汽车车身姿态测量一直是汽车技术的关键一环。提出了一种无陀螺实时车身姿态测量方案，利用单个三轴加速度传感器通过BP人工神经网络(ANN)映射出实时车身姿态。方案巧妙地根据汽车行驶的特性作出物理模型以及算法的精简，利用神经网络的非线性函数学习和泛化推理能力，对输入的三轴加速度值进行惯性有害分量滤除和系统误差补偿然后映射输出姿态角。最后给出了实测实验数据，表明系统满足精度和实时性要求。设计利用神经网络高度并行处理的特性以及对不确定问题的解决能力实现用最小的硬件开销取得良好的实时姿态检测效果。%Vehicle bodywork attitude measurement has been important in automotive technology. This paper presents a non-gyro bodywork attitude measurement program. Using a three-axis acceleration sensor,its output reflects realtime body posture through BP neural network. Program skillfully made physical model and the algorithm simple according to the characteristics of motor cars. Taking advantage of the nonlinear function in learning and generalization ability of ANN (Artificial Neural Net) ,the system made the input three-axis inertia] acceleration values of harmful components to be filtered and the system error to be compensated so as to mapoutput Attitude. And finally, it presents the data of experiments. By taking full advantage of ANN which is precessing in parallel, and good at dealing with problem uncertainty, this program provides a solution of calculating real-time bodywork attitude, while with minimal hardware overhead.
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