论文标题
高性能的低复杂性多白素跟踪过滤器，用于一系列非方向传感器
High Performance Low Complexity Multitarget Tracking Filter for a Array of Non-directional Sensors
论文作者
论文摘要
本文使用空间分布的振幅传感器网络来估算距离但不是方向的振幅传感器网络，开发了一个准确，有效的滤波器（称为“ TT滤波器”），用于跟踪多个目标。算法中包括一些创新，以提高准确性并降低复杂性。对于最初的目标采集开始后，基于测量模型和先前的高斯近似值，使用约束的Hessian搜索来找到最大可能性（ML）目标向量。 ML载体处的Hessian用于给出目标向量分布的负log可能性的初始近似值：如果由于近乎far的问题，Hessian不是正定的，则应用校正。通过应用与匹配距离距离传感器引入的已知非线性的转换来进行进一步的校正。使用此信息构建了一组集成点，该信息用于估计目标向量分布的平均值和力矩。结果表明，TT滤波器比以前的替代方案（例如基于Kalman的或粒子过滤器）具有更高的精度和更高的复杂性。
This paper develops an accurate, efficient filter (called the `TT filter') for tracking multiple targets using a spatially-distributed network of amplitude sensors that estimate distance but not direction. Several innovations are included in the algorithm that increase accuracy and reduce complexity. For initial target acquisition once tracking begins, a constrained Hessian search is used to find the maximum likelihood (ML) target vector, based on the measurement model and a Gaussian approximation of the prior. The Hessian at the ML vector is used to give an initial approximation of the negative log likelihood for the target vector distribution: corrections are applied if the Hessian is not positive definite due to the near-far problem. Further corrections are made by applying a transformation that matches the known nonlinearity introduced by distance-only sensors. A set of integration points is constructed using this information, which are used to estimate the mean and moments of the target vector distribution. Results show that the TT filter gives superior accuracy and lower complexity than previous alternatives such as Kalman-based or particle filters.
