Tiwari Ranjeet Kumar, Bhaumik Shovan, Date Paresh, Kirubarajan Thiagalingam
Department of Electrical Engineering, Indian Institute of Technology Patna, Patna 801106, India.
Department of Mathematics, Brunel University London, Uxbridge UB83PH, UK.
Sensors (Basel). 2020 Oct 6;20(19):5689. doi: 10.3390/s20195689.
This paper focuses on developing a particle filter based solution for randomly delayed measurements with an unknown latency probability. A generalized measurement model that includes measurements randomly delayed by an arbitrary but fixed maximum number of time steps along with random packet drops is proposed. Owing to random delays and packet drops in receiving the measurements, the measurement noise sequence becomes correlated. A model for the modified noise is formulated and subsequently its probability density function (pdf) is derived. The recursion equation for the importance weights is developed using pdf of the modified measurement noise in the presence of random delays. Offline and online algorithms for identification of the unknown latency parameter using the maximum likelihood criterion are proposed. Further, this work explores the conditions that ensure the convergence of the proposed particle filter. Finally, three numerical examples, one with a non-stationary growth model and two others with target tracking, are simulated to show the effectiveness and the superiority of the proposed filter over the state-of-the-art.
本文着重于开发一种基于粒子滤波器的解决方案，用于处理具有未知延迟概率的随机延迟测量。提出了一种广义测量模型，该模型包括被任意但固定的最大时间步数随机延迟的测量以及随机数据包丢失。由于在接收测量时存在随机延迟和数据包丢失，测量噪声序列变得相关。建立了修正噪声的模型，并随后推导了其概率密度函数（pdf）。利用存在随机延迟时修正测量噪声的pdf，推导了重要性权重的递归方程。提出了使用最大似然准则识别未知延迟参数的离线和在线算法。此外，这项工作探索了确保所提出的粒子滤波器收敛的条件。最后，通过模拟三个数值例子，一个具有非平稳增长模型，另外两个具有目标跟踪，以展示所提出的滤波器相对于现有技术的有效性和优越性。
