Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field

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.INDEX TERMS GSO, system identification, multi-modal functions, multi-target tracking, anti-UAVs.

A Novel Approach Based on Hybrid Algorithm for Energy Efficient Cluster Head Identification in Wireless Sensor Networks

“…This section explains the proposed hybrid model for cluster head selection in the wireless sensor network. The proposed model is a hybridization version of GSO algorithm [22] and ABC algorithm to determine the least distance between the member node and the cluster head which will reduce the transmission delay and increase the Quality of Service. This hybridization reduces the computational speed and it overcomes the disadvantages of individual algorithms of GSO and ABC algorithm.…”

Cocktail parity problem solution based on modified blind extraction technique

“…Similar challenges must be addressed by animals as well to traverse their intricate auditory environments, escape predators, mate, and find their infants. This includes mammals, penguins, songbirds, and fish [14], [15]. Engineering systems, including anything from smart phones to military communication and surveillance equipment, face a similar difficulty.…”

“…Swarm techniques are established and used in different ways through current years. Like bee colony (BC) technique is interested from bee's performances [15]. Genetic algorithm (GA) is motivated by genetic appliance [16].…”

Exploration-Based Planning for Multiple-Target Search with Real-Drone Results

“…Many ways have been proposed to search for and identify a known or unknown number of dynamic or static targets from measurements taken by mobile agents in robotics [7][8][9][10][11][12][13][14][15], control [16][17][18][19], reinforcement learning [20][21][22][23][24][25][26][27][28][29], multi-target filtering [5,[12][13][14]18,23,[30][31][32][33][34], etc. Among all these fields, we focus here on multi-target filtering with an intensity function representation because this framework best accommodates our setting.…”