Compensated Modeling of Taguchi Method and Genetic Algorithm Based on RSSI of DSRC Communication

The automotive industry is moving aggressively in the direction of advanced active safety. Dedicated Short Range Communication (DSRC) is a key enabling technology for the next generation of communication-based safety applications. Distance measurement based on RSSI, featuring low communication overhead and low complexity, is widely applied in the ranged-based localization of DSRC communication. However, the uncertainty factor and ranging error of shadowing model are varied under different circumstances. Furthermore, the drawback of positioning error will affect its priority in geocasting or multi-hop of DSRC signals. This paper presents an operational scheme of parameters contribution design by utilizing Taguchi method, and the experiment inference is redesigned to compensate circumstance factors for shadowing model using genetic algorithm in proving ground of ARTC. The objective of Taguchi design is to optimize the mean and minimize the variability that results from uncertainty represented by noise factors. The channels of scheduled experiments utilized 5.9 GHz for the entire packet duration, and Taguchi analysis result shows the system parameter contribution under signal to noise ratio. The genetic algorithm provides advanced solution to circumstance factor and measured uncertainty using minimal absolute error. These results may serve as benchmarks of parameters design for future DSRC channel communication.