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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Measurement Extraction of Two Targets With Unequal and Unknown Intensities in an FPA
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Measurement Extraction of Two Targets With Unequal and Unknown Intensities in an FPA

机译：在FPA中测量提取两种目标的两个目标和不平等强度

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This article extends previous work on location and intensity estimation for measurement extraction of targets in a focal plane array. Prior work has been done to extract single targets and two targets of equal intensity, whereas this work explores the case where two targets have unequal and unknown intensities. Here, we assume a Gaussian point spread function (PSF) with spread sigma(PSF), but our approach could be applied to other PSF shapes. We present a maximum likelihood (ML) method for target extraction under resolved and unresolved assumptions. In the unresolved case, we estimate the parameters of a single target that represents the centroid of the two unresolved targets. We also present the Cramer-Rao lower bound (CRLB) of the estimation variances for both cases. Our simulation results show that resolved targets have their parameter vectors estimated efficiently (i.e., the variance meets the CRLB) when the targets are separated by 0.9 sigma(PSF), or about 1.8 pixel widths. We also find that estimation of the centroid parameters is efficient below a target separation of 0.65 sigma(PSF). Furthermore, we find that increased difference in the SNR of two targets causes the variances in the resolved scenario to be lower, and in the case of the unresolved scenario, to increase. We also derive and characterize a decision about target cardinality as a hypothesis testing problem, and develop a generalized likelihood ratio test to perform the decision making. The performance of this test is evaluated via Monte Carlo simulations, and matches well to theoretical predictions. Finally, we explore the effect of separation between targets, and individual target SNR on resolvability.
机译：本文在焦平面阵列中的测量提取的位置和强度估计上扩展了先前的工作。已经完成了事先提取单个目标和两个相同强度的目标，而这项工作探讨了两个目标具有不平等和未知强度的情况。在这里，我们假设具有扩散的Sigma（PSF）的高斯点传播功能（PSF），但我们的方法可以应用于其他PSF形状。我们为解决和未解决的假设下的目标提取提供了最大可能性（ML）方法。在未解决的情况下，我们估计单个目标的参数，该目标代表两个未解决目标的质心。我们还介绍了两种情况的估计差异的克拉默 - RAO下限（CRLB）。我们的仿真结果表明，当目标分离0.9Σ（PSF），或约1.8像素宽度时，我们的仿真结果表明，所解析的目标有效地估计（即，方差符合CRLB）。我们还发现质心参数的估计有效低于0.65 sigma（PSF）的目标分离。此外，我们发现两个目标的SNR的增加差异导致解决方案中的差异降低，并且在未解决的方案的情况下增加。我们还导出并表征了关于目标基数的决定作为假设检测问题，并制定了概括的似然比测试来执行决策。通过Monte Carlo模拟评估该测试的性能，并与理论预测相匹配。最后，我们探讨了目标之间分离的效果，以及个体目标SNR对可解性的影响。

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