CAJ | 학술논문

为解决存在数据关联不确定、检测不确定和杂波情况下的多目标跟踪问题，提出了一种新的多目标贝叶斯滤波器。代替维持多目标状态的联合后验密度，所提出的贝叶斯滤波器联合传递各个目标状态的边缘分布和它们的存在概率。为了处理目标运动和传感器测量模型中的非线性，利用无迹变换技术提出了一种非线性高斯条件下边缘分布贝叶斯滤波器的近似实现算法。仿真实验结果表明，与 PHD（Probability Hypothesis Density）滤波器相比，所提出的滤波器具有更好的多目标跟踪能力。
위해결존재수거관련불학정、검측불학정화잡파정황하적다목표근종문제，제출료일충신적다목표패협사려파기。대체유지다목표상태적연합후험밀도，소제출적패협사려파기연합전체각개목표상태적변연분포화타문적존재개솔。위료처리목표운동화전감기측량모형중적비선성，이용무적변환기술제출료일충비선성고사조건하변연분포패협사려파기적근사실현산법。방진실험결과표명，여 PHD（Probability Hypothesis Density）려파기상비，소제출적려파기구유경호적다목표근종능력。
To resolve the problem for multi-target tracking in the presence of association uncertainty,detection uncertainty and clutter,we derive and present a novel multi-target Bayesian filter.Instead of maintaining the joint posterior density of the multi-target state,the proposed Bayesian filter jointly propagates the marginal distribution for each target and their existence probabilities. We also develop an approximation implementation algorithm of the marginal distribution Bayesian (MDB)filter for a nonlinear Gaussian system where the unscented transform technique is employed to deal with the nonlinearities of target dynamic and measure-ment models.The simulation results demonstrate that the proposed filter achieves better tracking performance of multiple targets than the probability hypothesis density (PHD)filter.