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本文研究了融合来自两个不同传感器的异步数据的技术,其中一个传感器以比另一个传感器高的速率提供数据。目的是:当用另一个传感器观察时,同时获得高数据率传感器数据的最小二乘法估计。先前研究的同步数据融合算法被用来融合时间一致的数据以更新目标状态估计。考虑了融合来自光学传感器和雷达数据的情况,其中,光学传感器以高速率提供周期性数据而雷达以低数据速率提供拟周期数据。通过提供类似于由际准序次数据处理方法所得的结果的仿真,证明了利用这种数据融合方法的跟踪滤波器的性能,这种标准的序次数据处理方法需要更多的有效计算。
This article explores techniques for merging asynchronous data from two different sensors, one of which provides data at a higher rate than the other. The objective is to simultaneously obtain a least squares estimate of the high data rate sensor data when viewed with another sensor. The previously studied synchronous data fusion algorithm was used to fuse time-consistent data to update target state estimates. Consider the convergence of optical sensor and radar data where the optical sensor provides periodic data at high rates and the radar provides quasi-periodic data at low data rates. The performance of the tracking filter using this data fusion method is demonstrated by providing simulations that are similar to the results obtained with the in-quasi-order data processing method, which requires more efficient computation.