Learn How to Calculate SNR for Vector A in Time Series | Tips & Tricks

In summary, the conversation discusses the calculation of a signal from a vector 'A' that changes over time from a baseline 'B'. The signal is determined by the distance between 'A' and the average of 'B'. The vector 'A' is divided into two groups based on whether their values are higher or lower than the previous values. The standard deviation of the calculated signal is defined as the noise. The conversation then poses a question about how to define the signal-to-noise ratio of 'A' and mentions finding different methods in different contexts but seeking clarification on which one to use.
  • #1
sue132
14
0
Hi,

I have a vector 'A' of length 'n', whose values change with time. 'A' gives the change from a baseline 'B'. I calculate a signal as the distance A from the average of B, i.e.,

If 'B' is my baseline, then the signal at a time 't' due to A(t) would be given by S=(1/n)*(Ʃ1Ai -<B> - Ʃ2Ai -<B>), i=1 to n. 'A' is divided into two groups of elements whose values are higher than their previous values and those whose values are lower.

I define noise as the standard deviation of S.

Given this, how do I define the signal-to-noise ratio of A?

I found different ways of calculating the SNR in different contexts, but am not very clear as to which one to use in this context.

Thanks in advance for any suggestions/help.
 
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  • #2
Dividing S by the standard deviation of S should give a meaningful quantity, if the formula for S itself is meaningful (it has missing brackets here, by the way).
 

1. What is SNR?

SNR stands for Signal-to-Noise Ratio and is a measure of the strength of a signal compared to the background noise in a system.

2. How is SNR calculated?

To calculate SNR, the signal's power is divided by the noise power. The signal power can be measured by taking the average of the squared amplitudes of the signal, while the noise power can be measured by taking the average of the squared amplitudes of the noise.

3. Why is SNR important?

SNR is important because it is used to determine the quality of a signal. A higher SNR indicates a stronger signal and a lower SNR indicates a weaker signal, which can affect the reliability and accuracy of data collected from a system.

4. What is a good SNR value?

The ideal SNR value can vary depending on the application, but generally a higher SNR is desired. For example, in wireless communication, an SNR of 20 dB or higher is considered good, while in medical imaging, an SNR of 50 dB or higher is preferred.

5. How can I improve the SNR in my system?

To improve SNR, you can increase the signal power or decrease the noise power. This can be achieved by using better quality components, reducing interference, or using signal processing techniques such as filtering.

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