Bayesian Mitigation of Sensor Position Errors to Improve Unexploded Ordnance Detection
Bayesian Mitigation of Sensor Position Errors to Improve Unexploded Ordnance Detection
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DOI：
10.1109/lgrs.2007.912088
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发表时间：
2008-01
影响因子：
4.8
通讯作者：
S. Tantum;Yongli Yu;L. Collins
中科院分区：
文献类型：
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作者：
S. Tantum;Yongli Yu;L. Collins
现象学建模加上统计信号处理已被证明可以显着提高使用电磁感应传感器数据从良性杂波中区分未爆炸弹药的能力。大多数这些耦合方法的基本前提是，一个现象学模型是适合测量的数据，从这个模型反演估计的参数，它的特点询问的目标，在随后的统计信号处理算法中使用的目标分类为未爆炸弹药或杂波。这种耦合方法的一个潜在的限制是，反演已被证明是敏感的传感器位置相关的不确定性。当测量位置不确定时，反演结果的变化更大，因此，鉴别性能下降。在这封信中，应用贝叶斯方法来估计所需的功能，从测量数据。该方法明确承认传感器位置存在不确定性，并将此知识整合到不确定的测量位置上，以找到最大似然特征估计。由于积分的高维性，采用蒙特卡罗积分，一种估计积分值的统计技术。仿真结果表明，这种贝叶斯方法在减轻传感器位置的不确定性产生的功能具有较低的变化，因此，提供了改进的歧视性能。
Phenomenological modeling coupled with statistical signal processing has been shown to significantly improve capabilities for discriminating unexploded ordnance (UXO) from benign clutter using electromagnetic induction (EMI) sensor data. The general premise underlying the majority of these coupled approaches is that a phenomenological model is fit to the measured data, and the parameters estimated from this model inversion, which characterize the interrogated target, are utilized in subsequent statistical signal processing algorithms to classify the target as either UXO or clutter. A potential limitation of this coupled approach is that the inversion has been shown to be sensitive to uncertainty associated with the sensor positions. When the measurement positions are uncertain, the inversion results are more variable, and consequently, discrimination performance degrades. In this letter, a Bayesian methodology is applied to estimate the desired features from the measured data. This method explicitly acknowledges that uncertainty in the sensor positions exists and incorporates this knowledge to find the maximum-likelihood feature estimates by integrating over the uncertain measurement positions. Due to the high dimensionality of the integration, Monte Carlo integration, a statistical technique to estimate the value of an integral, is employed. Simulation results show that this Bayesian approach in mitigating sensor position uncertainty produces features with lower variability and, therefore, provides improved discrimination performance.