Oversampling
This article is about signal processing oversampling.
For more information on analyzing oversampling data, see. Oversampling and subsampling in data analysis.
Signal processing, the oversampling process is a signal with a sample rate significantly higher than the straight Nyquist sample rate. In theory, a signal with limited bandwidth can be completely reconstructed when sampled above the Nike line. Nike Straight is defined as twice the bandwidth signal. Oversampling can improve resolution and signal-to-noise ratio, and can help prevent aliasing and phase distortion and relax the performance requirements of the antialiasing filter.
The signal is said to be oversampled with the following coefficients: N times the Nyquist line when sampled at N.
Motivation
There are three main reasons for oversampling.
Anti-aliasing
Oversampling makes analog realization easier. Anti-aliasing filters. [1] Without oversampling, implementing filters with the precise cuts necessary to maximize available bandwidth is very difficult. Nyquist limit. You can relax the design limitations of antialiasing filters by increasing the bandwidth of your sampling system. [2] When sampled, the signal looks like this: Digital filtering and downsampling to the desired sample rate. In modern integrated circuit technology, the digital filters associated with this subsampling are easier to implement than their counterparts. Analog filter Required for systems that are not oversampled.
Solution
In fact, oversampling is implemented to reduce costs and improve performance. Analog-to-digital converter (ADC) or digital-to-analog converter (DAC). [1] Oversampling with a factor of N increases the coefficient N because the dynamic range is also N times the total possible value. However, the signal-to-noise ratio (SNR) increases in amplitude when the uncorrelated noise is added as follows: As the coherent signals are summed, the average increases by N. As a result, the SNR increases as follows: .. sqrt {N} sqrt {N} sqrt {N}
For example, to implement a 24-bit converter, it is sufficient to use a 20-bit converter that can run at 256 times the target sample rate. Combining 256 consecutive 20-bit samples increases SNR by a factor of 16 and effectively adds 4 bits to the resolution to produce a single sample with 24-bit resolution. [3] [a]
The number of samples required to obtain the additional data precision bits.
{mbox {number of samples}} = (2 <n>) <2> = 2 <2n>.
To scale the average sample to a whole number, add bits, the total sample is divided by: n2 2n 2 n
{displaystyle {mbox {scaled mean}} = {frac {sum limits _ {i = 0} ^ {2 ^ {2n} -1} 2 ^ {n} {text {data}} _ {i}} {2 2n} = {frac {sum limits i = 0} 2 2n -1} {text {data}} i} {2 n}}. }
This average is recorded by an uncorrelated noise ADC that contains sufficient signal. [3] Otherwise, for stationary input signals, the sample values are all the same and the average result is the same. Therefore, oversampling did not improve in this case. In similar cases where the ADC does not register noise and the input signal changes over time, oversampling improves the results, but is inconsistent and unpredictable.2 n
Add a bit of dithering to improve dither noise using the resolution oversampling function, noise in the input signal is likely to improve the final result. In many real-world applications, a slight increase in noise deserves a significant improvement in measurement resolution. In practice, raster noise is often placed outside the frequency range of interest, so this noise is filtered in the digital domain to make the final measurement in the frequency range of interest. Resolution and low noise level. [Four]
noise
If multiple samples of the same quantity are obtained with uncorrelated noise, [b] will be added to each sample. This is because, as mentioned above, the uncorrelated signals are loosely coupled and averaged more than the correlated signals. N noise power samples times a factor of N. For example, 4x oversampling improves the signal-to-noise ratio for power by 4x. This equates to a two-fold improvement in voltage.
