A Novel Sub-Bottom Profiler and Signal Processor

Abstract

In this paper, we introduce a novel sub-bottom profiler, making good use of the Mills cross configuration of multibeam sonar and synthetic aperture techniques of the synthetic aperture sonar system. The receiver array is mounted along the ship keel, while the transmitter array is mounted perpendicular to the receiver array. With the synthetic aperture technique, the along-track resolution can be greatly improved. The system often suffers from motion error, which severely degrades the imaging performance. To solve this problem, the imaging algorithm with motion compensation (MC) is proposed. With the presented method, the motion error is first estimated based on overlapped elements between successive pulses. Then, the echo data is processed by using the range migration algorithm based on the phase center approximation (PCA) method, which simultaneously performs the MC with the estimated motion error. In order to validate the proposed sub-bottom profiler and data processing method, some simulations and lake trial results are discussed. The processing results of the real data further indicate that the presented configuration has great potential to find buried objects in seabed sediments.

Efficient imaging method for multireceiver SAS

“…At this point, this term is called the QM term. Inspecting (5), this phase is a function of the displaced distance between the transmitter and receiver, that is, d i . Due to this reason, this phase is called the BD term.…”

“…This phase also includes two terms. The first term in (5) is also a function of range r. Considering different characteristics of both terms in (5), they are compensated independently. After compensating the BD term shown in (5), it is expected that the monostatic SAS equivalent data can be obtained.…”

“…Synthetic aperture sonar (SAS) [1–3] dramatically improves the azimuth resolution. Due to this reason, this technique can be used for the seabed mapping [4, 5], marine archaeological surveying [6], target classification [7, 8], and so on. However, the traditional monostatic SAS system configured with a single transmitter and a single receiver possesses the contradiction between the wide swath coverage and high resolution in the azimuth dimension.…”

A Viterbi Decoder under Class A Modeled Noise in Shallow Water

“…The noise distribution [1,2] plays an important role in developing underwater signal processors. Traditional signal processors such as underwater localization [3][4][5][6][7][8][9][10][11][12][13], underwater tracking [9,14,15], sonar imaging [16][17][18][19][20][21][22][23][24][25][26][27], direction of arrival (DOA) estimation [28][29][30][31][32], and underwater acoustic communication (UAC) are mostly based on Gaussian noise, which can be supported by a central limit theorem. Besides, the Gaussian model is just determined by the first-order and second-order statistics [33].…”

Horizon Picking from SBP Images Using Physicals-Combined Deep Learning

“…SBP is a type of sonar system that is designed to produce 2-dimensional stratigraphic cross-sectional images called SBP images [5]. Generally, the transducer of an SBP system converts the control signals to acoustic pulses (100 Hz-10 kHz) and emits the pulses towards the seafloor.…”