On Unbiased Parameter Estimation and Signal Reconstruction
Provides theoretical foundations for unbiased estimation in compressed sensing and imaging, but the results are primarily theoretical with no concrete performance numbers, making it incremental for practitioners.
This paper extends the theory of unbiased parameter estimation to arbitrary numbers of non-zero parameters, deriving upper bounds on recoverable parameters in noiseless settings and a probability measure for correct magnitude order. It explains noise robustness of unbiased methods and reveals a sensor-number vs. SNR trade-off.
In this paper, we expand the theory of depth-unbiased source localization to unbiased parameter estimation and signal reconstruction of an arbitrary number of non-zero parameters to be recovered. The topic touches on the concept of exact reconstructibility, most commonly known in compressed sensing and multisource estimation in various imaging problems. The theoretical results derive upper bounds on the number of recoverable parameters in the noiseless case, and a probability measure is defined to assess the probability of obtaining all non-zero parameters with correct magnitude order. The work provides a mathematical explanation of the open question regarding the noise robustness of standardized and unbiased methods. Also, the paper reveals a trade-off between the number of sensors and the signal-to-noise ratio. Numerical experiments demonstrate the theoretical findings.