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

机译:具有多个成像传感器的协同搜索中目标采集的平均时间

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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,通过利用概率理论使用概率理论来计算通过建立的概率理论来计算静止成像传感器的平均时间,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。每个成像传感器的特征在于单独的P_M值,该P_M值描述了使用该传感器最终获取目标的观察者和单独的R值,该概率和单独的R值描述使用该传感器获取目标的平均时间。成像传感器使用的波长带没有限制。这里呈现的模型中没有经验常数,结果与先前公布的方程一致并概括。通过数值模拟验证了新开发的等式,并产生了输入参数的所有限制值的预期平均检测时间。展出了数值模拟中使用的代码。对于任何给定的场景,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或者在感知实验中测量。因此,通常可用模型所需的输入参数。比较结果在此显示的战争游戏模拟等结果可以提高两种产品的质量。

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