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Airborne magnetometers are utilized for the small-range search, precise positioning, and identification of the ferromagnetic properties of underwater targets. As an important performance parameter of sensors, the detection range of airborne magnetometers is commonly set as a fixed value in references regardless of the influences of environment noise, target magnetic properties, and platform features in a classical model to detect airborne magnetic anomalies. As a consequence, deviation in detection ability analysis is observed. In this study, a novel detection range model is proposed on the basis of classic detection range models of airborne magnetometers. In this model, probability distribution is applied, and the magnetic properties of targets and the environment noise properties of a moving submarine are considered. The detection range model is also constructed by considering the distribution of the moving submarine during detection. A cell-averaging greatest-of-constant false alarm rate test method is also used to calculate the detection range of the model at a desired false alarm rate. The detection range model is then used to establish typical submarine search probabilistic models. Results show that the model can be used to evaluate not only the effects of ambient magnetic noise but also the moving and geomagnetic features of the target and airborne detection platform. The model can also be utilized to display the actual operating range of sensor systems.

Traditional object tracking is easily affected by deformation, scale changes, illumination changes, partial occlusions and so on. TLD(Tracking-Learning-Detection) is a classic effective algorithm in long-term tracking which can solve these problems well. Meanwhile, the real-time performance of the system should be taken into account while in the actual situation. An improved fast-moving object tracking algorithm based on TLD is proposed in this paper. In the paper, a method of narrowing the region of detection is proposed to effectively minimize the consumption of time, the method is combined with self-prediction of motion direction to ensure the accuracy of detection. To compensate for the possible missing and false detections caused by the reduction of detection region and the changing background, the variance threshold is updated dynamically to let more possible correct bounding boxes pass the variance classifier. Experiments have been conducted to verify the improved TLD algorithm, the results show that our algorithm ensures the accuracy of object tracking and has a good performance on the real-time.

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