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Due to cost reduction and device miniaturization, amateur drones are now widely used in numerous civilian and commercial applications. However, the abuse of amateur drones has resulted in emerging threats to personal privacy and public security. To alleviate these threats, we design an acoustic-based surveillance system, which can achieve the capacity of amateur drones detection and localization with 24/7 (24 hours per day and 7 days per week under normal circumstances) availability. In the designed system, a detection fusion algorithm and a TDOA estimation algorithm based on the Bayesian filter are applied to improve the performance of drone detection and localization. Field experiments are carried out, and the results demonstrate that the designed system can detect and locate an amateur drone in real time with high accuracy and 24/7 availability.

Introducing the times of papers being cited to reflect the quality and international influence of scientific research, and the reputation to reflect the quality of teaching activity, this paper uses Malmquist Index based on DEA to measure efficiency, technology and productivity change of 72 Higher Educational Institutions (HEIs) directly under Ministry of Education of China in 2007-2012. Results reveal that the Total Factor Productivity (TFP) of HEIs manifests a reasonable growing tendency; the increase on technical efficiency is the mainly driving force of the increase on TFP, while the technological degression has an inhibiting effect; there are significant differences between different HEIs, regions and types. In the end, some suggestions are provided to promote continuous, stable and balance development of HEIs in China.

The wide proliferation of drones has posed great threats to personal privacy and public security, which makes it urgent to monitor the drones in the sensitive areas. Drone surveillance using acoustic arrays, as a promising method, could localize and track the drones effectively. In this paper, we develop a systematic method for drone localization and tracking by using acoustic arrays. Specifically, we develop a time difference of arrival (TDOA) estimation algorithm based on Gauss priori probability density function to overcome the multipath effect and the low signal-to-noise ratio (SNR), and design a localization method by making full use of the TDOA estimation results, followed by tracking the drone by Kalman filter. Finally, field experiments are carried out to verify the effectiveness and performance of the proposed methods. The results show that they can achieve good performance in drone localization and tracking.

Drone localization plays an important role in anti-drone systems, which are designed for alleviating the threats brought by the illegal use of amateur drones. For the realization of drone localization, in this paper, we consider the problem of the direction-of-arrival (DOA) estimation of amateur drones. After formulating the acoustic signals of amateur drones by a harmonic model, we prove the validity of the multiple signal classification (MUSIC) algorithm under the harmonic model, and utilize it to estimate the DOAs of amateur drones. Further, we integrate this algorithm into a real-life drone tracking platform. Extensive field experimental results show that the drone tracking platform is capable of realizing the DOA estimation of an amateur drone with high accuracy.

The wide proliferation of drones has posed great threats to personal privacy and public security, which makes it urgent to monitor and locate intruding drones in sensitive areas. In Direction of Arrival (DOA) based localization, the estimation accuracy of DOA directly affects the localization accuracy. In this paper, we propose a novel algorithm to estimate the DOA of an intruding drone by exploiting its acoustic feature, which is mainly reflected in the strength distribution of the harmonics of the received acoustic signal. Specifically, this algorithm first estimates the harmonic frequencies of the drone's acoustic signal in frequency domain. Then, multiple signal classification is used to estimate the DOAs of all the selected harmonics. Furthermore, weighted sum of these DOA estimates will be taken as the drone's DOA estimate, where the weights are in proportional to the energy of the corresponding harmonics. The performance of the proposed algorithm is verified by both simulation and field experiments.