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Passive tracking scheme for a single stationary observer

机译:单个静止观察者的被动跟踪方案

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摘要

While there are many techniques for bearings-only tracking (BOT) in the ocean environment, they do not apply directly to the land situation. Generally, for tactical reasons, the land observer platform is stationary; but, it has two sensors, visible and infrared, for measuring bearings and a laser range finder (LRF) for measuring range. There is a requirement to develop a new BOT data fusion scheme that fuses the two sets of bearing readings, and together with a single LRF measurement, produces a unique track. This paper first develops a parameterized solution for the target speeds, and then heading, prior to the occurrence of the LRF measurement, when the track is unobservable. At, and after the LRF measurement, a BOT, formulated as a least squares (LS) estimator, then produces a unique LS estimate of the target states. Bearing readings from the other sensor serve as instrumental variables in a data fusion setting to eliminate the bias in the BOT estimator. The result is an unbiased and decentralized data fusion scheme. Results from two simulation experiments have corroborated the theoretical development and show also that the scheme is optimal.
机译:尽管有许多技术可以在海洋环境中进行纯方位跟踪(BOT),但它们并不直接适用于陆地情况。通常,出于战术原因,陆地观察员平台是固定的;但是,它具有两个用于测量轴承的可见光和红外传感器以及一个用于测量范围的激光测距仪(LRF)。需要开发一种新的BOT数据融合方案,该方案融合两组轴承读数,并与单个LRF测量值一起产生一条唯一的轨迹。本文首先针对目标速度开发了参数化解决方案,然后在无法观察到轨道时,在进行LRF测量之前前进。在LRF测量时以及测量LRF之后,将BOT公式化为最小二乘(LS)估计器,然后生成目标状态的唯一LS估计。来自另一个传感器的方位读数在数据融合设置中用作工具变量,以消除BOT估算器中的偏差。结果是一个无偏且分散的数据融合方案。两次仿真实验的结果证实了理论发展,并表明该方案是最优的。

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