Signal Energy Intensity Prediction Technique Methods Study Based On Chirp Detection Data

The short-wave communication is strong at its low cost, far transmission distance, and its communication media will not be destroyed. It has been widely used at military and emergency communication.The shortwave broadcasting stations can send the signal to the other side of the world by using a little electricity, because of the reflection from ionosphere to sky-wave. As ionosphere reflection condition is influenced by seasons, day and night, longitude,latitude and sunspot activity, the communication effects always change, sometimes it may influenced the quality of communications of some signal path. At this time the option of communication frequency seems great important.After learning the short-wave communication history and understanding the domestic and foreign shortwave communication current situation, Based on the explanation about this topic's background, I have studied in arithmetic of the shortwave communication"the choice communication frequency".In my paper, I have introduced the interrelated theories,including:the introduction of ionosphere, the analysis of short-wave communication, the forecast of short-wave communication model, the principle of Chirp detection technique etc. Then I inferred algorithm of'short-term prediction signal energy intensity by algorithm of'short-term forecasts MUF'which is recognized by the industry.And I Further derive the algorithm of'linear time prediction signal energy intensity'by improving the method of'short-term prediction signal energy intensity',and validate the algorithm through a number of historical data. Finally, I program a software of forecasting signal energy intensity software using the above algorithm.The result shows, the variance by using method of short-term forecast of signal energy intensity is about 4~7 dBm. It's much more better than using long-term forecast of signal energy intensity which has almost 12dBm variance. The accuracy of the linear predictor is a litter increaser than the short-term predictor. Further more, linear prediction can find the relevant time period of stronger by perking deviation of the Ionospheric bias caused by accidental events.