• Proceedings of the IEEK Conference
• /
• 2003.11c
• /
• pp.15-19
• /
• 2003

In this paper, the bit error rate (BER) performance of the fast frequency hopping/M-ary frequency shift keying system using the clipper receiver is analyzed by using the characteristic function (CF) technique in the presence of n=1 band multitone jamming and additive white Gaussian noise environment. The CFs of the clipper receiver outputs are derived as a infinite series representation using Gamma function and Marcum's Q -function. The analytical results are validated with various simulation results. Performance comparisons with linear combining receiver are shown that the BER performance of the clipper receiver is much better than that of the linear combining receiver In addition, as the clipping level approaches to infinity, it is shown that the clipper receiver simply performs a linear combining without clipping and there exists an optimum value of diversity level (the number of hops per symbol) that maximizes the worst case BER performance of the clipper receiver.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.46 no.2
• /
• pp.152-161
• /
• 2009

As follow jamming signals in a FHSS system cause malfuctioning in the code tracking circuits using early-late gates, we need a code tracking circuit that is robust against the follow jamming signals. In this paper, we propose a code tracking circuit using a linear clipper-Gaussian filter algorithm to remedy the malfunctioning due to the follow jamming signals in FHSS systems. We investigate the mechanism of the malfunctioning of the code tracking circuit and verify that the proposed linear clipper-Gaussian filter metigates the problem through mathematical analysis and computer simulations.

• Journal of the Korean earth science society
• /
• v.30 no.3
• /
• pp.294-304
• /
• 2009

A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.