Name
Title | ||
|---|---|---|
Information in a photon: Relating entropy and maximum-likelihood range estimation using single-photon counting detectors |
Abstract | ||
|---|---|---|
Range estimation at low light-levels is accomplished using pulsed illumination of the target and time-of-flight measurement of backscattered light using single-photon detectors. Photon arrival statistics for this problem are time-inhomogeneous Poisson point processes where the rate function is determined by the illumination waveform. Given the flexibility to choose from different illumination waveforms, an important design question is - how does the range estimation performance depend on the pulse shape? The maximum-likelihood (ML) range estimation problem is nonlinear and thus it is difficult to analytically compare the estimation performance from different illumination waveforms. In this paper, we present an information-theoretic framework for evaluating ML range estimation performance. We derive relationships between the entropy of the photon arrival observations and the Cramér-Rao lower bound (CRLB) on the range estimate by extending De Brujin's identity and isoperimetric properties for non-Gaussian distributions. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICIP.2013.6738018 | ICIP |
Keywords | Field | DocType |
statistical distributions,photon arrival observations,pulsed illumination,time-inhomogeneous poisson point processes,low light-level range imaging,maximum-likelihood range estimation,time-of-flight,de brujin's identity,backscattered light,lidar,differential entropy,isoperimetric properties,maximum likelihood estimation,cramér-rao lower bound,time-of-flight measurement,de bruijn's identity,nongaussian distributions,cramér-rao lower bound,optical radar,illumination waveforms,single-photon imaging,information-theoretic framework,entropy,photon arrival statistics,rate function,crlb,radar imaging,single-photon counting detectors,ml range estimation performance evaluation,pulse shape | Photon counting,Upper and lower bounds,Point process,Probability distribution,Artificial intelligence,Poisson distribution,Statistical physics,Cramér–Rao bound,Photon,Mathematical optimization,Pattern recognition,Waveform,Mathematics | Conference |
ISSN | Citations | PageRank |
1522-4880 | 1 | 0.37 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongeek Shin | 1 | 21 | 3.47 |
| Ahmed Kirmani | 2 | 86 | 6.68 |
| Vivek K. Goyal | 3 | 2031 | 171.16 |
| Jeffrey H. Shapiro | 4 | 153 | 22.84 |