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Related Concept Videos

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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The impulse response is the system's reaction to an input impulse. In an RC circuit, the voltage source is the input, and the capacitor's voltage is the output. The system's state and output response before and after input excitation are distinctly defined.
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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
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In the middle of the nineteenth century, it was observed that two trains passing each other at a high relative speed get pulled towards each other. The same occurs when two cars pass each other at a high relative speed. The reason is that the fluid pressure drops in the region where the fluid speeds up. As the air between the trains or the cars increases in speed, its pressure reduces. The pressure on the outer parts of the vehicles is still the atmospheric pressure, while the resultant...
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DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment.

Sun-Yong Wu1,2, Jun Zhao3, Xu-Dong Dong4

  • 1School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China. wsy121991@guet.edu.cn.

Sensors (Basel, Switzerland)
|September 22, 2019
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Summary

This study introduces a new algorithm for tracking multiple sources in impulse noise using the Unscented Transform Multi-target Multi-Bernoulli (UT-MeMBer) filter. The method effectively handles time-varying direction of arrival (DOA) and target counts, improving tracking accuracy.

Keywords:
Multi-Bernoulli filterdirection-of-arrival (DOA) trackingimpulse noiseparticle filtering

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Statistical Signal Processing

Background:

  • Direction of Arrival (DOA) tracking is crucial in various applications, but impulse noise complicates traditional methods.
  • Existing algorithms struggle with the non-Gaussian nature of impulse noise, often modeled by symmetric alpha-stable (SαS) distributions.
  • Particle decay in particle filtering and the lack of finite covariance in SαS distributions pose significant challenges.

Purpose of the Study:

  • To develop a robust Direction of Arrival (DOA) tracking algorithm for multiple sources operating in impulse noise environments.
  • To address the limitations of particle decay and the absence of finite covariance in traditional filtering techniques.
  • To enhance the accuracy and reliability of DOA estimation in challenging noise conditions.

Main Methods:

  • Modeling impulse noise using the symmetric alpha-stable (SαS) distribution.
  • Proposing a Direction of Arrival (DOA) tracking algorithm within the Unscented Transform Multi-target Multi-Bernoulli (UT-MeMBer) filter framework.
  • Utilizing the Unscented Transform (UT) to approximate the posterior probability density and employing Fractional Lower Order Moment (FLOM) matrices with MUSIC spatial spectra to handle SαS distributions.
  • Implementing exponential weighting for improved particle resampling and presenting a Sequential Monte Carlo (SMC) implementation.

Main Results:

  • The proposed UT-MeMBer algorithm demonstrates superior performance in tracking time-varying Direction of Arrival (DOA) and target counts compared to PASTD and standard MeMBer DOA filter algorithms.
  • The algorithm effectively mitigates issues arising from impulse noise by leveraging SαS distribution modeling and FLOM matrices.
  • Simulation results validate the enhanced tracking capabilities and robustness of the developed method.

Conclusions:

  • The proposed UT-MeMBer algorithm offers a significant advancement for multiple-source DOA tracking in impulse noise.
  • The integration of UT, FLOM, and MUSIC spatial spectra within the MeMBer framework provides a robust solution to non-Gaussian noise challenges.
  • The algorithm's effectiveness in handling time-varying scenarios makes it suitable for real-world applications requiring accurate DOA estimation.