Study On The Receiving Performance And Anti-fading Technique Of Airborne AIS

Automation identification system(AIS) is to identify the ship and realize the navigation at VHF. Adopting self-organizing time division multiple access(SO-TDMA), AIS is mainly used as the digital navigation of the sea-going vessels and to realize the automatic ship-shore communication. The so-called airborne platform is to place AIS receivers on the aircrafts such as loitering aircraft, search and rescue helicopter and airborne warning and control system(AWACS) and applied to send and receive the AIS signals from large area, therefore the shore-based base station’s monitoring ability will be enhanced greatly. In view of the complexity, the airborne platform can also be utilized to realize the continuous track on specific ships or areas thus it’s able to improve to the integrity and continuity of the maritime surveillance overall. However the airborne AIS confronts with greater challenges. Firstly the slot collision occurs extensively in the system. Secondly the multipath effect and the noise both have a severe impact on the communication. Considering the above, the airborne AIS is taken as the application background here, some research on the ship detection probability, channel estimation and noise reduction algorithm has been done in this thesis.1.After analyzing the AIS base stations’ coverage radius, a model for the probability of airborne AIS’ slot collision is established in this thesis. This thesis analyzes the coverage of airborne AIS and the results in different height are also shown. Moreover, the metho d to obtain the ship detection probability of airborne receiver in cases is presented.2.Considering the multipath propagation’s devastating effects on the airborne communication system, A frequency-selective channel model was established for the airborne AIS and a fast least mean square(LMS) algorithm was also proposed for the channel estimation in this thesis. By using the training sequence, Gaussian elimination method was adopted to obtain the accurate channel parameters. In addition, an algorithm with rapid convergence for the channel estimation was presented based on LMS. The simulation results show our method was able to achieve the accurate cha nnel estimation, moreover the lower computational complexity and rapider convergence both proved the effectiveness of the proposed method.3.A method to reduce the noise with adaptive wiener filters is proposed in this thesis. The signal noise ratio(SNR) is estimated initially. Then the training sequence is taken as the reference signal and because of the different power spectrum between the original signal and noise, the wiener filter will be capable of separating the noise. Finally bit error ratio(BER) performance will be improved. The simulation results show the BER after wiener filtering is obviously reduced.