A grey wolf optimization-based track-before-detect (GWO-TBD) method is developed for extended target detection and tracking. The aim of the GWO-TBD is tracking weak and maneuvering extended targets in a cluttered environment using the measurement points of an air surveillance radar. The optimal solution is the trajectory constituted by the points of an extended target. At the beginning of the GWO-TBD, the measurements of each scan are clustered into alternative sets. Secondly, closely sets are associated for tracklets. Each tracklet equals a candidate solution. Thirdly, the tracklets are further associated iteratively to find a better solution. An improved GWO algorithm is developed in the iteration for removal of unappreciated solution and acceleration of convergence. After the iteration of several generations, the optimal solution can be achieved, i.e. trajectory of an extended target. Both the real data and synthetic data are performed with the GWO-TBD and several existing algorithms in this work. Result infers that the GWO-TBD is superior to the others in detecting and tracking maneuvering targets. Meanwhile, much less prior information is necessary in the GWO-TBD. It makes the approach is engineering friendly.
A grating lobe-free silicon optical phased array with large field of view is demonstrated. Antennas with periodically bending modulation are spaced at half wavelength or less. The experimental results show that the crosstalk between adjacent waveguides is negligible at 1550 nm wavelength. Additionally, to reduce the optical reflection caused by the sudden change of refractive index at the output antenna of the phased array, tapered antennas are added to the output end face so that more light will be coupled into the free space. The fabricated optical phased array shows a field of view of 120° without any grating lobes.
Mobile edge computing (MEC), which sinks the functions of cloud servers, has become an emerging paradigm to solve the contradiction between delay-sensitive tasks and resource-constrained terminals. Task offloading assisted by service caching in a collaborative manner can reduce delay and balance the edge load in MEC. Due to the limited storage resources of edge servers, it is a significant issue to develop a dynamical service caching strategy according to the actual variable user demands in task offloading. Therefore, this paper investigates the collaborative task offloading problem assisted by a dynamical caching strategy in MEC. Furthermore, a two-level computing strategy called joint task offloading and service caching (JTOSC) is proposed to solve the optimized problem. The outer layer in JTOSC iteratively updates the service caching decisions based on the Gibbs sampling. The inner layer in JTOSC adopts the fairness-aware allocation algorithm and the offloading revenue preference-based bilateral matching algorithm to get a great computing resource allocation and task offloading scheme. The simulation results indicate that the proposed strategy outperforms the other four comparison strategies in terms of maximum offloading delay, service cache hit rate, and edge load balance.
Large-scale optical fiber phased arrays (OFPAs) are capable of realizing high-power lasers and high-speed beam steering, which are widely used in long-distance detection and communication. However, dephasing occurring from optical fiber jitter and power amplifier noise can reduce beam quality and steering precision in applications. We demonstrate a two-dimensional 64-element OFPA system that employs a stochastic parallel gradient descent algorithm to synchronize the phases and thus achieve high-quality multi-beam output. Using multi-beam steering, the total scan time for covering a certain field of view can be shorter compared to single-beam steering. Moreover, an avalanche photodiode array is used to enhance the precision of the voltage for beam steering. Experimental results show that the peak sidelobe ratio of the main beam achieves 23.7 dB, and the speed of the beam steering between two discretionary angles is 128 kHz.
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