Abstract
In this paper, we proposed a new thresholding method for impulse radio ultra-wideband (IR-UWB) radar-based detection applications by taking both the false alarm and miss-detection rates into consideration. The thresholding algorithm is the key point of the detection application, and there have been numerous studies on these developments. Most of these studies were related to the occurrence of false alarms, such as the constant false alarm rate algorithm (CFAR). However, very few studies have considered miss-detection, which is another crucial issue in detection applications. To mitigate this issue, our proposed algorithm considered miss-detection as well as the false alarms occurring during thresholding. In the proposed algorithm, a threshold is determined by combining a noise signal-based threshold, in which the focus point is the false alarm, with a target signal-based threshold, in which the focus point is a miss-detection, at a designed ratio. Therefore, a threshold can be determined based on the focus point by adjusting the designed ratio. In addition, the proposed algorithm can estimate the false alarm and miss-detection rates for the determined threshold, and thus, the threshold can be objectively set. Moreover, the proposed algorithm is better in terms of understanding the target signal for a given environment. A target signal can be affected by the clutter, installation height, and the angle of the radar, which are factors that noise-oriented algorithms do not consider. As the proposed algorithm analyzed the target signal, these factors were all considered. We analyzed the false alarm and miss-detection rates for the thresholds, which were determined by different combination ratios at various distances, and we experimentally verified the validity of the proposed algorithm.
Highlights
Due to the increased interest in the Internet of Things (IoT), there has been a growing demand for smart sensors
Most of the studies on detection have been based on the constant false alarm rate algorithm (CFAR), which focuses on a false alarm
Unlike the CFAR algorithm, the algorithm proposed in this study considers both conditions in thresholding because if only one condition is considered, the threshold is only optimized for that condition and the other is sacrificed
Summary
Due to the increased interest in the Internet of Things (IoT), there has been a growing demand for smart sensors. Smart sensors can be combined with IoT devices, which enables these devices to operate automatically and to provide useful information to the users. IoT devices, such as lights and air conditioners, can be combined with smart sensors with presence detection capabilities, allowing them to be turned on or off automatically. Smart sensors with people counting abilities can be used to provide congestion information regarding public places to the users, thereby decreasing congestion and preventing accidents. Smart sensors can be mounted on vehicles to analyze the surrounding conditions and prevent collisions, and to detect criminal activity, such as intrusions or thefts within security zones
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