrade the signal-to-noise ratio of the system, such as Time-skew error, Gain error and Offset error. This paper have done some researches and analysis, and given the modeling of the three channels mismatch. What's more, it also given a detailed analysis of error and the method of measure it, derived the formula of signal to noise and distortion ratio(SINAD) and spurious free dynamic range(SFDR). All of them provide a reference for the tolerance range of TIADC channel mismatch error. Meanwhile, the result of this paper has provided a theoretical basis for eliminating TIADC channel mismatch error.

scholarly journals Adaptive fading factor unscented Kalman filter with application to target tracking

2020 ◽  
Author(s):  
Peng Gu ◽  
Zhongliang Jing ◽  
Liangbin Wu
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
System Model ◽  
Mismatch Error ◽  
Tracking Accuracy ◽  
Model Mismatch ◽  
Error Covariance ◽  
Nonlinear Tracking ◽  
Fading Factor

AbstractOne purpose of target tracking is to estimate the states of targets, and unscented Kalman filter is one of the effective algorithms for estimating in the nonlinear tracking problem. Considering the characteristics of complex maneuverability, it is easy to reduce the tracking accuracy and cause divergence due to the mismatch between the system model and the practical target motion model. Adaptive fading factor is an effective counter to this problem, having been instrumental in solving accuracy and divergence problems. Fading factor can adaptively adjust covariance matrix online to compensate model mismatch error. Moreover, fading factor not only improves the filtering accuracy, but also automatically adjusts the error covariance in response to the different situation. The simulation results show that the adaptive fading factor unscented Kalman filter has more advantages in target tracking and it can be better applied to nonlinear target tracking.

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