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Bayesian Source Separation And System Data Fusion Methodology

机译：贝叶斯源分离与系统数据融合方法

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The probability of correctly selecting a target object from among many objects is a measure of how well one can discriminate. If more than one system modality for object discrimination is available, then one can fuse respective information derived from multiple systems. In this case, performance is dependent upon the accurate association of object tracks seen by one system with common object tracks seen by another system and can be viewed in terms of answering the question: "Which objects seen by one system are associated with which objects seen by another system?" Because discrimination performance is dependent upon how accurately track data from the various systems is associated, the association question has bearing on the discrimination question, i.e., the association question must be answered to facilitate answering the discrimination question. The purpose of this paper is to address the association question using the logical question formalism advocated by Richard Cox instead of the standard approach of random variables. Biases result from random and common object track errors from each system. An association matrix correlates each object track seen by one system with object tracks seen by another system. While estimation of the common bias is essential to robust track association, most current association algorithms do not jointly estimate the association matrix and the common bias. The essential problem is analogous to that of blind source separation. A combined M-on-N track association matrix and common bias inferencing algorithm using a Bayesian source separation methodology is described with a sample 2-on-2 track association problem. While the described Bayesian algorithm deals with common translation biases and currently uses only metric information in the likelihood function, the same algorithmic approach can also effectively deal with errors having the form of any common affine transformation and can be extended to exploit features and any other available track information. Although its effectiveness has some dependence on track positions and relative sizes of the random and common errors which should be further investigated, the algorithm is both statistically efficient in its optimal exploitation of the likelihood information and exhaustive in its delineation of and search over all possible association configurations.
机译：从许多对象中正确选择目标对象的可能性是衡量对象区分程度的一种度量。如果有不止一种用于物体识别的系统模式，则可以融合来自多个系统的相应信息。在这种情况下，性能取决于一个系统看到的对象轨迹与另一系统看到的公共对象轨迹的准确关联，并且可以通过回答以下问题来查看：“一个系统看到的哪些对象与看到的哪些对象相关联通过另一个系统？”因为区分性能取决于关联各种系统的跟踪数据的准确程度，所以关联问题与区分问题有关，即，必须回答关联问题以便于回答区分问题。本文的目的是使用理查德·考克斯（Richard Cox）提倡的逻辑问题形式主义代替随机变量的标准方法来解决关联问题。偏差是由每个系统的随机和常见对象跟踪错误引起的。关联矩阵将一个系统看到的每个对象轨迹与另一个系统看到的对象轨迹关联起来。虽然对公共偏差的估计对于鲁棒的轨迹关联至关重要，但是大多数当前的关联算法并未共同估计关联矩阵和公共偏差。基本问题类似于盲源分离问题。描述了结合贝叶斯源分离方法的M对N磁道关联矩阵和公共偏差推断算法，并给出了样本2对2磁道关联问题。尽管所描述的贝叶斯算法处理常见的平移偏差，并且当前仅在似然函数中使用度量信息，但是相同的算法方法也可以有效地处理具有任何常见仿射变换形式的错误，并且可以扩展为利用特征和任何其他可用的方法跟踪信息。尽管其有效性在一定程度上取决于轨道位置以及随机误差和常见误差的相对大小，这需要进一步研究，但是该算法在优化利用似然信息方面在统计上是有效的，并且在描述和搜索所有可能的关联方面都非常详尽配置。

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