Automatic Underwater Multiple Objects Detection and Tracking Using Sonar Imaging
Shi Zhao
Submitted in Fulfilment of the requirements for the degree of Master of Engineering Science School of Mechanical Engineering The University of Adelaide Adelaide, South Australia
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Then the contributions of this research are described. Finally, future possible research directions are outlined. 6.1 Summary Navigation is of paramount importance for a truly autonomous vehicle. Detecting and tracking potential obstacles which may threaten underwater vehicles is considered in this research. In Chapter 2, a brief overview of the literature and recent approaches were presented. The applications focused on ESIS and MSIS were investigated. The review finally led to the objectives of this study. Chapter 3 concerned the principle mechanisms of underwater sound and sonar imaging. The criteria of sensor selection were also described. It concluded a simple experiment, which was designed for examining the ability of the mechanically scanned imaging sonar (MSIS) and providing a reference data for the future target object selection. Chapters 4 and 5 presented two different approaches according to two distinctive underwater domains and proved the validity of the approaches with real sensor data. Chapter 4 firstly described the reverberation suppression filter for confined environments based on the understanding of sound propagation in such structured environments. It secondly investigated the detection and tracking algorithms. The multiple range approach successfully and accurately detected small static and moving objects, which alleviates the system requirements for the tracking. Meanwhile, the trajectories of the target objects were recorded by nearest neighbour tracking method. Furthermore, the enclose environment was also detected and a 129 mathematical map was built by least square curve fitting. Finally, designed experiments carried out in an elliptic test tank in chapter 4 demonstrated a conclusive result of the detector developed by image processing techniques. Chapter 5 provided a more intelligent approach to filter out the coloured reverberation in very shallow water (about 2 to 3 meters). Firstly, it described a detection method according to a priori knowledge using image processing techniques. Secondly, it explained the reason for the selection of fuzzy logic for automatic detection in natural shallow water. Thirdly, a close inspection of the single ping was taken and fuzzy logic was applied to the detector. The fuzzy logic detector in Chapter 5 originated from the reverberation filter. However, it proved to be more powerful and robust than its predecessor. Finally, in a similar vein, experimental data collected in the River Torrens validated the fuzzy approach. The developed navigation system can be explained by the flow chart in Figure 6-1. 6.2 Contributions Reverberation and ambient noise are two major issues which interfere with measurements of active sonar in a very shallow water environment. The similarity between small obstacles and weak reverberation in structured environments causes false alarms and miss detections which may lower the efficiency or threatens the safety of the AUV in an exploration. On the other hand, natural shallow water environments are always filled with rocks and coral on the muddy bottom. The bottom reverberation from these surfaces is much stronger than small object echoes. It is a challenge problem to filter out the non stationary and coloured reverberation noise and remain the object information unaltered. Different and stand out from other approaches in the literature, the thesis examined object echoes in a natural way with a mechanically scan image sonar (MSIS) and provided a series of solutions for automatically detecting small obstacles. Furthermore, to simulate the dangerous explosive ordnances, obstacles introduced in the study were all in the centimetre scales. The selection of such small size obstacles and the 130 algorithm for detecting such small size obstacles using a low resolution sonar senor are very rare throughout the literature survey. Fig. 6-1: Flow chart for object detection using imaging sonar. 131 The work presented in this thesis achieved the proposed goal for the AUV’s navigation contributes the following achievements: Reverberation suppression filter: Even weak reverberation energy picked up by the sonar hydrophone will affect the detection of the small underwater objects. The designed filter reserves the useful information and eliminates interferences for further processing. Fuzzy logic detector: Although it is inherited from the reverberation suppression filter, the fuzzy logic estimation performs robustly and reliably in dealing with the strong reverberation in very shallow water environments. While the sonar sensor operates in very shallow water, the noisy background will encompass the real obstacles. The use of traditional image processing techniques may exclude valuable information for navigation. Thanks to the fuzzy logical technique, the estimation process appraises the echoes and identifies the real object in each single ping. Instead of reproducing acoustic image in a Cartesian system, it can fully support the navigation and obstacles avoidance missions independently. Real-time processing: Various approaches were published for automatic detection using imaging sonar. However, to process the sensorial data in real time, a common method is to extract point features of object echoes from acoustic image by identifying the peak intensity values. The actual size and shape of the obstacles are always missing using this method. This study dedicated to an architecture of real time processing, the developed algorithms were strived to concise and effective. The developed detector for confined environment has proved to run in real time. The sizes, shapes and the distances information of obstacles can be updated in every 4 seconds (determined by sonar scan period). This result was achieved using a P4 2.4GHz, 1GB RAM computer when sonar scan range was 6 meters. Feature extraction for pre-known structured environment: The method for extracting the features from the acoustic image is another contribution of this study. The presented method successfully processes the continued data flow from the sonar sensor with a high level of accuracy. It provides opportunities for the simplification of the object 132 tracking algorithm. Moreover, the inner walls of the enclosed surroundings are also extracted. The results directly lead to the final mathematical mapping algorithm. 6.3 Further research In this dissertation, a mechanically scanned imaging sonar data processing system is interpreted. It raises several interesting extensions of the system and algorithms in both theoretical and practical performances. Sensor Data fusion: The imaging sonar system acts like human eyes to perceive the surrounding objects for the vehicle. To build a true autonomous underwater vehicle, it should cooperate with other sensory systems, such as DVL (Doppler Velocity Log) and gyro to estimate a vehicle’s pose and underwater acoustic modem for communication. The observations from different sensor obtained at different times contribute to the perception of the external environment for the AUV. Data fusion is an estimation process which combines the observations into a coherent description of the environment. The motion of the vehicle will distort the acoustic image when the sensor scanning speed is relatively slow in comparison to the speed of the vehicle. Such distortion may lead to poor estimation results of the objects. In such situations, the displacements and rotations (pose of the vehicle) can be incorporated to correct any distorted acoustic images. Another extension for the data fusion is the development of the localization system. The current algorithm is sufficient while the vehicle is static or moves at low speed (less than 1.5 m/s). Although the proposed localization algorithm has shown promising results, more work needs to be done to successfully localize the vehicle in real time. Moving objects tracking: The traditional nearest neighbourhood tracking is a simple but compelling algorithm. It exhibits adequate reliability in the discrete time data flow while the detection results are accurate. However, the application of the nearest neighbour in an environment where spurious detections occur frequently will lead to poor results. This is mainly because one important step is missing: associating the current 133 measurement with the previous one. That is to say, the measurement used for tracking might have originated from a source different with the target of interest. Intelligent control system: Intelligent control system is the brain of the autonomous vehicle. Measurements from the sensors will integrate and process the system to command and control the vehicle’s motion. Estimations from the sonar image processing system are mainly used to avoid obstacles in front of the vehicle. The algorithm for collision avoidance and an efficient navigation system is another important issue for future development. AUV operational scenarios: The whole project serves the purpose of the underwater vehicle protecting ports and being used for harbour surveillance missions. The improved and newly developed algorithms need to be tested in the ocean, where new problems may be generated, for example the presence of moving boats in a harbour. The wake caused by the engine will be detected by the sonar sensor. Enhanced ability to gain obstacle information in real time and to achieve guided AUV navigation contributes much of value to the field of autonomous underwater investigation.