KEYWORDS: Target detection, Signal to noise ratio, Signal processing, Doppler effect, Radar signal processing, Data processing, Interference (communication), Radar, Filtering (signal processing), Electronic filtering
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.
KEYWORDS: Data modeling, Target detection, Signal to noise ratio, Stochastic processes, Filtering (signal processing), Signal processing, Interference (communication), Linear filtering, Data processing, Detection and tracking algorithms
The Modified Forward Backward Linear Prediction, MFBLP, is an effective method for data dimensionality reduction and combined with eigen-vector and eigen-value techniques significant improvements in signal isolation have been shown and discussed in previous notes of this technique. In the present work, a Stochastic Gradient Descent technique is utilized to limit the dimensionality reduction of the MFBLP and the results of this technique is compared in relation to an application of the eigen-vector eigen-value technique to limit the dimensionality reduction of the MFBLP. By using a correlation metric we are able to discuss the measure of goodness of the new implementation of the MFBLP, discuss its potential, and some of its applications in this analysis. The processing approach is for active sensor systems and discussed for comparison.
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