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Related Concept Videos

Active Filters01:25

Active Filters

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Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Aliasing01:18

Aliasing

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
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Passive Filters01:27

Passive Filters

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Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Phase-lead and Phase-lag Controllers01:22

Phase-lead and Phase-lag Controllers

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Understanding the working function of different types of controllers can be illustrated with practical analogies, such as adjusting a stereo's volume equalizer. Cranking up the bass involves a phase-lead controller, which functions as a high-pass filter, while increasing the treble uses a phase-lag controller, which acts as a low-pass filter. PD controllers, similar to high-pass filters, enhance the system's response to high-frequency components. PI controllers, akin to low-pass...
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Fuzzy Logic-Based Adaptive Filtering for Transfer Alignment.

Zhaohui Gao1, Jiahui Yang2, Chengfan Gu3

  • 1School of Electronic Engineering, Xi'an Shiyou University, Xi'an 710065, China.

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|August 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a fuzzy logic adaptive filter to improve strapdown inertial navigation system (SINS) transfer alignment accuracy. The new method enhances SINS state estimation by effectively managing system model errors, achieving over 18% higher accuracy.

Keywords:
adaptive robust filteringfuzzy logic theorystrapdown inertial navigationtransfer alignment

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Area of Science:

  • Navigation Systems
  • Control Theory
  • Signal Processing

Background:

  • Strapdown inertial navigation systems (SINS) require accurate transfer alignment for airborne tactical vehicles.
  • System model errors in Kalman filters degrade SINS state estimation accuracy.

Purpose of the Study:

  • To develop a fuzzy logic-based adaptive filtering method for SINS transfer alignment.
  • To mitigate the impact of system model errors on SINS state estimation.

Main Methods:

  • Designed a fuzzy logic-based adaptive filtering approach for SINS transfer alignment.
  • Embedded state and measurement error models with residuals into the Kalman filter framework.
  • Utilized fuzzy rules to estimate system measurement and predicted state covariances by minimizing residuals.

Main Results:

  • The proposed method effectively handles system model errors in SINS transfer alignment.
  • Achieved at least 18.83% higher accuracy compared to benchmark methods.
  • Simulations and experiments validated the method's performance.

Conclusions:

  • The fuzzy logic adaptive filter significantly improves SINS transfer alignment accuracy.
  • This approach offers a robust solution for airborne tactical vehicle navigation.
  • The method demonstrates superior performance in managing model uncertainties.