基于实时FPGA的自适应系统技术用于下一代汽车安全系统中的实时的传感器融合
Exploiting Real-Time FPGA Based Adaptive Systems Technology for Real-Time Sensor Fusion in Next Generation Automotive Safety Systems
摘要：
我们提出了一个系统的传感器使用惯性测量设备作为基础，开发了一系列的动态实时的【形】传感器融合应用的轴线校准。概念验证利用COTS FPGA平台进行传感器融合和未对准视频传感器的实时的【形】校正。我们利用定制设计的32位软处理器内核和基于C的设计与综合，实现快速、平台中立的开发。卡尔曼滤波器和传感器融合技术建立在先进的航空系统应用于汽车的结果超过典型的工业要求的传感器对准。静态和动态测试的结果表明，使用安装在传感器上（或在组装过程中）的廉价加速度计和固定到车辆的惯性测量单元（IMU）可以用于计算传感器与IMU以及车辆的未对准。在某些情况下，模型预测和测试结果超出要求一个数量级，置信度为3 σ或99%。
英文摘要：
We present a system for the boresighting of sensors using inertial measurement devices as the basis for developing a range of dynamic real-time sensor fusion applications. The proof of concept utilizes a COTS FPGA platform for sensor fusion and real-time correction of a misaligned video sensor. We exploit a custom-designed 32-bit soft processor core and C-based design & synthesis for rapid, platform-neutral development. Kalman filter and sensor fusion techniques established in advanced aviation systems are applied to automotive vehicles with results exceeding typical industry requirements for sensor alignment. Results of the static and the dynamic tests demonstrate that using inexpensive accelerometers mounted on (or during assembly of) a sensor and an Inertial Measurement Unit (IMU) fixed to a vehicle can be used to compute the misalignment of the sensor to the IMU and thus vehicle. In some cases the model predications and test results exceeded the requirements by an order of magnitude with a 3-sigma or 99% confidence.
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