The particle filter (PF) perform the nonlinear estimation and have received much attention from many engineering fields over the past decade. However, the standard PF is inconsistent over time due to the loss of particle diversity caused mainly by the particle depletion in resampling step and incorrect a priori knowledge of process and measurement noise. To overcome these problems, intelligent adaptive unscented particle filter (IAUPF) is proposed in this paper. The IAUPF uses an adaptive unscented Kalman filter filter to generate the proposal distribution, in which the covariance of the measurement and process of the state are online adjusted by predicted residual as adaptive factor based on a covariance matching technique. In addition, it uses the genetic operators to increase diversity of particles. Three experiment examples show that IAUPF mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF. The effectiveness of IAUPF is demonstrated through Monte Carlo simulations. The simulation results demonstrate the effectiveness of the proposed method.

中文翻译：

---

智能自适应无味粒子滤波器在目标跟踪中的应用

粒子滤波器（PF）执行非线性估计，在过去十年中受到了许多工程领域的广泛关注。然而，由于重采样步骤中的粒子消耗以及过程和测量噪声的先验知识不正确导致粒子多样性的损失，因此标准 PF 会随着时间的推移而不一致。为了克服这些问题，本文提出了智能自适应无味粒子滤波器（IAUPF）。IAUPF 使用自适应无迹卡尔曼滤波器滤波器生成建议分布，其中基于协方差匹配技术的预测残差作为自适应因子在线调整测量和状态过程的协方差。此外，它使用遗传算子来增加粒子的多样性。三个实验示例表明，与一般 PF 相比，IAUPF 减轻了粒子贫困并提供了更准确的状态估计结果。IAUPF 的有效性通过 Monte Carlo 模拟得到证明。仿真结果证明了所提出方法的有效性。