An algorithm for tracking multiple targets
An algorithm for tracking multiple targets
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DOI：
10.1109/cdc.1978.268125
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发表时间：
1978
期刊：
影响因子：
--
通讯作者：
D. Reid
中科院分区：
文献类型：
--
作者：
D. Reid
提出了一种用于混沌环境下多目标跟踪的算法。该算法能够初始化跟踪，计算错误或丢失的报告，以及处理相关报告集。当接收到每个测量值时，根据以下假设计算概率：测量值来自目标文件中先前已知的目标，或来自新目标，或测量值为假。使用卡尔曼滤波从每个这样的数据关联假设中估计目标状态。当接收到更多的测量值时，使用所有可用的信息，如未知目标密度、假目标密度、检测概率和位置不确定性，递归地计算联合假设的概率。这种分支技术允许基于后续和先前数据的测量与其源进行关联。为了使假设的数量保持合理，排除不可能的假设，并将目标估计值相似的假设组合起来。为了最小化计算需求，整个目标和测量集被划分为独立求解的簇。在飞机跟踪的示例中，该算法成功地跟踪了各种条件下的目标。
An algorithm for tracking multiple targets in a cluttered environment is developed. The algorithm is capable of initiating tracks, accounting for false or missing reports, and processing sets of dependent reports. As each measurement is received, probabilities are calculated for the hypotheses that the measurement came from previously known targets in a target file, or from a new target, or that the measurement is false. Target states are estimated from each such data-association hypothesis, using a Kalman filter. As more measurements are received, the probabilities of joint hypotheses are calculated recursively using all available information such as density of unknown targets, density of false targets, probability of detection, and location uncertainty. This branching technique allows correlation of a measurement with its source based on subsequent, as well as previous, data. To keep the number of hypotheses reasonable, unlikely hypotheses are eliminated and hypotheses with similar target estimates are combined. To minimize computational requirements, the entire set of targets and measurements is divided into clusters that are solved independently. In an illustrative example of aircraft tracking, the algorithm successfully tracks targets over a wide range of conditions.