1. Alexander Gavrilov, CMST
Long-range acoustic transmissions for navigation, communication, and ocean observation in the Arctic
Alexander Gavrilov, CMST
Peter Mikhalevsky, SAIC

2. OUTLINE
Some examples of long-range acoustic transmissions in the Arctic Ocean (TAP and ACOUS experiments)
Numerical prediction of transmission loss at different frequencies and experimental results
Possible outline of the network
Navigation
Communication
Ocean Observation
4. Problems ?

3. TAP (blue) and ACOUS (red) experiment paths in the Arctic Ocean

4. TAP signal at ice camp SIMI after pulse compression
3500 3
3000
- numerical prediction 4
2500
2000 2
Amplitude, Pa
1500
1000
500 1
1805
1810
1815
1820
1825
1830
1835
Travel time, s
Evidence of multi-path (multi-mode) propagation

5. ACOUS signal and noise levels at individual receivers
of the Lincoln Sea array
(ACOUS source level: 195 dB; distance: ~ 1250 km) 50
100
150
200
250
300
350
400 60 65 70 75 80 85 90 95
105
110
Day number
Signal level, dB re. 1 Pa
Before processing 50
100
150
200
250
300
350
400
450 60 70 80 90
110
120
Day number
Signal level, dB re. 1 Pa
After pulse compression
450
Noise level in a 1-Hz frequency band
Noise level limited by receivers’ dynamic range

6. SNR and coherence of ACOUS signals on
the Lincoln Sea array
SNR before (blue) and after (red) pulse compression
Cross-correlation matrix of 10 periods of ACOUS signal 45 1 1 40
0.99 2 35
0.98 3 30
0.97 4 25
0.96 5
SNR, dB 20
0.95 6 15
0.94 7 10
0.93 8 5
0.92 9
0.91 10 -5
0.9 50
100
150
200
250
300
350
400
450 1 2 3 4 5 6 7 8 9 10
Day number
Period number
Exceptional temporal stability of the channel at 20 Hz!

7. Level of two ACOUS signals (blue) and noise (red) on APLIS vertical array after pulse compression
(Distance: ~2720 km) 80 75 70 65 60
Signal level, dB re. 1 Pa 55
~ 34 dB theoretical limit 50 45 40 35
100
200
300
400
500
600
700
Depth, m

8. Variation of ambient noise level in the Arctic10 1 2 3 65 70 75 80 85 90 95
100
105
Frequency, Hz
Noise level, dB re. 1 Pa/Hz1/2
~90% of time

9. Frequency dependence of modes attenuation modeled for the Central Arctic Basin and some experimental results 10 1 2 -3 -2 -1
Frequency, Hz
Attenuation, dB/km F
1.5
NUSC 1959
FRAM IV, 1982
TAP, 1994 (mode 1)
TAP, 1994 (modes 2-4)
ACOUS, APLIS (mode 1)
ACOUS, APLIS (mode 2)
Ice model parameters: mean ice thickness – 3.5 m; bottom standard deviation – 2.3 m; top standard deviation – 0.6 m; correlation length – 40 m

10. Transmission loss along ACOUS path at 50 m and 400 m modeled for a broadband signal
200
400
600
800
1000
1200 30 40 50 60 70 80
-135
-130
-125
-120
-115
-110
-105
-100
-95
-90
-85
Range, km
Frequency, Hz
Depth: 50 m
Range, km
200
400
600
800
1000
1200 30 40 50 60 70 80
Depth: 400 m
-120
-115
-110
-100
-95
-90
-140
-130
-80
-105
0-dB SNR for a 50-Watt (~190 dB) source
-20-dB SNR for a 50-Watt (~190 dB) source

11. Notional acoustic network30
150 60
120
180 G r e n l a d R u s i C 40 00 5 35 20
2000
ACOUS
source
90W
90E
Autonomous sources
Acoustic observation paths
Cabled transceiver nodes
with shore terminals
Cable
Cabled/autonomous transceiver nodes

12. 3. Observation (thermometry, ice monitoring)
Navigation:
Stationary acoustic sources are to transmit pulse-like signals for accurate measurements of travel times to moving platforms. Nav. signals should also contain certain information (at least source ID numbers, UTC time, etc.).
2. Communication:
Two-way communication is needed to check the operational state (most important) and to track position of mobile platforms
Underwater communication of oceanographic data over long distances does seem feasible
3. Observation (thermometry, ice monitoring)
Feasible for stationary receivers/transceivers. For mobile platforms, it requires accurate timing and complicated interpretation of travel time data.

13. A simple method to design navigational/ communicational/observational signals
Series of two signals: training (observational) signals followed by informational signal = navigational signal
, where
, and
is the M-sequence of length N = 2M - 1
is the Hadamard code of number m < N
Processing: compute the likelihood function:
for each message m, using Hadamard transform

14. Error probability for binary message m at different SNR for two different signal bases
Signal-to-noise ratio
10-1
1.0
0.0
0.05
0.10
0.15
0.20
0.25
M=512
M=1024
10-2
10-3
Error probability

15. Most serious problems
Weight, power consumption and reliability of low-frequency sources, especially for mobile platforms
2. Doppler effect for mobile platforms
3. Slow communication rate
4. Accurate timing for mobile platforms
5. Separation of acoustic thermometry/halinometry data from navigational errors.
6,7,… ?