. This contribution addresses the problem of automated tuning of change detectors with given false alarm rate. By estimating a parametric distribution to the test statistics computed from real or simulated data, the threshold of the test can be computed directly. The advantage is that we can

Performance analysis of order statistic constant false alarm rate (CFAR) detectors in generalized Rayleigh environment

Epetein [2] has suggested that Bayes ’ theorem may have applications in forecast verification. I would like to suggest one such use, namely in estimating false alarm rates for forecasts of rare but hazardous events such as

A constant false alarm rate (CFAR) algorithm has been developed for use in multi-band mine detection. While it is often difficult to predict the spectral signatures of targets, the shape of the target may be known. This test exploits geometric target features and spectral differences between

Abstract — This paper introduces the concept of Constant False Alarm Rate (CFAR) in fire detection for multispectral satellite data. A new algorithm is proposed, based on a technique successfully applied for detection of extended objects in High Resolution SAR images. It compares the pixel under

The main drawback of previous Chinese character error detection systems is the high false alarm rate. To solve this problem, we propose a system that combines a statistic method and template matching to detect Chinese character errors. Error types include pronunciationrelated errors and form

high rate of either false or clinically not relevant alarms. It was found that the high rate of false alarms has a negative impact on both patients and staff. In this study we apply data mining methods to reduce the false alarm rate of monitoring systems. We follow a multi-parameter approach where

. The latter is applied during every detec-tion process by applying the difference of the two thresholds from the recent to the current threshold. These two threshold adjustment methods can reduce the false alarm rate. 1

Learning objectives As you work through this session you will: Become familiar with the multi-faceted nature of forecast quality Learn the difference between forecast “quality ” and forecast “value”, in addition to other terms in forecast verification Understand common biases in estimates of forecast quality Become familiar with aspects of communicating forecast confidence: What do (or should) forecast probabilities convey?

-plex process, such as Spherically Invariant Random Process (SIRP). We derive, for appropriate covariance matrix estimates, the analytical expression of the relationship between the Proba-bility of False Alarm (PFA) and the detection threshold. We show that this detector is SIRP-CFAR: the GLR distribution does