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

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 ?

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

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

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

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!

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

Variation of ambient noise level in the Arctic 10 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

Frequency dependence of modes 1 - 40 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

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

Notional acoustic network 30 150 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

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.

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

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

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,… ?