无线传感器网络中基于模糊逻辑的自适应IMM跟踪算法

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"这篇论文提出了一种新的基于模糊逻辑的自适应交互多模型(IMM)滤波器，用于无线传感器网络(WSN)中的旋转车辆跟踪。通过使用模糊逻辑推理系统(FIS)来自适应调整近似恒定速度(NCV)模型的系统噪声协方差矩阵，该方法优化了跟踪性能。同时，通过减少相互作用的模型数量，简化了现有的IMM算法，并结合三角测量方法和动态传感器分组实现了数据聚合过程中的定位。模拟结果表明，这种方案能够实现良好的跟踪性能。" 本文主要探讨的是在无线传感器网络中对旋转物体进行跟踪定位的问题。其中，交互多模型(IMM)是一种常用的追踪技术，它结合了多个不同的运动模型，以适应目标可能的不同行为模式。在传统IMM的基础上，作者引入了模糊逻辑，创建了一个自适应的IMM滤波器。模糊逻辑允许系统根据环境条件和观测数据的变化动态调整参数，提高了模型的适应性，特别是对于车辆这类可能快速改变运动状态的目标。具体来说，模糊逻辑推理系统(FIS)被用来调整与NCV模型相关的系统噪声协方差矩阵。NCV模型假设目标在一段时间内保持近似的恒定速度，但在实际应用中，车辆可能会加速、减速或转向，因此需要适应这些变化。FIS通过对噪声协方差的自适应调整，使模型能更好地匹配实际的运动情况，从而提高跟踪精度。此外，为了实现网络中的定位功能，论文采用了三角测量方法。这是一种基于信号到达时间差或信号强度来确定物体位置的常见技术。结合动态传感器分组，可以在减少通信开销的同时，优化传感器的布局和协作，从而更高效地完成数据聚合和定位任务。通过蒙特卡洛仿真，研究人员证明了这个模糊逻辑自适应IMM方案在跟踪旋转物体时表现出色。这表明，结合模糊逻辑的IMM滤波器不仅能够有效处理复杂的运动模型，而且在实际的无线传感器网络环境中具有实用性和有效性。这篇论文提出的模糊逻辑自适应IMM方法为无线传感器网络中的目标跟踪和定位提供了一种新思路，特别是在处理旋转物体这类动态目标时，其性能表现优异。这一技术有望在自动驾驶、智能交通监控等领域找到广泛应用。

Tracking of a Rotating Object in a Wireless Sensor

Network Using Fuzzy Based Adaptive IMM Filter

Amin Hassani

∗,†

, Alexander Bertrand

∗,†

, Marc Moonen

∗,†

∗ KU Leuven, Department of Electrical Engineering-ESAT, SCD-SISTA / † iMinds Future Health Department

Address: Kasteelpark Arenberg 10, B-3001 Leuven, Belgium

E-mail: amin.hassani@esat.kuleuven.be

alexander.bertrand@esat.kuleuven.be

marc.moonen@esat.kuleuven.be

Abstract—In this paper, a new fuzzy-logic based adaptive

Interactive Multiple Model (IMM) ﬁlter is presented for tracking

a vehicular rotating object in a Wireless Sensor Network (WSN).

In this method, a Fuzzy-logic Inference System (FIS) is employed

to adaptively tune the system noise covariance matrix associated

with the Nearly Constant Velocity (NCV) model. By reducing the

number of interacting models, our algorithm simpliﬁes state-of-

the-art IMM algorithms for tracking of a rotating object. Local-

ization for data aggregation process is performed by means of the

triangulation method in conjunction with dynamic grouping of

sensors. Monte Carlo simulations show that this scheme achieves

good tracking performance for both highly rotating and non-

rotating objects compared to state-of-the-art IMM algorithms.

I. INTRODUCTION

The capability to manufacture small-sized and low-cost

sensor nodes, which include sensing, data processing, and

communication components, makes it possible to set up

so-called Wireless Sensor Networks (WSN). This emerging

technology has been utilized for environmental measuring,

monitoring, surveillance, audio enhancement, and many other

applications. A WSN consists of many low cost, spatially

dispersed position sensor nodes. Each node can process infor-

mation and share data with the sensor nodes that are placed

within its communication range or only with a leader node

(cluster head). The advantages of WSNs over the traditional

sensing methods are not only extending the spatial coverage

and achieving higher resolution, but also increasing the fault

tolerance and robustness of the whole system [1].

Among the WSN applications, object tracking has received

great attention for commercial, public-safety, and military ap-

Acknowledgements : This work was carried out at the ESAT Laboratory

of KU Leuven, in the frame of KU Leuven Research Council CoE EF/05/006

Optimization in Engineering (OPTEC) and PFV/10/002 (OPTEC), Concerted

Research Action GOA-MaNet, the Belgian Programme on Interuniversity

Attraction Poles initiated by the Belgian Federal Science Policy Ofﬁce

IUAP P6/04 (DYSCO, Dynamical systems, control and optimization, 2007-

2011), and Research Project FWO nr. G.0763.12 (wireless acoustic sensor

networks for extended auditory communication’). The work of A. Bertrand

was supported by a Postdoctoral Fellowship of the Research Foundation -

Flanders (FWO). The scientic responsibility is assumed by its authors.

plications. The greatest challenge associated with this problem

is the accuracy of the state estimation module. Since the nodes

of a WSN are physically distributed, the computation and

estimation procedure in object tracking should preferably also

be distributed. For an object with nearly constant velocity,

kinematics will be linear and the tracking problem could be

performed using the standard Kalman Filter (KF). However,

when rotation is to be taken into consideration, the single

standard (linear) KF is not applicable anymore due to the

fact that in this case the dynamics are nonlinear. This issue

makes that the unknown acceleration appears as large process

noise in the object model and since the model’s noise level

cannot cover it, ﬁlter divergence may occur. A ﬁrst attempt to

resolve this difﬁculty was made by Singer [2], who proposed

an object localization model in which rotation was assumed to

be a ﬁrst-order Markov process with time correlation. Many

approaches were proposed for increasing the performance of

the ﬁlters with a single model. Some works also combined

fuzzy logic with the KF [3], but still were not able to track

a highly rotating object because of the hybrid nature of the

problem. Later, various techniques were developed, where

multiple models are used to describe the different potential

modes of object motion where the ﬁnal estimate is obtained

by a weighted sum of the estimates from the sub-ﬁlters of

the different models [4]. In this scheme, different levels of

potential object movements are performed in parallel using dis-

tinct ﬁlters. To be more realistic, rotations are typically abrupt

deviations from basically a straight-line object motion and

therefore the problem can be treated as a hybrid problem. One

of the most successful yet challenging hybrid state estimators

is an Interactive Multiple Model (IMM) approach which has

received a large amount of research attention on itself. How-

ever, this algorithm needs predeﬁned sub-models with different

dimensions or process noise levels, and may not guarantee

good performance in the case where one of the models does

not exactly match the object’s motion. An approach to resolve

this problem is estimating an unknown input simultaneously

or using the adaptive IMM (AIMM) algorithm, where the

input is estimated by a two-stage Kalman estimator and sub-

978-1-4673-2115-0/12/$31.00

 2012 IEEE

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