Modified G-S Adaptive Null Forming Algorithm Applied to Radar
For the problem that the researches on Gram-Schmidt(G-S) adaptive null forming algorithm are still theoretical researches and there is no study on its application to LPI(Low Probability of Intercept) radar,a method of applying modified Gram-Schmidt(G-S) adaptive null forming algorithm to continuous wave(CW) radar is proposed.This algorithm can remove the target signals from the received signals and just leave the jamming signals to be orthogonalized,preventing that target signals are treated as jamming signals.Array pattern,null analysis and error analysis show that the modified G-S algorithm can form deeper null and has better stability.Then it verifies that the modified G-S algorithm can effectively improve radar's LPI performance.
ABSTRACTWe utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting.