数据压缩卡尔曼滤波的多传感器精确配准方法

摘要

For the systematic biases estimation of multi-sensor systems, an exact registration method is presented with data compression Kalman filter. First, the new method constructs the pseudomeasurements of systematic biases by the local measurements of each sensor, using unbiased converted measurement model. Then data compression strategy is introduced to get a synthetic measurement, and the Kalman filter is employed to get the estimates. As the converted model is unbiased, the estimations are consistent and robust even though the measurement noise and the systematic biases are large. Meanwhile, the data compression strategy can avoid the multiple iterations of the conventional method and a single filtering procedure is required only. Simulation results show that the new method can get a good performance and reduce the computation time cost efficiently.%针对多传感器的系统偏差估计问题,提出了数据压缩卡尔曼滤波的精确配准方法.首先采用无偏转换测量模型,利用各传感器局部测量值来构造系统偏差的伪测量方程,然后引入数据压缩方法得到一个合成的测量值,并使用卡尔曼滤波得到偏差估计值.转换模型的无偏特性,保证了即使在测量噪声及系统偏差较大的情形下,估计结果仍然能有较好的一致性和稳定性.同时数据压缩方法可避免传统方法的多次迭代处理,只需一次滤波过程即可完成估计.仿真实验结果表明新方法的估计结果精度良好,同时可有效降低计算耗时.