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Passive Filters01:27

Passive Filters

1.0K
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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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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What are Estimates?01:06

What are Estimates?

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
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Muscles that Move the Head01:19

Muscles that Move the Head

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The muscles that move the head are a dynamic and complex group of structures that work together to facilitate a wide range of head movements, including rotation, flexion, extension, and lateral bending.
The bilateral sternocleidomastoid, or SCM, and the suprahyoid and infrahyoid muscles are significant head flexors. The SCM muscles originate at the sternum and clavicle and attach to the mastoid process of the temporal bone. The SCM contracts bilaterally to bend the head forward, whereas...
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Estimation of k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

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Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
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Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Related Experiment Video

Updated: Feb 8, 2026

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
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Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

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Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering.

Dongjin Wu1, Linyuan Xia2, Jijun Geng3

  • 1School of Geography and Planning, SunYat-Sen University, 135 # Xingangxi Road, Guangzhou 510275, China. wudj3@mail.sysu.edu.cn.

Sensors (Basel, Switzerland)
|June 21, 2018
PubMed
Summary

This study introduces a robust adaptive Kalman filtering (RAKF) method to improve pedestrian dead reckoning (PDR) heading accuracy. The RAKF approach enhances indoor localization by reducing accumulated errors from smartphone sensors.

Keywords:
MEMS sensorsheading estimationpedestrian dead reckoningrobust adaptive Kalman filteringsmart phone

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Area of Science:

  • Ubiquitous computing
  • Sensor fusion
  • Localization algorithms

Background:

  • Pedestrian dead reckoning (PDR) using smartphone micro-electro-mechanical system (MEMS) sensors is crucial for indoor/outdoor localization.
  • PDR's relative nature causes error accumulation, limiting long-term autonomous operation.
  • Heading estimation errors are a primary source of location inaccuracies in PDR.

Purpose of the Study:

  • To enhance pedestrian dead reckoning (PDR) performance in complex environments.
  • To propose a novel approach for accurate heading estimation.
  • To improve the robustness and accuracy of PDR-based localization.

Main Methods:

  • Fusion of gyroscope, accelerometer, and magnetometer data using Kalman filtering (KF).
  • Correction of integrated states from angular rates using heading measurements from acceleration and magnetic field data.
  • Implementation of a maximum likelihood-type estimator (M-estimator) for outlier identification and control.
  • Application of an adaptive factor to mitigate state model disturbances.

Main Results:

  • The proposed robust adaptive Kalman filtering (RAKF) approach yields more accurate heading estimates.
  • RAKF demonstrates more robust and dynamic adaptive location tracking compared to conventional KF.
  • Experimental validation under static and dynamic indoor conditions confirmed the effectiveness of the RAKF method.

Conclusions:

  • The RAKF method significantly improves heading accuracy in PDR systems.
  • This approach enhances the reliability and precision of PDR-based localization, especially in challenging environments.
  • RAKF offers a superior alternative to conventional Kalman filtering for PDR heading estimation.