The important work of improving signal to noise ratios for improved target detection presents one way to improve the target detection process. Dimensionality analysis of the data and the removal of uninteresting data is an effective method for target detection especially since it does not correlate the existing data. The process of deciding whether an anomaly in the data is a target is also an important part of target detection and this process may be just as important as uncovering the target from buried noise through the analysis of high dimensional data sets and the interrelated frequency contents, said in a different way, the noise and clutter removal processing may not always be able to help pull the target out of the high dimensional data enough to be able to detect the target with a simple thresholding approach. In this paper, we utilize the random forest technique to try and improve the decision making process in the detection of targets buried in noise.
Vahid R. Riasati and Patrick G. Schuetterle, "Random forest estimator for enhances target detection," Proc. SPIE 10648, Automatic Target Recognition XXVIII, 106480M (Presented at SPIE Defense + Security: April 17, 2018; Published: 30 April 2018); https://doi.org/10.1117/12.2306277.
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