Abstract
The 5G cellular network is expected to provide core service platform for the expanded Internet of Things (IoT) by supporting enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low latency communications (URLLC). Unmanned aerial vehicles (UAVs), also known as drones, provide civil, commercial, and government services in various fields. Particularly in a 5G IoT scenario, UAV-aided network communications will fulfill an increasingly important role and will require the tracking of multiple UAV targets. As UAVs move quickly, maintaining the stability of the communication connection in 5G will be a challenge. Therefore, it is necessary to track the trajectory of UAVs. At present, the GM-PHD filter has a problem that the new target intensity must be known, and it cannot obtain the moving target trajectory and the influence of the clutter is likely to cause false alarm. A UAV-PHD filter is proposed in this work to improve the traditional GM-PHD filter by applying machine learning to the emergency detection and trajectory tracking of UAV targets. An out-of-sight detection algorithm for multiple UAVs is then presented to improve tracking performance. The method is assessed by simulation using MATLAB, and OSPA distance is utilized as an evaluation indicator. The simulation results illustrate that the proposed method can be applied to the tracking of multiple UAV targets in future 5G-IoT scenarios, and the performance is superior to the traditional GM-PHD filter.
Highlights
The communication industry has experienced massive growth over the past two decades.In the first generation (1G), only analog systems were used
This paper explored the vital role of Unmanned aerial vehicles (UAVs) in future 5G-Internet of Things (IoT) networks
The proposed algorithm was implemented in MATLAB, and optimal subpattern assignment (OSPA) distance was used to evaluate the performance of the UAV-PHD filter
Summary
The communication industry has experienced massive growth over the past two decades. In the first generation (1G), only analog systems were used. It was later discovered that RFS-based multitarget Bayesian filters contain multidimensional integrations that are difficult to implement To solve this problem, Mahler proposed a PHD (Probability Hypothesis Density) filter [17], which uses first-order moment information to approximate multitarget states. Mahler proposed a PHD (Probability Hypothesis Density) filter [17], which uses first-order moment information to approximate multitarget states This theory avoids the problem of data association between observed and state values in traditional multitarget tracking methods. The GM-PHD filter can propagate the posterior probability density function related to multiple targets, and avoids the data interconnection problem between the target and the measured value through recursion This filter is combined with machine learning in this work, and a UAV-PHD filter is proposed to achieve multitarget tracking of UAVs. The remainder of this paper is organized as follows.
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