Abstract

A deconvolution technique for deriving more resolved signals from lidar signals with typical CO2 laser pulses is proposed, utilizing special matrices constructed from the temporal profile of laser pulses. It is shown that near-range signals can be corrected and small-scale variations of backscattered signals can be retrieved with this technique. Deconvolution errors as a result of noise in lidar data and in the laser pulse profile are also investigated numerically by computer simulation.

© 1997 Optical Society of America

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References

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    [CrossRef]

1995 (2)

1993 (1)

1988 (2)

1985 (1)

1983 (1)

1981 (1)

Adam, P.

Baker, P. W.

Carlisle, C. B.

Carr, L. W.

Chiaroni, J. P.

Dreischuh, T. N.

Gurdev, L. L.

Hardesty, R. M.

Kavaya, M. J.

Klett, J. D.

Lea, T. K.

Menzies, R. T.

Schotland, R. M.

Stoyanov, D. V.

van der Laan, J. E.

Zhao, Y.

Appl. Opt. (6)

J. Opt. Soc. Am. A (2)

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Figures (2)

Fig. 1
Fig. 1

Original lidar signals and retrieved lidar signals with different laser pulse shapes: (a) in an infinitely short pulse limit (δ-function laser pulse profile), (b) with a laser pulse of 1-µs duration including a 100-ns gain-switching peak, (c) with a laser pulse of 2-µs duration including a 100-ns gain switching peak.

Fig. 2
Fig. 2

Plots of noise after deconvolution versus the sampling interval: (a) the effect of stationary white noise in original lidar data and (b) the effect of 5% fluctuation of laser pulse power. The noise ratio in (a) means the ratio of noise after deconvolution to noise before deconvolution. The power fluctuation in (b) means power fluctuation in the retrieved signal around noise-free retrieved signals. Each point represents the averaged value and each error bar represents the standard deviation of each data point for a total of 20 measurements.

Tables (1)

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Table 1 Parameters of the Model Lidar System and Atmosphere

Equations (5)

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Pδt=2Rc=ELξc2ARR2βRexp-20Rαrdr,
Pt=0TLtPδt-tdt, 0TLtdt=1,
Pt1Pt2Pt3PtnPtn+1=TLt100000TLt2TLt10000TLt3TLt2TLt12000TLtnTLtn-1TLtn-2TLt100TLtnTLtn-1TLt2TLt1Pδt1Pδt2Pδt3PδtnPδtn+1,
MT-1=θ1000θ2θ100θnθn-1θ10θn+1θnθ2θ10,
θ1=-1Tt1,θi=1Tt1k=2iTtkθi+1-ki=2,3,4,.

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