Abstract
In this paper, methods for detection of frequency-hopping spread spectrum (FHSS) signals from compressive measurements are proposed. Rapid switching of the carrier frequency in a pseudorandom manner makes detection of FHSS signals challenging. Conventionally, FHSS detection is performed by scanning small segments of the spectrum in a sequential manner using a sweeping spectrum analyzer (SSA). However, SSAs have the inherent risk of missing the transmitted signal depending on factors such as the rate of hopping and scanning. In this paper, we propose compressive detection strategies that sample the full FHSS spectrum in a compressive manner. We discuss the use of random measurement kernels as well as designed measurement kernels in the proposed architecture. The measurement kernels are designed to maximize the mutual information between the FHSS signal and the compressive measurements. Using a mixture-of-Gaussian model to represent the FHSS signal, we derive a closed-form gradient of the mutual information with respect to the measurement kernel. Theoretical analysis and simulation results are provided to compare different systems. These results demonstrate that the proposed compressive system with random measurement kernels is not subject to the performance limitations suffered by SSAs when their scanning rates are low and designed adaptive measurement kernels provide enhanced detection performance compared to random ones.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
