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The invention concerns a method for spectrum monitoring a given frequency band, in which the spectral power density (S(f)) within the given frequency band is determined for all noise and signal components in the frequency band and, in order to detect the presence of one or more signals within the given frequency band, it is evaluated whether the spectral power density (S(f)) exceeds a threshold value (&lgr;). According to the invention, the threshold value (&lgr;) is calculated in accordance with an estimation of a distribution density (hR(S)) for the noise component of the spectral power density (S(f)) within the given frequency band and in accordance with a predefined value for the false-alarm probability (Pfa).
Since cabling is very complex and often causes reliability problems in aircrafts new approaches which base on wireless technologies are highly desired. In this paper an innovative communication system is proposed that uses the essential elements of the airframe for data transfer. The communication is based on the wireless standard for Digital Video Broadcasting (DVB) and enables high data rates, which are required for the in-flight entertainment system as an example of use.
The automatic classification of the modulation format of a detected signal is the intermediate step between signal detection and demodulation. If neither the transmitted data nor other signal parameters such as the frequency offset, phase offset and timing information are known, then automatic modulation classification (AMC) is a challenging task in radio monitoring systems. The approach of clustering algorithms is a new trend in AMC for digital modulations. A novel algorithm called `highest constellation pattern matching' is introduced to identify quadrature amplitude modulation and phase shift keying signals. The obtained simulation and measurement results outperform the existing algorithms for AMC based on clustering. Finally, it is shown that the proposed algorithm works in a real monitoring environment.
Signal detection and bandwidth estimation, also known as channel segmentation or information channel estimation, is a perpetual topic in communication systems. In the field of radio monitoring this issue is extremely challenging, since unforeseeable effects like fading occur accidentally. In addition, most radio monitoring devices normally scan a wide frequency range of several hundred MHz and have to detect a multitude of different signals, varying in signal power, bandwidth and spectral shape. Since narrowband sensing techniques cannot be directly applied, most radio monitoring devices use Nyquist wideband sensing to discover the huge frequency range. In practice, sensing is normally conducted by an FFT sweep spectrum analyzer that delivers the power spectral density (PSD) values to the radio monitoring system. The channel segmentation is the initial step of a comprehensive signal analysis in a radio monitoring system based on the PSD values. In this paper, a novel approach for channel segmentation is presented that is based on a quantization and a histogram evaluation of the measured PSD. It will be shown that only the combination of both evaluations will lead to an successful automatic channel segmentation. The performance of the proposed algorithm is shown in a real radio monitoring szenario.