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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Detection, Location Estimation, and CRLB of a Streaking Target in an FPA With a Poisson Model
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Detection, Location Estimation, and CRLB of a Streaking Target in an FPA With a Poisson Model

机译：用泊松模型在FPA中检测，位置估计和条纹目标的CRLB

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This paper deals with measurement extraction, from an optical sensor's Focal Plane Array (FPA), of a streaking target. We use a model that assumes pixels are separated by dead zones and model the streaking target's point spread function (PSF) as a Gaussian PSF that moves during the optical sensor's integration time. We make an assumption that the target has a constant velocity over the sampling interval and parametrize its motion with a starting and ending position. The noise model for a single pixel has variance proportional to its area, consistent with a Poisson model of the number of nontarget originated photons. We develop a maximum likelihood (ML) method of estimating the target motion parameter vector based on the set of pixel measurements from the optical sensor. This paper then derives the Cramer-Rao lower bound (CRLB) on the estimation error of the target motion parameter. We then present a matched filter (MF) based definition of the signal-to-noise ratio (SNR) to use as a basis for comparison of Monte Carlo simulation based location estimates to the calculated CRLB. It is shown that the ML estimator for the starting and ending positions of a streak in the FPA is efficient for MFSNR $geq 12$ dB. We then provide a test statistic for target detection and propose approximate distributions to set the detection threshold for specific detection ($P_D$) and false alarm probabilities ($P_{ext{FA}}$), which are then verified via simulations. This paper's major contributions are the proposal of an ML/MF method for measurement extraction of streaking targets, confirmation that this method achieves the best accuracy possible for realistic FPA sensors, i.e., it attains the CRLB, the introduction of a statistically supported definition of SNR for these measurements, and an evaluation of the target measurement detection performance. Furthermore, this paper shows that, given our MFSNR definition, the streak length and direction of motion in the FPA have a negligible effect on performance compared to the SNR where we show that with a 4-dB change, the detection performance increases dramatically.
机译：本文从光学传感器的焦平面阵列（FPA），条纹目标的测量提取涉及测量提取。我们使用假设像素被死区分隔的模型，并将条纹目标的点扩展功能（PSF）模拟为在光学传感器的集成时间期间移动的高斯PSF。我们假设目标在采样间隔内具有恒定的速度，并使用起始和结束位置参加其运动。单个像素的噪声模型具有与其区域成比例的方差，与Nontarget的数量的泊松模型一致。我们基于来自光学传感器的一组像素测量来制定估计目标运动参数向量的最大可能性（M1）方法。然后，本文在目标运动参数的估计误差上导出了克拉梅-RAO下限（CRLB）。然后，我们呈现基于信噪比（SNR）的基于匹配的滤波器（MF）定义，以用作基于蒙特卡罗模拟的基于蒙特卡罗模拟的位置估计的基础。结果表明，用于FPA中的条纹的起始和结束位置的ML估计器对于MFSNR $ GEQ 12 $ DB是有效的。然后，我们为目标检测提供了测试统计，并提出了对特定检测的检测阈值（$ p_d $）和误报概率（$ p _ { text {fa} $）设置检测阈值，然后通过仿真验证。本文的主要贡献是ML / MF方法的测量射线测量目标的方法，确认该方法实现了现实FPA传感器的最佳精度，即它达到CRLB，引入了SNR的统计支持定义对于这些测量，以及对目标测量检测性能的评估。此外，本文表明，鉴于我们的MFSNR定义，与SNR相比，FPA中的条纹长度和运动方向对性能的影响可忽略不计，我们认为具有4 dB的变化，检测性能急剧增加。

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