Probabilistic Analysis on Successive Cancellation-Assisted Transmitter Identification in SFN With Randomly Distributed Co-Channel Interferers

This paper investigates the detection error of radio frequency (RF)-watermark type transmitter identification (TxID) signal in a single frequency network (SFN). Based on the Cramer-Rao bound (CRB) of TxID detection, closed-form failure probabilities for TxID detection are derived. In order to reflect the practical network condition, the interference from Poisson-distributed out-of-guard interval transmitters is accounted for TxID detection failure probability (TDFP). The proposed approach consequently examines the possible TDFP gain that can be obtained by applying successive preamble cancellation to the TxID detection. Numerical results reveal that the performance of the preamble cancellation-assisted technique is dependent on the threshold signal-to-interference noise ratio (SINR) for preamble signal detection. Nevertheless, the assistance of preamble cancellation is verified to guarantee more than 4 dB improvement of TxID detection capability under practical preamble signal configurations.

Published in the IEEE Broadcast Technology Society Section within IEEE Access.

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A Study on the Elimination of Thermal Reflections

Recently, thermal cameras have been used in various surveillance and monitoring systems. In particular, in camera-based surveillance systems, algorithms are being developed for detecting and recognizing objects from images acquired in dark environments. However, it is difficult to detect and recognize an object due to the thermal reflections generated in the image obtained from a thermal camera. For example, thermal reflection often occurs on a structure or the floor near an object, similar to shadows or mirror reflections. In this case, the object and the areas of thermal reflection overlap or are connected to each other and are difficult to separate. Thermal reflection also occurs on nearby walls, which can be detected as artifacts when an object is not associated with this phenomenon. In addition, the size and pixel value of the thermal reflection area vary greatly depending on the material of the area and the environmental temperature. In this case, the patterns and pixel values of the thermal reflection and the object are similar to each other and difficult to differentiate. These problems reduce the accuracy of object detection and recognition methods. In addition, no studies have been conducted on the elimination of thermal reflection of objects under different environmental conditions. Therefore, to address these challenges, we propose a method of detecting reflections in thermal images based on deep learning and their elimination via post-processing. Experiments using a self-collected database (Dongguk thermal image database (DTh-DB), Dongguk items and vehicles database (DI&V-DB)) and an open database showed that the performance of the proposed method is superior compared to that of other state-of-the-art approaches.

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