无方向性传感器阵列的高性能低复杂度多目标跟踪滤波器
High Performance Low Complexity Multitarget Tracking Filter for a Array of Non-directional Sensors
摘要：
本文开发了一种精确，高效的过滤器（称为“TT过滤器”），用于跟踪多个目标，使用空间分布的振幅传感器网络，估计距离，但不方向。算法中包含了几项创新，提高了准确性并降低了复杂性。对于初始目标采集，一旦跟踪开始，一个受约束的海森搜索被用来找到最大似然（ML）目标向量，基于测量模型和高斯近似的先验。ML向量处的Hessian用于给出目标向量分布的负对数似然的初始近似：如果Hessian由于远近问题而不是正定的，则应用校正。通过应用与仅距离传感器引入的已知非线性相匹配的变换来进行进一步的校正。使用该信息构造一组积分点，其用于估计目标向量分布的均值和矩。结果表明，TT滤波器提供了优越的精度和较低的复杂度比以前的替代品，如卡尔曼滤波器或粒子滤波器。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
