State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of Big Data era, the disturbances of complicated systems are physically multisource, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative, and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multisource heterogenous disturbances are usually simplified as a lumped one so that the “single” disturbance can be either rejected or attenuated. Since the pioneering work in 2012 (Guo and Cao, 2012), a novel state estimation methodology called composite disturbance filtering (CDF) has been proposed, which deals with the multisource, heterogenous, and isomeric disturbances based on their specific characteristics. With CDF, enhanced antidisturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this article, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g., alignment, localization, and navigation), and future research directions. In summary, it is expected that CDF offers an effective tool for state estimation, especially in the presence of multisource heterogeneous disturbances.

中文翻译：

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复合干扰滤波：一种针对多源、异构和异构干扰系统的新型状态估计方案

状态估计长期以来一直是信号处理和控制领域的一个基本问题。主要挑战是设计能够抑制或衰减各种干扰的滤波器。随着大数据时代的到来，复杂系统的扰动在物理上是多源的，在数学上是异质的，通过异构（加法、乘法和隐性）通道影响系统动力学，并且相互之间深度耦合。在传统的滤波方案中，多源异质扰动通常被简化为集总扰动，以便可以拒绝或衰减“单一”扰动。自 2012 年的开创性工作以来（Guo 和 Cao，2012），一种新颖的状态估计方法被称为复合扰动滤波（CDF）被提出，根据多源、异质、同质扰动的具体特点对其进行处理。利用CDF，可以通过精细量化、有效分离以及同时抑制和衰减干扰来实现增强的抗干扰能力。本文概述了CDF方案，包括基本原理、总体设计流程、应用场景（例如对准、定位和导航）以及未来的研究方向。总之，CDF 有望提供一种有效的状态估计工具，特别是在存在多源异质扰动的情况下。