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Related Concept Videos

Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Aliasing01:18

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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.
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Downsampling01:20

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Bandpass Sampling01:17

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
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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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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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Digital phased array beamforming using single-bit delta-sigma conversion with non-uniform oversampling.

M Kozak1, M Karaman

  • 1Department of Electronics Systems, Center for Micro-Electronics System Applications (CMSA), University of Westminster, W1M 8JS, London, UK. kozakm@cmsa.wmin.ac.uk

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
|August 2, 2001
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Summary
This summary is machine-generated.

This study introduces non-uniform oversampling delta-sigma analog-to-digital conversion for digital beamforming, improving signal-to-noise ratio in phased arrays. This method enhances phased array processing by overcoming synchronization issues in traditional delta-sigma beamforming.

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Array Signal Processing

Background:

  • Digital beamforming with oversampled delta-sigma analog-to-digital (A/D) conversion offers reduced cost, size, and power for phased arrays.
  • Dynamic delta-sigma beamforming faces signal-to-noise ratio degradation due to synchronization disruption from signal resampling.

Purpose of the Study:

  • To propose and evaluate a novel digital beamforming approach using non-uniform oversampling delta-sigma A/D conversion.
  • To address the synchronization issues and signal-to-noise ratio degradation in existing delta-sigma beamforming techniques.

Main Methods:

  • Implemented a non-uniform sampling scheme with distinct clocks for each array channel.
  • Digitized echo signals using single-bit delta-sigma A/D conversion before coherent beam summation.
  • Processed delta-sigma coded beamsums through a decimation filter for final beamforming outputs.

Main Results:

  • The proposed non-uniform oversampling delta-sigma A/D conversion method effectively mitigates synchronization problems.
  • Emulations using experimental raw RF data demonstrate the performance and validity of the new beamforming approach.
  • Achieved improved signal-to-noise ratio compared to conventional dynamic delta-sigma beamforming.

Conclusions:

  • Non-uniform oversampling delta-sigma A/D conversion is a viable solution for digital beamforming in phased arrays.
  • This technique enhances the efficiency and performance of phased array front-end processing.
  • The method offers a promising alternative for applications requiring high-performance, low-power beamforming.