Underwater Sensor Array Research Articles

A Machine Learning-Based Approach for Auto-Detection and Localization of Targets in Underwater Acoustic Array Networks

The localization and tracking of underwater objects have many applications. In a proactive underwater sensor array, some nodes will periodically broadcast linear frequency modulated (LFM) signals, which will hit the targets, get reflected and received by the other nodes. Depending on the target's position and velocity, the received signals will also be LFM signals of different frequencies and frequency rates. We can use the Fractional Fourier Transform (FrFT) to analyze the received signal's spectrum and find the peak. Based on the location of the peak, the target's distance and radial velocity can be estimated. However, the accuracy is highly dependent on the sampling interval of the spectrum. Smaller sampling interval leads to higher accuracy but also induces considerable complexity. To overcome this issue, we propose a machine learning-based approach to automatically detect the existence of the target, and roughly estimate the peak's location if targets exist. Then over-sampling can be conducted for a small area around the peak, leading to improved accuracy and reduced complexity. The idea is based on the following observation: if a target exists, we will be able to observe an “X” pattern on the spectrum. Extensive simulations are conducted to verify the effectiveness of the proposed architecture.

Simulation research of underwater passive direction finding technology

As one of the indispensable technologies for underwater target detecting, underwater acoustic passive detection technology has always been a hot spot in marine system research. A vector sensor can detect underwater targets by combination with the pressure and vibration velocity information; vector sensor array can obtain higher gain than pressure array with the same number hydrophones. Vector sensor can reduce the length of the underwater sensor array and can detect underwater targets in the case of low signal to noise ratio. This is the goal that many researchers strive to solve for many years; therefore, vector sensor passive detection technology has far-reaching scientific research value.Through the verification of the simulation experiment, the azimuth estimation technique and the adaptive frequency estimation technique are feasible. By estimating the azimuth sequence and frequency sequence of the target radiation signal, the relevant information of the target can be obtained, and so it is of great significance in the research of underwater acoustic detection technology.

The research of passive detection parameter measurement based on vector sensor

As one of the indispensable technologies for underwater target detecting, underwater acoustic passive detection technology has always been a hot spot in marine system research. A vector sensor can detect underwater targets by combination with the pressure and vibration velocity information; vector sensor array can obtain higher gain than pressure array with the same number hydrophones. Vector sensor can reduce the length of the underwater sensor array and can detect underwater targets in the case of low signal to noise ratio. This is the goal that many researchers strive to solve for many years; therefore, vector sensor passive detection technology has far-reaching scientific research value. Through the verification of the simulation experiment, the azimuth estimation technique and the adaptive frequency estimation technique are feasible. By estimating the azimuth sequence and frequency sequence of the target radiation signal, the relevant information of the target can be obtained, so it is of great significance in the research of underwater acoustic detection technology.

Past, Present, and Future of Underwater Sensor Arrays to Measure the Electromagnetic Field Signatures of Naval Vessels

AbstractThe first underwater electromagnetic field sensor arrays were installed during World War II to measure and reduce the signatures of naval vessels and their associated susceptibility to actuating magnetic influence mines. Initially, inductive loops and high permeability cored search coil sensors were installed beneath the seafloor, inside protective nonmagnetic tubes, to measure the magnetic signatures of ships and to actively reduce them using a field cancellation technique called degaussing. Eventually, the sensor arrays were upgraded with triaxial magnetometers and, later, with electric field sensors. The historical developments to improve the underwater electromagnetic measurement arrays from 1940 to the present will be summarized, along with a discussion of potential future innovations that might materialize.

A Study of the Nature and Incidence of Density Currents in a Shallow Glacial Lake

Abstract This paper outlines the findings of a study of sedimentation processes in a shallow glacial lake in which an integrated, basinwide instrumentation network, a multidimensional underwater sensor array, and a central rapid scan data-recording system were employed to obtain detailed information on processes both in and around the lake. The results presented here focus upon the nature and occurrence of density currents in the lake over a two-season period. The general character of the system includes a largely isothermal lake environment, water residency times of only 8–12 days, and considerable sediment throughput with over 30 percent of the incoming sediment load moving through the system. The lack of significant thermal stratification and the tendency toward generally isopycnal conditions precludes a conventional over-, inter-, and underflow model of density currents. It appears instead that an over- and underflow model with homopycnal mixing is more appropriate. The overflows, largely wind driven, are frequent events, and on occasion wind-driven translake overflows occur. Underflows, occurring with density differences as low as 0.19 kg m-3, are also frequent and may reach velocities capable of eroding the finer materials. Core data support many of the findings from the instrumentation network. Finally, the results point to the need for careful measurement of both input and output variables and the significance of even small variations in the entrance conditions on sedimentation processes in the lake.