Abstract
Fast detection and high-precision tracking of multiple UAVs are the keys to achieving efficient low-altitude defense. However, as there are many hyper-parameters which need to be optimized in target detection network, commonly used methods such as random search method are computationally intensive and cannot quickly obtain multiple optimal hyper-parameters combinations. In addition, angular random walk due to low-frequency noise of speed sensor in servo loop can cause target tracking accuracy to decrease. Fortunately, those two problems can be regarded as a single-mode function optimization problem and a multi-mode function optimization problem, respectively. In the paper, in order to overcome the aforementioned problems, GSOM (glowworm swarm optimization mutation) algorithm and GSOMLDW (glowworm swarm optimization mutation linearly decreasing weight) algorithm are firstly proposed. Furthermore, the global convergence of GSOMLDW has been proven in the paper, which has not been analyzed in currently available literature. Then, experimental results on four multi-modal benchmark functions have strongly illustrated that the novel GSOM algorithm can enhance glowworms’ memory ability and improve peak detection rate effectively. When it is used for optimizing hyper-parameters of multi-target detection network, it can be expected to obtain much more hyper-parameter combination selections. Meanwhile, experimental results on ten uni-modal benchmark functions have obviously demonstrated that GSOMLDW algorithm can balance glowworms’ exploration and exploitation abilities powerfully and obtain superior global solution accuracy at last. When the GSOMLDW algorithm is used for servo system identification and drift error model identification, the final position error fluctuation after compensation is almost zero while it reaches $1500~urad$ before compensation. Consequently, the proposed method can effectively improve target tracking precision.
Highlights
With the popularization of UAVs in various fields, their characteristics that they can be equipped with functional equipments casually and are subject to subjective awareness make them to be a serious threat to public safety [1]
When an individual gets into traps and there are no better neighborhoods, the proposed GLOWWORM SWARM OPTIMIZATION MUTATION (GSOM) algorithm will produce a new individual according to the best solution found during search history
Experimental results of four multi-modal benchmark functions have illustrated that the proposed GSOM algorithm can make fully use of all glowworms’ search histories and locate more optima than Glowworm swarm optimization (GSO) algorithm
Summary
With the popularization of UAVs (unmanned aerial vehicles, UAVs) in various fields, their characteristics that they can be equipped with functional equipments casually and are subject to subjective awareness make them to be a serious threat to public safety [1]. X. Xu et al.: Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field be detected. Grid search method and random search method are often used to obtain hyper-parameters for target detection network models. These methods have certain disadvantages such as large calculation amount [4]. Different from other swarm intelligence algorithms, GSO algorithm which is different from FA algorithm [24] owns both local optimum location ability and global optimum location ability That means it is good at solving uni-modal optimization problems and multi-modal optimization problems.
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