Angle of arrival (AOA) is widely used to locate a wireless signal emitter. Compared with received signal strength (RSS) and time of arrival (TOA), it has higher accuracy and is not sensitive to time synchronization of the distributed sensors. However, there are few works focused on three-dimensional (3-D) scenario. Furthermore, although maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra high. It is hard to employ it in practical applications. This paper proposed two multiplane geometric center based methods for 3-D AOA positioning. The first method could estimate the source position and angle measurement noise at the same time by seeking a center of the inscribed sphere, called CIS. Firstly, every sensor could measure two angles, azimuth angle and elevation angle. Based on that, two planes are constructed. Then, the estimated values of source position and angle noise are achieved by seeking the center and radius of the corresponding inscribed sphere. Deleting the estimation of the radius, the second algorithm, called MSD-LS, is born. It is not able to estimate angle noise but has lower computational complexity. Theoretical analysis and simulation results show that proposed methods could approach the Cramer-Rao lower bound (CRLB) and have lower complexity than MLE.
翻译:到达角度( AOA) 被广泛用于定位无线信号发射。 与收到的信号强度( RSS) 和到达时间( TOA) 相比, 它的准确性更高, 对分布式传感器的时间同步性不敏感。 然而, 很少有工作侧重于三维( 3- D) 假设情景。 此外, 虽然最大可能性估计器( MLE) 的性能相对较高, 但其计算复杂性是超高的。 很难在实际应用中使用它 。 本文为 3 - D AOA 定位提出了两种多平面测深点中心法 。 第一个方法可以在同一时间通过寻找一个被标定的域中心来估计源位置和角度测量噪音。 首先, 每个传感器可以测量两个角度( 3- D) 角度( 3- D) 和 升角角度。 在此基础上, 建造了两架飞机, 其估计的源位置和角噪音值是通过寻找相应定的球场的中心和半径。 取消对半径的估算, 第二个算法, 称为 MSD- LSD- LS, 是诞生的。 它无法估计角度的定位方法,, 并显示较低的测测算方法, 。 它比 CR- 的测测算法 。 它比 CRR- 比较的测测测测 的 比较低的复杂。