Fumitaka Maeda

Advanced Surveillance Technology for Underwater Security Sonar Systems

On the Japanese coasts, there are so many important facilities which can be exposed to the threats of terror attacks. So, we built an acoustic video system, modifying a dual-frequency acoustic camera DIDSON and developing an optimum underwater surveillance method and software. In succession, a 100 kHz-frequency Wharf Installed Security Sonar was developed. Its transmitting beam is variable in width and can be steered up and down depending on the installation environments, making it possible to easily detect targets. Recently, we have developed an effective moving surveillance method employing a Shipboard Installed Security Sonar consisting of three transducers, each with a different frequency (110 kHz, 240 kHz and 455 kHz) and all sharing a receiving array to monitor both far and near fields. In the 110 kHz mode, the sonar can monitor a far distance and wide area. With the embedded software we developed for overlaying aerial photo maps on sonar images, we obtained data useful for further research to develop an effective surveillance method. We are now proceeding with this development while implementing in this system the moving surveillance method employing the acoustic video camera.

Sub-bottom synthetic aperture imaging sonar system using an AUV and an autonomous surface tracking vehicle for searching for buried shells of toxic chemicals

On Kanda port construction in Fukuoka prefecture, no fewer than 2,800 harmful chemical bombs have been discovered beneath the sea bottom so far. In advance of dredging in the port, we have carefully carried out magnetic inspection for looking for chemical bombs. Since a true target often gets mixed in a lot of suspicious metallic objects, we must cautiously draw up detected objects one by one. Use of a magnetic sensor only is uneconomical and inefficient. On account of these, we started a new three-year project of developing a sub-bottom synthetic aperture imaging sonar (sub-bottom SAS) system by using an autonomous underwater vehicle (AUV) and an autonomous surface vehicle for tracking the AUV. The AUV Tri-Dog 1, which was developed by T. Ura and H. Kondo, is going to be used. The AUV will be installed with a new sub-bottom SAS system and could perform stable and suitable navigation for SAS imaging in shallow water. The surface autonomous tracking vehicle can compensate for positioning error of the AUV and monitor sub-bottom imaging. The synthetic aperture sonar technology for sub-bottom imaging is now in the worldwide limelight, and several projects are ongoing. Since most of chemical bombs were buried up to 5 meters beneath the sea bottom, operating frequency 10 kHz to 30 kHz is expected to be useful for sub-bottom SAS. At the beginning of the project, we measured acoustical characteristics of sub-bottom sediments in Kanda port in 2010 May using a 33kHz single beam echo sounder RESON-Navisound 410 and a parametric sub-bottom profiler Innomar SES2000 Compact. Based on the acoustical characteristics, we will design a sonar projector and hydrophone arrays and a new surface autonomous tracking vehicle in this year.

Automatic signal processing of forward looking surveillance sonar data in low signal-to-noise ratio conditions

In recent years, several surveillance sonar systems have been developed to detect intruders in difficult conditions of coastal waters like ports and harbors. Because man-made noise and reverberation from the bottom and surface are significant in shallow water, the detection process must remove this influence to be robust. Moreover, underwater static objects have strong reflection characteristics, and their echo signal can vary over time. These static objects behave as if they were low-speed, maneuvering, targets like divers. Therefore, they can cause numerous false alarms. These challenges make it difficult to realize an effective sonar-ADT (Automatic target Detection and Tracking) for shallow-coastal harbours. Starting in 2009, our group at the University of Tokyo has developed advanced ADT-techniques that reduces false alarms and improves detection performance using an interferometric method, and delivers improved target-tracking performance under difficult SNR conditions using an original stochastic framework In April 2010, RESON and the University of Tokyo started a collaborative study for automatic data processing techniques dedicated to surveillance sonars. In this paper, we present an overview of the collaborative research project and the current R&D status. Based on the result of our studies and development, we present the results of newly developed ADT techniques that significantly decrease the false alarm rate. We also present improvement of the tracking performance which is influenced by the fluctuations of the underwater target signal using our R&D techniques.

Implementation of machine learning algorism to autonomous surface vehicle for tracking and navigating AUV

On the construction of Kanda port in Fukuoka prefecture, many harmful chemical bombs have been discovered beneath the sea bottom and they are needed to be dug up carefully and quickly as possible. So our group is trying to develop a new sub-bottom interferometric synthetic aperture imaging sonar (sub-bottom interferometric SAS) system to recognize chemical bombs as centimeters-resolution 3D-sub-bottom acoustic image. In this R&D study, it is the reasonable methodology to use an autonomous underwater vehicle (AUV) which can survey the seafloor with sub-bottom interferometric SAS transmitter and receiver at a constant height. To accomplish this R&D goal, positioning AUV accurately is needed, so we are trying to develop the technique which minimizes the error of positioning, using autonomous surface vehicle (ASV) which tracks AUV and surveys its absolute position by super short-baseline (SSBL) method. In development of tracking ASV, it is important to develop the controlling algorism which orders ASV to steer stably and control adequately its velocity according to the result of SSBL positioning of the AUV. Based on machine learning method, we are trying to develop an algorism which infers appropriate control of ASV from precious controlling log. Implementation of this algorism will improve the precision of underwater positioning. This paper reports the development status of our ASV and controlling algorism.