minimum requirement of SNR for fft algorithm i will like to ask that does fft algorithm requires any minimum snr for frequency estimation

please also refer some technical documentation in this regard
Some assumptions are made in this paragraph. The signal is sinusoidal. It lands exactly at a frequency domain bin, and has magnitude $a$. No frequency domain interpolation is needed. Frequency estimation will look for and report the bin with the largest magnitude. This can be the signal bin or a noise-only bin. The noise is additive, but its contribution to the signal bin will be ignored as an approximation. The real $\operatorname{Re}(Z)$ and imaginary $\operatorname{Im}(Z)$ components of the noise $Z$ in the noise-only bins are independent and identically distributed (IID) Gaussian random variables of zero expected value.

The probability that the magnitude of the noise, at a given noise-only bin, is lower than $a$ is given by the cumulative distribution function of the Rayleigh distribution:

$$P(|Z| &lt; a) = 1-\operatorname{exp}\left(\frac{-a^{2}}{2\sigma^2}\right),$$

where $\sigma^2 = E\left(\operatorname{Re}(Z)^2\right) = E\left(\operatorname{Im}(Z)^2\right)$, the expected value of both the squared real and imaginary components of $Z$. If there are $N$ bins to look in, then the probability that none of the noise-only bins has higher magnitude than the signal bin is given by:

$$P(|Z| &lt; a)^{N-1}.$$

A conclusion is that the correct bin will be chosen at a higher probability if you can narrow down the frequency range where to look for the signal, making $N$ smaller. It is up to you/someone to decide how much risk of reporting the wrong bin is acceptable. There is more to say about this I'm sure.