Automobile Dual Burglary-resisting System Design Based On Gsm

As the increasing of cars, Cases of automobile theft are increased gradually, automobile theft has become an urgent social problem which is concerned by public. Especially in the market which is led by small discharge capacity and economical automobile, a kind of credible, safe, usable and cheap automobile burlary-resisting system is required.An automobile burglary-resisting system based on GSM module is designed in this paper. Dual burlary-resisting is introduced. The automobile security is greatly enhanced through dual burlary-resisting. It presents the system design as a whole, then presents the hardware and software design of system, at last adjusts the sensors, presents some discussions and analysis in the possible applying fields.GSM is a kind of developed digital mobile communication system. It has charactors of strong anti-interference ability and high communications quality in covering regions. Short Message Service has many good features such as simple realization, low-cost communications, high spectrum efficiency and good confidentiality. GSM network works as a plantform for the long-range information transmission. The paper makes a detailed explaination about the network architecture of GSM and short message correspondence theory. Short message using Text mode is the main mean of the data transmission; Short messages are received and sent through the AT commands. The article lists the commonly used AT commands and their usages. The system combines the MCU and TC35i GSM module.Sensor and speed sensor are used to implement message alarm and dual theftproof of automobile.Three main parts are included in the hardware design: external circuit of TC35i, instrctural design of on-chip CAN controller P8XC592 and the design of intelligent sensor nodes. The software design of the system refers to the procedures' command of GSM module and the programming language design of the microcontroller.The paper also introduces the principles of genetic neural networks in detail. The sensors are nonlinear corrected by genetic neural networks. Experiments show that, this method of correction has high precision and strong robustness.