Underwater target positioning technology is the most important part of UnderWater Acoustic Sensor Network(called UWASN), and it is one of the most important research directions in this field with broad application prospects in commercial and military fields. Due to the complex and variability of underwater acoustic environment, the underwater acoustic sensor network has the characteristics of fluidity, sparse deployment and energy limitation, which brings certain challenges to underwater positioning technology. Aiming at the scenario that the node redundancy in the underwater acoustic sensor network leads to low positioning efficiency, this paper considers the sound velocity correction factor based on the traditional anchor node selection algorithm in this paper. Under the premise of ensuring certain positioning accuracy, considering the communication overhead, node residual energy, position suspiciousness, sound ray propagation bending characteristics and other factors, the anchor node optimization mechanism which uses the particle swarm algorithm to iterate out the optimal sensor combination for improving the accuracy of positioning is designed. The simulation results show that the proposed algorithm shows small calculation, fast convergence and high positioning accuracy. It can effectively improve the energy utilization of nodes, balance positioning performance as well as energy use efficiency, and optimize the positioning result of UWASN, which is well suited for underwater acoustic positioning scenarios. INDEX TERMS UWASN, node selection, sound velocity correction, underwater acoustic positioning.
Map matching method is a fundamental preprocessing technique for massive probe vehicle data. Various transportation applications need map matching methods to provide highly accurate and stable results. However, most current map matching approaches employ elementary geometric or topological measures, which may not be sufficient to encode the characteristic of realistic driving paths, leading to inefficiency and inaccuracy, especially in complex road networks. To address these issues, this article presents a novel map matching method, based on the measure of curvedness of Global Positioning System (GPS) trajectories. The curvature integral, which measures the curvedness feature of GPS trajectories, is considered to be one of the major matching characteristics that constrain pairwise matching between the two adjacent GPS track points. In this article, we propose the definition of the curvature integral in the context of map matching, and develop a novel accurate map matching algorithm based on the curvedness feature. Using real-world probe vehicles data, we show that the curvedness feature (CURF) constrained map matching method outperforms two classical methods for accuracy and stability under complicated road environments.
Detection of moving vehicles in aerial video sequences is of great importance with many promising applications in surveillance, intelligence transportation, or public service applications such as emergency evacuation and policy security. However, vehicle detection is a challenging task due to global camera motion, low resolution of vehicles, and low contrast between vehicles and background. In this paper, we present a hybrid method to efficiently detect moving vehicle in aerial videos. Firstly, local feature extraction and matching were performed to estimate the global motion. It was demonstrated that the Speeded Up Robust Feature (SURF) key points were more suitable for the stabilization task. Then, a list of dynamic pixels was obtained and grouped for different moving vehicles by comparing the different optical flow normal. To enhance the precision of detection, some preprocessing methods were applied to the surveillance system, such as road extraction and other features. A quantitative evaluation on real video sequences indicated that the proposed method improved the detection performance significantly.
Due to the mobility of underwater equipment, high-precision underwater positioning technology will face two technical challenges: dealing with mixed-field signals composed of near-field signals and far-field signals; adapting to variable component of mixed-field signals considering the mobility of equipment. Under this condition, an effective method based on MUSIC is addressed in this paper. After distinguishing far-field signal subspace from mixed-field signal subspace, estimations of DOAs and powers of far-field sources are carried out. Then the corresponding far-field and noise signal components can be eliminated from the signal subspace. After that, based on path-following algorithm, modified 2D-MUSIC is performed for DOA and range estimations of near-field sources. The performance of the proposed method is verified and compared with the other methods through computer simulations. Reasonable classification of source types and accurate localization estimation can be achieved by using the proposed method.
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