Modern low-altitude unmanned aircraft (UA) detection and surveillance systems mostly adopt the multi-sensor fusion technology scheme of radar, visible light, infrared, acoustic and radio detection. Firstly, this paper summarises the latest research progress of UA and bird target detection and recognition technology based on radar, and provides an effective way of detection and recognition from the aspects of echo modeling and micro motion characteristic cognition, manoeuver feature enhancement and extraction, motion trajectory difference, deep learning intelligent classification, etc. Furthermore, this paper also analyses the target feature extraction and recognition algorithms represented by deep learning for other kinds of sensor data. Finally, after a comparison of the detection ability of various detection technologies, a technical scheme for low-altitude UA surveillance system based on four types of sensors is proposed, with a detailed description of its main performance indicators.

We constructed a high reliability parts-picking device with an active and multi-sensor visual system, that picks up a part from randomly stacked parts. The system determines the orders in which to pick up the parts. To determine the order, we introduce concepts of risks of collapsing a stack by picking, and the ease of manipulator operations. The system examines if the risks exist and estimates the ease. We show that the parts-picking system can achieve high reliability by determining the orders, using the effects of the examination and the estimation. We also give some experimental results.