ABSTRACT

This paper presents modification of standard Kalman filter (KF) based on augmented input estimation (AIE) and deadbeat dissipative FIR filtering (DDFF) for maneuvering target tracking. Although KF is a well-known tool in estimation and tracking but it has two weaknesses, disability in maneuvering motions and lack of robustness against temporary model uncertainty. For the first problem, the AIE is proposed to cover both the non-maneuvering and maneuvering parts of motion and so the maneuver detection procedure is eliminated. This model with an input estimation (IE) approach is a special augmentation in the state space model which considers both the state vector and the unknown input vector as a new augmented state vector. Also, KF is based on infinite impulse response (IIR) structure and cannot be robust against temporary model uncertainty. Unlike IIR, finite impulse response (FIR) filter is robust and stable against model uncertainty. So, for the second problem, an extra type of FIR filter named DDFF is introduced in this paper that not only has the intrinsic properties of FIR but also can improve these features by tuning some weighting parameters. In the last examples of the paper, the advantages of the proposed model against other models are shown.

摘要

本文提出了基于增强输入估计 (AIE) 和无差拍耗散 FIR 滤波 (DDFF) 的标准卡尔曼滤波器 (KF) 的修改，用于机动目标跟踪。虽然 KF 是一个众所周知的估计和跟踪工具，但它有两个弱点，机动运动障碍和对临时模型不确定性缺乏鲁棒性。对于第一个问题，AIE 被提议覆盖运动的非机动和机动部分，因此取消了机动检测程序。这种具有输入估计（IE）方法的模型是状态空间模型中的一种特殊增强，它将状态向量和未知输入向量都视为新的增强状态向量。此外，KF 基于无限脉冲响应 (IIR) 结构，不能对临时模型不确定性具有鲁棒性。与 IIR 不同，有限脉冲响应 (FIR) 滤波器对模型不确定性具有鲁棒性和稳定性。因此，针对第二个问题，本文引入了一种额外类型的 FIR 滤波器，称为 DDFF，它不仅具有 FIR 的固有特性，而且可以通过调整一些加权参数来改善这些特性。在本文的最后一个例子中，展示了所提出的模型相对于其他模型的优势。