Abstract

The A1-sequences and the A2-sequences have been proposed to replace the M-sequence that is generally used to modulate continuous-wave pseudorandom noise lidar. These new sequences, under two hypotheses, provide a reduction in the background noise, which is especially significant in noisy conditions when one uses M-sequences. We show that one of these two hypotheses is not verified for cloudy atmospheric conditions. Thus, the A1- and the A2-sequences cannot be used for such conditions.

© 1998 Optical Society of America

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References

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  1. N. Takeuchi, N. Sugimoto, H. Baba, K. Sakurai, “Random modulation cw lidar,” Appl. Opt. 22, 1382–1386 (1983).
    [CrossRef] [PubMed]
  2. N. Takeuchi, H. Baba, K. Sakurai, T. Ueno, “Diode-laser random-modulation cw lidar,” Appl. Opt. 25, 63–67 (1986).
    [CrossRef] [PubMed]
  3. C. Nagasawa, M. Abo, H. Yamamoto, O. Uchino, “Random modulation cw lidar using new random sequence,” Appl. Opt. 29, 1466–1470 (1990).
    [CrossRef] [PubMed]
  4. M. L. Skolnik, Radar Handbook (McGraw-Hill, New York, 1970), Chap. 20, pp. 18–26.
  5. Y. Emery, “Statistical approach of the lidar signal analysis: experimental and atmospheric error evaluation,” Ph.D. dissertation (University of Geneva, Geneva, Switzerland, 1996).
  6. Y. Emery, C. Flesia, “Effect of signal dynamics: a comparative study between PRN and pulse illuminated lidar systems,” in Advances in Atmospheric Remote Sensing with LIDAR Systems, A. Ansmann, R. Neuber, P. Rairoux, U. Wandinger, eds. (Springer-Verlag, Berlin, 1996), pp. 123–126.
  7. G. P. Anderson, S. A. Clough, F. X. Kneizys, J. H. Chetwynd, E. P. Shettle, “AFGL atmospheric constituent profiles (0–120 km),” AFGL-TR-86-0110 (U.S. Air Force Geophysics Laboratory, Hanscom Air Force Base, Mass., 1986).

1990

1986

1983

Abo, M.

Anderson, G. P.

G. P. Anderson, S. A. Clough, F. X. Kneizys, J. H. Chetwynd, E. P. Shettle, “AFGL atmospheric constituent profiles (0–120 km),” AFGL-TR-86-0110 (U.S. Air Force Geophysics Laboratory, Hanscom Air Force Base, Mass., 1986).

Baba, H.

Chetwynd, J. H.

G. P. Anderson, S. A. Clough, F. X. Kneizys, J. H. Chetwynd, E. P. Shettle, “AFGL atmospheric constituent profiles (0–120 km),” AFGL-TR-86-0110 (U.S. Air Force Geophysics Laboratory, Hanscom Air Force Base, Mass., 1986).

Clough, S. A.

G. P. Anderson, S. A. Clough, F. X. Kneizys, J. H. Chetwynd, E. P. Shettle, “AFGL atmospheric constituent profiles (0–120 km),” AFGL-TR-86-0110 (U.S. Air Force Geophysics Laboratory, Hanscom Air Force Base, Mass., 1986).

Emery, Y.

Y. Emery, “Statistical approach of the lidar signal analysis: experimental and atmospheric error evaluation,” Ph.D. dissertation (University of Geneva, Geneva, Switzerland, 1996).

Y. Emery, C. Flesia, “Effect of signal dynamics: a comparative study between PRN and pulse illuminated lidar systems,” in Advances in Atmospheric Remote Sensing with LIDAR Systems, A. Ansmann, R. Neuber, P. Rairoux, U. Wandinger, eds. (Springer-Verlag, Berlin, 1996), pp. 123–126.

Flesia, C.

Y. Emery, C. Flesia, “Effect of signal dynamics: a comparative study between PRN and pulse illuminated lidar systems,” in Advances in Atmospheric Remote Sensing with LIDAR Systems, A. Ansmann, R. Neuber, P. Rairoux, U. Wandinger, eds. (Springer-Verlag, Berlin, 1996), pp. 123–126.

Kneizys, F. X.

G. P. Anderson, S. A. Clough, F. X. Kneizys, J. H. Chetwynd, E. P. Shettle, “AFGL atmospheric constituent profiles (0–120 km),” AFGL-TR-86-0110 (U.S. Air Force Geophysics Laboratory, Hanscom Air Force Base, Mass., 1986).

Nagasawa, C.

Sakurai, K.

Shettle, E. P.

G. P. Anderson, S. A. Clough, F. X. Kneizys, J. H. Chetwynd, E. P. Shettle, “AFGL atmospheric constituent profiles (0–120 km),” AFGL-TR-86-0110 (U.S. Air Force Geophysics Laboratory, Hanscom Air Force Base, Mass., 1986).

Skolnik, M. L.

M. L. Skolnik, Radar Handbook (McGraw-Hill, New York, 1970), Chap. 20, pp. 18–26.

Sugimoto, N.

Takeuchi, N.

Uchino, O.

Ueno, T.

Yamamoto, H.

Appl. Opt.

Other

M. L. Skolnik, Radar Handbook (McGraw-Hill, New York, 1970), Chap. 20, pp. 18–26.

Y. Emery, “Statistical approach of the lidar signal analysis: experimental and atmospheric error evaluation,” Ph.D. dissertation (University of Geneva, Geneva, Switzerland, 1996).

Y. Emery, C. Flesia, “Effect of signal dynamics: a comparative study between PRN and pulse illuminated lidar systems,” in Advances in Atmospheric Remote Sensing with LIDAR Systems, A. Ansmann, R. Neuber, P. Rairoux, U. Wandinger, eds. (Springer-Verlag, Berlin, 1996), pp. 123–126.

G. P. Anderson, S. A. Clough, F. X. Kneizys, J. H. Chetwynd, E. P. Shettle, “AFGL atmospheric constituent profiles (0–120 km),” AFGL-TR-86-0110 (U.S. Air Force Geophysics Laboratory, Hanscom Air Force Base, Mass., 1986).

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Figures (4)

Fig. 1
Fig. 1

Ground-based lidar: atmospheric response from a cumulus cloud (4–6 km) and a cirrus cloud (10–12 km).

Fig. 2
Fig. 2

Systematic error of the A1-sequence that is due to a large signal gradient between two successive layers. The open circles indicate the middle of each backscattered layer.

Fig. 3
Fig. 3

Systematic error of the A2-sequence that is due to a large signal gradient between two successive layers. The open circles indicate the middle of each backscattered layer.

Fig. 4
Fig. 4

Retrieved atmospheric response in the presence of clouds obtained with an M-sequence (dotted curve), an A1-sequence (solid curve with filled circles), and an A2-sequence (solid curve with open circles). The M-sequence recovers the original atmospheric response perfectly.

Equations (12)

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ϕ aa j = i = 0 N - 1   a i a i + j = N + 1 / 2 0   j = 0 j 0   mod   N mod   N .
y i = η j = 0 N - 1   x i - j   G j + b + n i ,
z i = k = 1 M   y i + k - 1 N .
S l = i = 0 N - 1   z i a i - l .
S ¯ l = i = 0 N - 1   z ¯ i a i - l = η M P 0 N + 1 l / 2 + b ¯ ,
a i * = - 1 i a i ,   i = 0 ,   1 ,     ,   2 N - 1 .
ϕ a * a * k = N 1 - 1 - N   k = 0 k = 2 n - 1 k = 2 n k = N   mod 2 N mod N mod N mod 2 N   n = 1 ,   2 ,     ,   N - 1 / 2 .
S ¯ l * = η NP 0 G l - NP 0 G l + N + P 0 n = 1 N - 1 / 2 G 2 n - 1 + l + G 2 n - 1 + l + N - P 0 n = 1 N - 1 / 2 G 2 n + l + G 2 n + l + N +   i = 0 2 N - 1   a i - l * b i .
a i ** = a i i = 4 m ,   4 m + 1 - a i i = 4 m + 2 ,   4 m + 3 m = 0 ,   1 ,     ,   N - 1 .
ϕ a ** a * k = 2 N 2 0 - 2 - 2 N   k = 0 k = 4 n - 2 k = 2 n - 1 k = 4 n k = 2 N   mod 4 N mod 2 N mod 2 N mod 2 N mod 4 N n = 1 ,   2 ,     ,   N - 1 / 2 .
S ¯ l ** = η 2 NP 0 G l - 2 NP 0 G l + 2 N + 2 P 0 n = 1 N - 1 / 2 G 4 n - 2 + l + G 4 n - 2 + l + 2 N - 2 P 0 n = 1 N - 1 / 2 G 4 n + l + G 4 n + l + 2 N +   n = 1 4 N - 1   a i - l ** b i .
S ¯ l * η NP 0 G l ,     S ¯ l ** 2 η NP 0 G l ,

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