PPubMed8 Feb 2021Cited 10×

Research on DOA Estimation Based on Acoustic Energy Flux Detection Using a Single MEMS Vector Hydrophone

Xiaoyong Zhang, Guojun Zhang, Zhenzhen Shang, Shan Zhu, Peng Chen, Renxin Wang, Wendong Zhang, Huikai Xie

Abstract

'Renxin Wang' 'Wendong Zhang' 'Huikai Xie'] The principle of acoustic energy flux detection method using a single micro electromechanical system (MEMS) vector hydrophone is analyzed in this paper. The probability distribution of acoustic energy flux and the weighted histogram algorithm are discussed. Then, an improved algorithm is proposed. Based on the algorithm, the distribution range of the energy is obtained by a sliding window, the energy center of gravity in the range is considered as the result of direction of arrival (DOA) estimation, and it is proved to be the maximum likelihood estimation of the target direction. The simulation results show that, with the signal to noise ratio (SNR) from −10 dB to 10 dB, the root mean square error (RMSE) of the improved algorithm is reduced by 47.8% on average, and is more accurate in the presence of interference. The experimental results of lake test are consistent with the theory analysis and simulation results.

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An improved weighted‑histogram algorithm using a single MEMS vector hydrophone cuts DOA estimation RMSE by ~48% versus the original, validated by simulation and lake tests.

What’s new

i.Analyzed the acoustic energy flux detection principle for a single MEMS vector hydrophone
ii.Proposed an improved weighted‑histogram algorithm that uses the energy center of gravity as the DOA estimate and proved it is a maximum‑likelihood estimator
iii.Demonstrated the algorithm’s performance with simulations across SNRs and interference levels
iv.Validated the method experimentally in a lake environment, confirming simulation results

How they did it

The authors derived the acoustic energy flux equations for a single MEMS vector hydrophone and identified shortcomings of the original weighted‑histogram peak‑search approach. They introduced an improved algorithm that computes the energy center of gravity within a selected angular range, showing it corresponds to the maximum‑likelihood direction. Performance was evaluated through Monte‑Carlo simulations (varying SNR, resolution, and interference) and a field experiment in a lake using a calibrated hydrophone system.

What they found

RMSE of the improved algorithm is reduced by 47.8% on average for SNRs from –10 dB to 10 dB
When interference‑to‑noise ratios are –10 dB and –3 dB, the improved algorithm’s RMSE drops by 54.5% and 62.6% respectively compared to the original
In a high‑SNR lake test (41 dB), both algorithms achieve 0.1° RMSE, matching the statistical interval
At low SNR (0 dB) in the lake test, the original algorithm RMSE is 4.78° while the improved algorithm achieves 2.21°

Caveats

✕Peak‑searching in the original weighted‑histogram algorithm is unreliable and can miss the true direction
✕Under strong noise, false energy peaks may cause estimation bias
✕The analysis assumes isotropic Gaussian white noise and far‑field conditions, which may not hold in all underwater environments
✕Resolution selection involves a trade‑off: too coarse reduces multi‑target capability, too fine lowers sample count for reliable estimation

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