Power-Aware Improvement in Signal Detection

Improvements in signal detection characteristics for a remote - sensing instrument can be achieved at the expense of computational effort and the power associated with that effort. DSP used in remote sensing scenarios usually involves the detection of a signal and the estimation of parameters associated with that signal. Fortunately, the algorithms used for parameter estimation are the same algorithms that, through post-processing decision making, decrease the false alarm rate. This post processing allows for the reduction in the false alarm rate as seen at the end product of the instrument. The level of false alarm reduction must be balanced against the amount of additional power that is needed to produce this level. This paper will present quantitative results that demonstrate this tradeoff for a specific application. This paper focuses on the detection of transient radio frequency (RF) events (e.g., lighting) as observed from the FORTÉ satellite. However, the methodology presented for power-aware improvement in signal detection is general enough to be applied to most remote-sensing scenarios. A suite of algorithms, which vary widely in their precision of estimated parameters, is presented in the paper. Equally wide in variation is the amount of power required by each of the algorithms. Power requirements of the algorithms were obtained by actual physical measurement for a mimic of a RAD750 processor. Algorithm performance was determined via Monte Carlo testing. Using that same Monte Carlo testing post-processing, thresholds for each of the algorithms were developed for the reduction of the false alarm rate. A quantitative display of how each of the algorithms decreases the false alarm rate over the front-end analog detection is displayed versus the power required