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tworitdash

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This question comes from my experience in radar signal processing. As I am going more deep into the theory of sampling, statistical signal processing and estimation theory in general, I have a very silly but important mathematical question that I want to ask here.

For example, we are estimating a physical quantity after getting some information out of a sensor that doesn't directly measure the quantity. The signal from the sensor is sampled in time domain and based on the sampling interval, the quantity of interest lies within a certain bounds (typically this bound is inversely proportional to the sampling time).

For example, to keep it simple, let's say that the estimated quantity is a Doppler velocity. Due to the sampling, we can only measure for example from [itex]-v[/itex] to [itex]+v[/itex]meters per second in frequency/ velocity domain.If we increase our viewing window further, we will see many copies of the same velocity. These are known as ambiguities. These exist because of the sampling I mentioned above and a quantity like velocity is wrapped and scaled in the signal as an angle and an angular variable has limit from [itex]-\pi[/itex] to [itex]+\pi[/itex] and any solution that is outside this limit is a replica of the real solution. Only one of them is true.

It is like saying $$ \cos(2\pi + \theta) = \cos(\theta) $$

I know that as it is a fundamental limitation and people use irregular sampling intervals (with more fancy patterns like Fibonacci, log normal and so on..) to essentially increase the physical limit of the variable to avoid folding. Irrational sampling intervals also may increase this Nyquist limit to a very high value. In this case, there is only one solution.

However, I was wondering if these ambiguities can be distinguished somehow in a more fundamental way from the real one in the case of regular sampling. Do these ambiguities have a distinct feature that can make it separable? I know it is a very silly question, because they are essentially the multiple solutions to the same question. However, it may happen that my understanding is incomplete.

Thanks in advance.

For example, we are estimating a physical quantity after getting some information out of a sensor that doesn't directly measure the quantity. The signal from the sensor is sampled in time domain and based on the sampling interval, the quantity of interest lies within a certain bounds (typically this bound is inversely proportional to the sampling time).

For example, to keep it simple, let's say that the estimated quantity is a Doppler velocity. Due to the sampling, we can only measure for example from [itex]-v[/itex] to [itex]+v[/itex]meters per second in frequency/ velocity domain.If we increase our viewing window further, we will see many copies of the same velocity. These are known as ambiguities. These exist because of the sampling I mentioned above and a quantity like velocity is wrapped and scaled in the signal as an angle and an angular variable has limit from [itex]-\pi[/itex] to [itex]+\pi[/itex] and any solution that is outside this limit is a replica of the real solution. Only one of them is true.

It is like saying $$ \cos(2\pi + \theta) = \cos(\theta) $$

I know that as it is a fundamental limitation and people use irregular sampling intervals (with more fancy patterns like Fibonacci, log normal and so on..) to essentially increase the physical limit of the variable to avoid folding. Irrational sampling intervals also may increase this Nyquist limit to a very high value. In this case, there is only one solution.

However, I was wondering if these ambiguities can be distinguished somehow in a more fundamental way from the real one in the case of regular sampling. Do these ambiguities have a distinct feature that can make it separable? I know it is a very silly question, because they are essentially the multiple solutions to the same question. However, it may happen that my understanding is incomplete.

Thanks in advance.

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