In recent times, there has been a surge of interest around the usage of adaptive antenna arrays of Internet of Things (IoT) based Drones in the communication systems. Adaptive antenna arrays have the ability to form customized radiation patterns based on the changes in the environment by employing methods for estimating Direction of Arrival (DOA) and adaptive beamforming. Nevertheless, upon deploying adaptive antenna arrays in complex IoT platforms, the radiation patterns that result from the use of such adaptive algorithms may be adjusted to the preceding location of the node and not attuned to the current location. These issues that arise due to mobility can be resolved by continuously tracking the DOA of the intended target. As DOA is time varying in an IoT Drone environment, existing algorithms for estimating the DOA like MUltiple SIgnal Classification (MUSIC) and Estimation of Signal Parameter via Rotational Invariance Techniques (ESPRIT) cannot be used to track the signal subspace recursively, as they are based on batch eigenvalue decomposition which is highly time consuming with a time complexity of $\mathrm{O}\left({\mathrm{n}}^3\right)$. Furthermore, DOA estimation algorithms do not result in robust subspace estimates when the Signal to Noise Ratio (SNR) is low.The main novelty of the proposed work is a low computational complexity subspace tracking algorithm for tracking DOA in order to provide seamless connectivity. Simulation results show that the proposed DOA tracking takes lesser time for tracking the current location of the drone target as opposed to conventional DOA estimation methods. Furthermore,it is observed that the tracking process remains unaffected by SNR.

最近，围绕在通信系统中使用基于物联网 (IoT) 的无人机的自适应天线阵列的兴趣激增。自适应天线阵列能够​​通过采用估计到达方向 (DOA) 和自适应波束成形的方法，根据环境的变化形成定制的辐射图。然而，在复杂的物联网平台中部署自适应天线阵列时，由于使用此类自适应算法而产生的辐射模式可能会调整到节点的先前位置，而不是与当前位置调谐。这些由机动性引起的问题可以通过持续跟踪预定目标的 DOA 来解决。由于 DOA 在物联网无人机环境中随时间变化，$\mathrm{哦}\left({\mathrm{n}}^3\right)$. 此外，当信噪比 (SNR) 低时，DOA 估计算法不会产生鲁棒的子空间估计。所提出的工作的主要新颖之处是一种低计算复杂度的子空间跟踪算法，用于跟踪 DOA 以提供无缝连接。仿真结果表明，与传统的 DOA 估计方法相比，所提出的 DOA 跟踪需要更少的时间来跟踪无人机目标的当前位置。此外，观察到跟踪过程不受 SNR 的影响。