A Novel GSC Beamformer Using a Combination of Two Adaptive Filters for Smart Antenna Array

Sun, Jinping; Wang, Guohua; Tian, Jing

doi:10.1109/lawp.2012.2192250

Cited by 10 publications

(2 citation statements)

References 10 publications

(18 reference statements)

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“…Adaptive filters can be described as variable transfer functions defined in the Z-Transform domain, H(z) [17]. Their use for emissions measurement improvements in OATS and antenna design has been recently documented [6,5,18]. In 7 One important aspect of an adaptive filter design concerns the definition of an adaptive algorithm and its parameters specification.…”

Section: Adaptive Filtering 130mentioning

confidence: 99%

Improving antenna gain estimations in non-ideal test sites with auto-tunable filters

et al. 2020

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Estimating the gain of an antenna prototype is an important step during both design and validation phases. Nevertheless, many small technology companies and universities are not provided with adequate infrastructure to run this type of test. In this paper, we propose a computational tool to deal with undesirable contributions coming from non-ideal test conditions over electromagnetic measurements. It is based on auto-tunable filters and therefore it allows a lab technician to run the test without a major knowledge on signal processing. After a series of evaluations based on lab measurements, the proposed tool gives results as good as those provided by two other approaches but with a reduction of the total number of experimental runs. Furthermore, it is more efficient than the traditional time gating approach for low signal-to-noise ratio environments. Finally, a validation with different antennas and test sites confirmes its robustness.

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Section: Adaptive Filtering 130mentioning

confidence: 99%

Improving antenna gain estimations in non-ideal test sites with auto-tunable filters

et al. 2020

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“…The TL method and the second-order BMO method each have advantages and disadvantages. It may be possible to combine the two methods (Lu et al, 2012). In this study, a new method was developed for analyzing TDR waveforms that leverages the strengths of the TL method and the second-order BMO method to achieve accurate, stable, and automatic analysis results.…”

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confidence: 99%

Tangent Line/Second‐Order Bounded Mean Oscillation Waveform Analysis for Short TDR Probe

Wang

Kojima

et al. 2016

Vadose Zone Journal

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Tangent line (TL) methods and the second-order bounded mean oscillation (BMO) method have been proposed for determining the reflection positions of time domain reflectometry (TDR) waveforms, especially for short-probe TDR sensors. However, the accuracy of TL methods is limited by the multi-reflection effects of the short-probe TDR sensor, and an automatic implementation of the second-order BMO is challenging because of the superimposition of the TDR waveforms. In this study, we combined a TL method with second-order BMO to develop a tangent line/second-order bounded mean oscillation (TL-BMO) method. Laboratory and field data were used to evaluate the TL-BMO method. Separate tests were performed on laboratory data to compare the TL-BMO method with the TL method and the second-order BMO method. For selected waveforms, the TL-BMO was more accurate than the TL method (the RMSE of TL-BMO was 0.0197 m 3 m −3 and the RMSE of the TL method was 0.1071 m 3 m −3 ). The TL-BMO was able to avoid calculation errors associated with automatic analysis by the second-order BMO (RMSE of TL-BMO automatic analysis was 0.0199 m 3 m −3 and the RMSE of second-order BMO automatic analysis was 0.1414 m 3 m −3 ). For analyzing field measurements, the TL-BMO method was able to determine soil water contents accurately during a 3-wk-long measurement period. Conclusively, the new TL-BMO method was more accurate than the TL method, and it demonstrated the stability necessary for automatic analysis of short-probe TDR sensors.Abbreviations: BMO, bounded mean oscillation; TDR, time domain reflectometry; TL, tangent line; TL-BMO, tangent line/second-order bounded mean oscillation; T-TDR, thermotime domain reflectometry.

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Combined Filtering Architectures for Complex Nonlinear Systems

Azpicueta-Ruiz

Arenas‐García

Silva

et al. 2018

Adaptive Learning Methods for Nonlinear System Modeling

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A Novel GSC Beamformer Using a Combination of Two Adaptive Filters for Smart Antenna Array

Cited by 10 publications

References 10 publications

Improving antenna gain estimations in non-ideal test sites with auto-tunable filters

Improving antenna gain estimations in non-ideal test sites with auto-tunable filters

Tangent Line/Second‐Order Bounded Mean Oscillation Waveform Analysis for Short TDR Probe

Combined Filtering Architectures for Complex Nonlinear Systems

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