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Performance evaluation of track fusion with information matrixfilter

机译：信息矩阵滤波器对轨迹融合的性能评估

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摘要

In a multisensor environment, each sensor detects multiple targets and creates corresponding tracks. Fusion of tracks from these, possibly dissimilar, sensors yields more accurate kinematic and attribute information regarding the target. Two methodologies have been employed for such purpose, which are: measurement fusion and state vector fusion. It is well known that the measurement fusion approach is optimal but computationally inefficient and the state vector fusion algorithms are more efficient but suboptimal, in general. This is so because the state vector estimates to be fused obtained from two sensors, are not conditionally independent in general due to the common process noise from the target being tracked. It is to be noted that there are three approaches to state vector fusion, which are: weighted covariance, information matrix, and pseudomeasurement. This research is restricted solely to performance evaluation of the information matrix form of state vector fusion. Closed-form analytical solution of steady state fused covariance has been derived as a measure of performance using this approach. Note that the results are derived under the assumptions that the two sensors are synchronized and no misassociation or merged measurement is considered in the study. Results are compared with those using Monte Carlo simulation, which was used in the past to predict fusion system performance by various authors. These results provide additional insight into the mechanism of track fusion and greatly simplify evaluation of fusion performance. In addition, availability of such a solution facilitates the trade-off studies for designing fusion systems under various operating conditions
机译：在多传感器环境中，每个传感器都会检测到多个目标并创建相应的轨迹。来自这些可能不同的传感器的轨迹融合产生了关于目标的更准确的运动学和属性信息。为此已采用了两种方法，即：测量融合和状态向量融合。众所周知，通常，测量融合方法是最佳的，但是计算效率低下，并且状态矢量融合算法更有效，但是次优。之所以如此，是因为从两个传感器获得的要融合的状态向量估计值通常由于在跟踪来自目标的共同过程噪声而在条件上不独立。要注意的是，状态向量融合有三种方法，分别是：加权协方差，信息矩阵和伪测量。该研究仅限于状态向量融合的信息矩阵形式的性能评估。使用这种方法已经得出了稳态融合协方差的闭式解析解，作为性能的度量。请注意，结果是在两个传感器均已同步且在研究中未考虑任何误关联或合并测量的前提下得出的。将结果与使用蒙特卡洛模拟的结果进行比较，该方法过去曾被多位作者用来预测融合系统的性能。这些结果提供了对轨道融合机制的更多了解，并大大简化了融合性能的评估。此外，这种解决方案的可用性有助于在各种操作条件下设计融合系统的权衡研究

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