CAJ | 학술논문

针对动态系统目标跟踪问题，RBPF算法通过将高维状态空间分解成易于处理的线性子部分与非线性子部分，并采取不同策略进行滤波估计。为了提高RBPF的计算效率，提出将粒子群优化思想融入到RBPF滤波估计中，凭借粒子群算法卓越的全局搜索能力，对于状态空间中非线性部分，通过粒子群算法驱使所有采样粒子向高似然区域（最优适应值区域）移动；对于线性状态部分，依然利用卡尔曼滤波进行处理。通过多组实验仿真结果对比，PSO-RBPF利用较少采样粒子、耗费较少时间即能获得极佳的估计精度。
침대동태계통목표근종문제，RBPF산법통과장고유상태공간분해성역우처리적선성자부분여비선성자부분，병채취불동책략진행려파고계。위료제고RBPF적계산효솔，제출장입자군우화사상융입도RBPF려파고계중，빙차입자군산법탁월적전국수색능력，대우상태공간중비선성부분，통과입자군산법구사소유채양입자향고사연구역（최우괄응치구역）이동；대우선성상태부분，의연이용잡이만려파진행처리。통과다조실험방진결과대비，PSO-RBPF이용교소채양입자、모비교소시간즉능획득겁가적고계정도。
The Rao-Blackwellised particle filter (RBPF)algorithm usually performs better than the traditional particle filter (PF)by exploiting conditional dependencies between parts of the state to estimate.By doing so,RBPF can not only improve the estimation precision but also reduce the overall computational complexity.However,the computational burden is still too high for many real-time applications.To improve the efficiency of Rao-Blackwellized particle filter,this paper applied the par-ticle swarm optimization to drive all the particles to the regions where likelihood was high in the nonlinear region.So it only needed a few particles to participate the required computation.The experiments verify the efficiency and the precision of the proposed algorithm.