Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Dopple...Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.展开更多
Frequency lock loops （FLL） discriminating algorithms for direct-sequence spread-spectrum are discussed. The existing algorithms can＇t solve the problem of data bit reversal during one pre-detection integral period....Frequency lock loops （FLL） discriminating algorithms for direct-sequence spread-spectrum are discussed. The existing algorithms can＇t solve the problem of data bit reversal during one pre-detection integral period. And when the initial frequency offset is large, the frequency discriminator can＇ t work normally. To solve these problems, a new FLL discriminating algorithm is introduced. The least-squares discriminator is used in this new algorithm. As the least-squares discriminator has a short process unit period, the correspond- ing frequency discriminating range is large. And the data bit reversal just influence one process unit period, so the least-squares discriminated result will not be affected. Compared with traditional frequency discriminator, the least-squares algorithm can effectively solve the problem of data bit reversal and can endure larger initial frequency offset.展开更多
In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,r...In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals,whereas in the second group,the original noise was left untouched to simulate an extremely high-noise scenario.For each part,artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process.In the aforementioned conditions,the performance of the PCNN was evaluated and compared with five other commonly used methods of n-γdiscrimination:(1)zero crossing,(2)charge comparison,(3)vector projection,(4)falling edge percentage slope,and(5)frequency gradient analysis.The experimental results showed that the PCNN method significantly outperforms other methods with outstanding FoM-value at all noise levels.Furthermore,the fluctuations in FoM-value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zerocrossing methods under extreme noise conditions.Additionally,the changing patterns and fluctuations of the FoMvalue were evaluated under different noise conditions.Hence,based on the results,the parameter selection strategy of the PCNN was presented.In conclusion,the PCNN method is suitable for use in high-noise application scenarios for n-γdiscrimination because of its stability and remarkable discrimination performance.It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.展开更多
The immittance spectral frequencies （ISFs） is proposed as a new set of classification features and compared with the linear spectral frequencies （LSFs） applied in a frame-level wideband speech/music discrimination...The immittance spectral frequencies （ISFs） is proposed as a new set of classification features and compared with the linear spectral frequencies （LSFs） applied in a frame-level wideband speech/music discrimination system. These two sets of features can be shared by the classifier and coding module to reduce the total computational complexity, making our classification system suitable for multi-mode audio coding applications. A performance assessment and comparison of the features are made. The experiment results show that the ISFs and LSFs have similar good performance when using full covariance matrices in classification models and the ISFs perform slightly better when using diagonal matrices. Their statistical differences for speech and music signals are also revealed.展开更多
文摘Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.
文摘Frequency lock loops （FLL） discriminating algorithms for direct-sequence spread-spectrum are discussed. The existing algorithms can＇t solve the problem of data bit reversal during one pre-detection integral period. And when the initial frequency offset is large, the frequency discriminator can＇ t work normally. To solve these problems, a new FLL discriminating algorithm is introduced. The least-squares discriminator is used in this new algorithm. As the least-squares discriminator has a short process unit period, the correspond- ing frequency discriminating range is large. And the data bit reversal just influence one process unit period, so the least-squares discriminated result will not be affected. Compared with traditional frequency discriminator, the least-squares algorithm can effectively solve the problem of data bit reversal and can endure larger initial frequency offset.
基金supported by the National Natural Science Foundation of China(Nos.4210040255,U19A2086)the Sichuan Science and Technology Program(No.2021JDRC0108)。
文摘In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals,whereas in the second group,the original noise was left untouched to simulate an extremely high-noise scenario.For each part,artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process.In the aforementioned conditions,the performance of the PCNN was evaluated and compared with five other commonly used methods of n-γdiscrimination:(1)zero crossing,(2)charge comparison,(3)vector projection,(4)falling edge percentage slope,and(5)frequency gradient analysis.The experimental results showed that the PCNN method significantly outperforms other methods with outstanding FoM-value at all noise levels.Furthermore,the fluctuations in FoM-value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zerocrossing methods under extreme noise conditions.Additionally,the changing patterns and fluctuations of the FoMvalue were evaluated under different noise conditions.Hence,based on the results,the parameter selection strategy of the PCNN was presented.In conclusion,the PCNN method is suitable for use in high-noise application scenarios for n-γdiscrimination because of its stability and remarkable discrimination performance.It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.
文摘The immittance spectral frequencies （ISFs） is proposed as a new set of classification features and compared with the linear spectral frequencies （LSFs） applied in a frame-level wideband speech/music discrimination system. These two sets of features can be shared by the classifier and coding module to reduce the total computational complexity, making our classification system suitable for multi-mode audio coding applications. A performance assessment and comparison of the features are made. The experiment results show that the ISFs and LSFs have similar good performance when using full covariance matrices in classification models and the ISFs perform slightly better when using diagonal matrices. Their statistical differences for speech and music signals are also revealed.
基金supported by National Natural Science Foundation of China(61275166,91123036,61178058)National Natural Science Funds for Distinguished Young Scholar(51225504)
