System Identification with a Limited Bandwidth Input Signal and Region of Interest Given a FIR filter $h[n]$. Its action can described as:

$$
\mathbf{y} = \mathbf{H} \mathbf{x} \\
\mathbf{y} = \mathbf{X} \mathbf{h}
$$

where $\mathbf{H}$ and $\mathbf{X}$ is a Toeplitz matrix.
If $h$ is unknown, Least Squares with a white Gaussian input signal $x[n]$ can be used to find the unknown coefficients:

$$
\hat{\mathbf{h}} = (\mathbf{X}^{T}\mathbf{X})^{-1} \mathbf{X}^T \mathbf{y}
$$

Caveat: $x[n]$ must be white; otherwise the regression matrix $\mathbf{X}^T \mathbf{X}$ is badly conditioned.

The frequency domain information is encoded in the coefficients $h$. However, as can be seen above, the LS algorithm estimates the coefficients with zero prior knowledge; the estimation ONLY depends on the input signal $x$. It does not matter if the system to be identified is an allpass filter or has a notch of 200dB attenuation at $\pi/2$.

Now my question: What do I do if I only care about a small frequency range in $h$ and hence my input signal $x[n]$ does not need to be white?

Example: My Nyquist rate is 10kHz. My unknown system is a lowpass with -3dB at 300 Hz. It has some "weird" frequency behavior around 300 Hz which I want to estimate. I do NOT care about anything beyond, say, 500 Hz.
Additionally, my measurement setup prevents me from using a white input signal. I have a bandwidth limitation of 500 Hz. I cannot change the Nyquist rate.

With Least Squares I cannot identify the system because $x$ is not white (persistently exciting). Regularization/SVD does not help me: It provides a biased solution and still gives me $h$ values that try to estimate the entire frequency range. But I really want to say "Give me the $h$ that describes the unknown system best up to 500 Hz with a 500 Hz input signal"
The reason x[n] must be white is because the solution will effectively spectrally weight the channel response based on the amount of energy present in each spectral frequency location. A white noise source provides equal weight to all frequencies. If energy is not present in any particular frequency bin, a proper solution cannot be found for that frequency.

If you only care about a small band of signals, then I would argue that you can still use the least squared approach. The reason for this is to consider a system that provides band pass filtering: for such a system I could excite the input with a white noise source and use the least squares approach (Wiener-Hopf equations) of comparing the input and output signals which would accurately provide a least squares estimate of the channel. So if you have a band limited signal, as long as that signal is white over your band of interest, it will still provide the accurate solution over that band (and you ignore everything else).