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Mean time for target acquisition in collaborative search with multiple imaging sensors

机译:使用多个成像传感器的协作搜索中目标获取的平均时间

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In this paper the mean time to acquire a stationary target by n stationary imaging sensors is computed using probability theory by making use of the well established result that the detection time for a single imaging sensor is a random variable from an exponential probability density function. Each imaging sensor is characterized by a separate P_m value which describes the probability an observer using that sensor will eventually acquire the target and a separate r value which describes the mean time to acquire the target using that sensor. There is no restriction on the wavelength band used by the imaging sensor. There are no empirical constants in the model presented here and the results are in agreement with and generalize previously published equations. The newly developed equations have been verified by numerical simulations and also yield the expected mean detection time for all limiting values of the input parameters. The code used in the numerical simulations is exhibited. For any given scenario, the separate observer-sensor-target parameters P∞ and τ can be estimated using the NV-IPM model or measured in perception experiments. Thus the input parameters needed by the model are generally available. Comparing results presented here with results from war game simulations such as OneSAF may improve the quality of both products.
机译:在本文中,利用概率论,通过充分确定的结果,即单个成像传感器的检测时间是来自指数概率密度函数的随机变量,使用概率论计算了n个静止成像传感器获取静止目标的平均时间。每个成像传感器的特征在于一个单独的P_m值,该值描述使用该传感器的观察者最终获取目标的概率,一个单独的r值,描述使用该传感器获取目标的平均时间。对成像传感器使用的波段没有限制。这里介绍的模型中没有经验常数,其结果与先前发布的方程式相符并可以推广。新开发的方程式已通过数值模拟进行了验证,并且还为输入参数的所有极限值提供了预期的平均检测时间。展示了用于数值模拟的代码。对于任何给定场景,可以使用NV-IPM模型估计单独的观察者-传感器-目标参数P∞和τ或在感知实验中对其进行测量。因此,模型所需的输入参数通常可用。将此处提供的结果与战争游戏模拟(例如OneSAF)的结果进行比较可能会提高两种产品的质量。

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