论文标题
使用ADS-B签名的多个无人机的关节范围和相位偏移估计
Joint Ranging and Phase Offset Estimation for Multiple Drones using ADS-B Signatures
论文作者
论文摘要
本文提出了一种对多个无人机/飞机进行联合范围和相位偏移(PO)估计的新方法。该提出的方法采用了由无人机/飞机广播的叠加不协调的自动依赖监视广播(ADS-B)数据包,用于关节范围和PO估计。它在ADS-B数据包解码之前共同估计范围和PO;因此,当数据包碰撞导致数据包解码时,它可以提高空气安全。此外,它可以对ADS-B数据包进行连贯的检测,这可以使用合作传感器检测和避免(DAA)在航空系统中更可靠的多个目标跟踪。通过将Kullback Leibler Divergence(KLD)统计距离度量降至最低,我们表明,来自K的无人机发出的复杂的基带信号可以通过单个天线接收器的添加性白色高斯噪声(AWGN)损坏,可以通过独立且相同分布的高斯混合物(GM)与2个电源组合的平面(gm)近似于两粒粒度(两粒)。虽然来自派生的GM概率密度函数(PDF)的直接关节最大可能性估计(MLE)和PO导致了可怕的最大化,但我们的提议方法采用了期望最大化(EM)算法来估算2D Gaussian混合物的模式,然后通过对组合技术的重新估算范围和PO po estatimate andimate andimate andimate andimate andimate andimate andimate andimate andimate andimation antimate andimation antimate和PO估算。本文还研究了多个天线接收器的扩展。虽然提出的估计器可以估计具有单个接收天线的多个无人机的范围,但通过在接收器处使用多个天线,可以以更高精度来支持更多的无人机。拟议估计器的有效性由模拟结果支持。我们表明,所提出的估计器可以准确地估算三个无人机的范围。
A new method for joint ranging and Phase Offset (PO) estimation of multiple drones/aircrafts is proposed in this paper. The proposed method employs the superimposed uncoordinated Automatic Dependent Surveillance Broadcast (ADS-B) packets broadcasted by drones/aircrafts for joint range and PO estimation. It jointly estimates range and PO prior to ADS-B packet decoding; thus, it can improve air safety when packet decoding is infeasible due to packet collision. Moreover, it enables coherent detection of ADS-B packets, which can result in more reliable multiple target tracking in aviation systems using cooperative sensors for detect and avoid (DAA). By minimizing the Kullback Leibler Divergence (KLD) statistical distance measure, we show that the received complex baseband signal coming from K uncoordinated drones corrupted by Additive White Gaussian Noise (AWGN) at a single antenna receiver can be approximated by an independent and identically distributed Gaussian Mixture (GM) with 2 power K mixture components in the two dimensional (2D) plane. While direct joint Maximum Likelihood Estimation (MLE) of range and PO from the derived GM Probability Density Function (PDF) leads to an intractable maximization, our proposed method employs the Expectation Maximization (EM) algorithm to estimate the modes of the 2D Gaussian mixture followed by a reordering estimation technique through combinatorial optimization to estimate range and PO. An extension to a multiple antenna receiver is also investigated in this paper. While the proposed estimator can estimate the range of multiple drones with a single receive antenna, a larger number of drones can be supported with higher accuracy by the use of multiple antennas at the receiver. The effectiveness of the proposed estimator is supported by simulation results. We show that the proposed estimator can jointly estimate the range of three drones accurately.