粒子滤波实时定位

A RSS Based Indoor Tracking Algorithm

Using Particle Filters

Yueming Song, Hongyi Yu

Zhengzhou Information Science and Technology Institute

P.O.BOX 1001, NO.828, Zhengzhou, Henan, P. R. China

yueming1979@yahoo.com.cn, yuhongyi@vip.sina.com

Abstract

In recent years, the location finger printing techniques

draws more attention for the indoor location systems because

of the easiness for deployment. The Kalman filter is also

applied for indoor tracking system using the location

information estimated by location finger printing technique,

while the performance would be weak in some more complex

indoor environment. In this paper, we develop a new indoor

tracking algorithm using received signal strength directly and

particle filter is applied for the nonlinear tracking model. The

numerical simulation shows the new algorithm outperforms

the tracking algorithm using Kalman filter in the former

research.

Key words: RSS, Fingerprint, NNSS-AVG, Indoor

Tracking, Particle Filter

1. Introduction

Position location systems are becoming increasingly

important as add-ons to wireless technology [1]. Location-

aware services are based on the positioning techniques. They

are also necessary for emergency services such as E-911 for

cellular systems. For the outdoor environment, the global

positioning system (GPS) has been very popular for many

purposes. But in the indoor environment the GPS doesn’t work

well [2].

There have been many dedicated indoor location system [3,

4, 5, 6] which can achieve an extremely high positioning

accuracy. However, they are highly environment dependent,

and the cost of deploying a dedicated system covering a large

area is not trivial. In the last couple of years, location

fingerprinting techniques using existing wireless local area

network (WLAN) infrastructure have been suggested for

indoor areas [7]. Such schemes are based on the received

signal strength (RSS) or “fingerprinting” technique, which is

relatively simple to deploy and there is no specialized

hardware required at the mobile station (MS). Any existing

wireless LAN infrastructure can be reused for this positioning

system.

There are several relative works on the WLAN-based

location technique, such as [7]-[9], which mainly focus on

location of stationary user. In fact, tracking mobile user is also

very important in many applications. In the RADAR system

[7], the problem of tracking a mobile user reduced to a sequence of location determination problem for a (nearly

stationary) user, using a sliding window to smooth the signal

strength on a continuous basis. Obviously this is a coarse

solution for the problem. Other works [10][11] tries to

enhance the tracking performance using Kalman filter or its

variant, with the assumption that the error of location estimate

is Gaussian. However, the distribution of the estimate error is

hard to be determined and the tracking performance will be

worse with bad estimation.

In this paper we present a new nonlinear state-space model

in which the filter algorithm is directly applied on the RSS

measurements. In many nonlinear/non-Gaussian systems the

traditional Kalman filter and other variants perform weak

because their assumption are not satisfied very well, and the

Particle filter will be an approximate technique with Monte

Carlo (MC) method. Particle filters are sequential MC

methods based on point mass (or "particle") representations of

probability densities, which can be applied to any state-space

model and which generalize the traditional Kalman filtering

methods. As the number of samples becomes very large,

particle filter approaches the optimal Bayesian estimate. The

first "working" particle filter is described in [12]. An

overview of several problems, theoretical results, and

applications in the field of particle filters can be found in [13].

The rest of the paper is organized as follows. In the next

section, the system model of location fingerprint technique is

addressed. In Section III, the tracking model for mobile user is

set up and an SIR particle filter is applied for it. Section IV

gives the simulation results, and finally, some conclusions are

offered in Section V.

2. Description of System Model

A. Location Fingerprint Technique

Consider an indoor positioning system overlaid on an

indoor wireless local area network on a single floor inside a

building. We assume there are N-IEEE 802.11b access points

in the area and they are all visible for the mobile station (MS)

in the building. Then the RSS vector which consist the samples

of the RSS measured at the MS from N access points in the

area can be achieved, which is denoted as