Digital beamforming (DBF) has been studied to obtain automatic beam steering towards desired signals and simultaneous elimination of multipath and jamming signals at GNSS receivers, which is made possible by spatial and temporal digital signal processing. In this paper, the limitations of conventional multipath and jamming suppression techniques, which have been proven and widely used in GPS, are investigated. Different DBF algorithms suitable for GNSS applications are investigated theoretically. New ideas for future development of DBF are presented. The implementation of digital beamforming in FPGA/ DSP for practical application environments is also discussed.
Dynamic spectrum detection has attracted increasing interest in drone or drone controller detection problems. Spectrum sensing as a promising solution allows us to provide a dynamic spectrum map within the target frequency band by estimating the occupied sub-bands in a specific period. In this paper, a robust Student’s t-distribution model is built to tackle the scenario with a small number of observed samples. Then, relying on the characteristics of the statistical model, we propose an appropriate goodness-of-fit (GoF) test statistic regarding a small number of samples. Moreover, to obtain a reliable sensing, bilateral hypotheses of the test statistic are both used to make a decision. Numerical simulations show the superiority of the proposed method compared with other schemes, including the unilateral hypothesis-based GoF testing and the conventional energy detection, in a small number of sample cases.
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