Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider passive joint estimation of the time-difference of arrival (TDOA) and frequency-difference of arrival (FDOA) of an unknown signal at two sensors. The classical approach for deriving the Cramer-Rao bound (CRB) in this context assumes that the signal (as ll as the noise) is Gaussian and stationary. As a result, the obtained Fisher information matrix with respect to the TDOA and FDOA is diagonal, implying that the respective estimation errors are uncorrelated (under asymptotic conditions). Hover, for some specific (non-Gaussian, non-stationary) signals, especially chirp-like signals, these errors can be strongly correlated. In this work  derive a “signal-specific” (or a “conditional”) CRB for this problem: Modeling the signal as a deterministic unknown,  obtain a bound which, given any particular signal, can reflect the possible signal-induced correlation beten the TDOA and FDOA estimates.  further demonstrate that this bound is instrumental for proper ighting when combining joint TDOA and FDOA estimates from independent intervals.
['Arie Yeredor']
I am writing a research paper on a idea A signal-specific bound for joint tdoa and FDOA estimation and its Use in combining multiple segments
[{"from": "human", "value": "I am writing a research paper on a idea A signal-specific bound for joint tdoa and FDOA estimation and its Use in combining multiple segments"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider passive joint estimation of the time-difference of arrival (TDOA) and frequency-difference of arrival (FDOA) of an unknown signal at two sensors. The classical approach for deriving the Cramer-Rao bound (CRB) in this context assumes that the signal (as ll as the noise) is Gaussian and stationary. As a result, the obtained Fisher information matrix with respect to the TDOA and FDOA is diagonal, implying that the respective estimation errors are uncorrelated (under asymptotic conditions). Hover, for some specific (non-Gaussian, non-stationary) signals, especially chirp-like signals, these errors can be strongly correlated. In this work  derive a \u201csignal-specific\u201d (or a \u201cconditional\u201d) CRB for this problem: Modeling the signal as a deterministic unknown,  obtain a bound which, given any particular signal, can reflect the possible signal-induced correlation beten the TDOA and FDOA estimates.  further demonstrate that this bound is instrumental for proper ighting when combining joint TDOA and FDOA estimates from independent intervals."}]