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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Tracking maneuvering targets with multiple sensors: does more data always mean better estimates?
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Tracking maneuvering targets with multiple sensors: does more data always mean better estimates?

机译：使用多个传感器跟踪机动目标：更多数据是否总是意味着更好的估计？

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摘要

In many multisensor systems the number and type of sensors supporting a particular target track can vary with time due to the mobility, type, and resource limitations of the individual sensors. This variability in the configuration of the sensor system poses a significant problem when tracking maneuvering targets because of the uncertainty in the target motion model. A Kalman filter is often employed to filter the position measurements for estimating the position, velocity, and acceleration of a target. When designing the Kalman filter, the process noise (acceleration) variance Q/sub k/ is selected such that the 65 to 95% probability region contains the maximum acceleration level of the target. However, when targets maneuver, the acceleration changes in a deterministic manner. Thus, the white noise assumption associated with the process noise is violated and the filter develops a bias in the state estimates during maneuvers. The problem of tracking maneuvering targets with multiple sensors is illustrated through an example involving target motion in a single coordinate in which it is shown that with two sensors one can have (under certain conditions that include perfect alignment of the sensors) worse track performance than a single sensor. The Interacting Multiple Model (IMM) algorithm is applied to the illustrative example to demonstrate a potential solution to this problem of track filter performance.
机译：在许多多传感器系统中，由于单个传感器的移动性，类型和资源限制，支持特定目标轨道的传感器的数量和类型会随时间而变化。当跟踪机动目标时，由于目标运动模型中的不确定性，传感器系统配置的这种可变性构成了一个重大问题。通常使用卡尔曼滤波器对位置测量值进行滤波，以估计目标的位置，速度和加速度。在设计卡尔曼滤波器时，选择过程噪声（加速度）方差Q / sub k /，以使65％到95％的概率区域包含目标的最大加速度。然而，当目标机动时，加速度以确定性方式改变。因此，违反了与过程噪声相关的白噪声假设，并且滤波器在操纵期间在状态估计中产生了偏差。通过一个涉及单个坐标中目标运动的示例说明了使用多个传感器跟踪机动目标的问题，该示例显示了使用两个传感器时，一个传感器（在某些条件下，包括传感器的完美对准）会比一个传感器具有较差的跟踪性能。单传感器。交互多模型（IMM）算法应用于说明性示例，以演示解决此轨道滤波器性能问题的潜在解决方案。

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