现代PDR室内定位系统：技术综述

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"这篇文章是关于基于PDR(步行者死航推算)的室内定位系统的综合概述，主要探讨了PDR技术在室内跟踪中的应用，分析了不同领域的研究贡献，并对比了各种技术方法。文中还涉及了步态检测、惯性导航、基于步态和方向的死航推算，以及如何利用建筑地图和粒子滤波器进行定位校正。此外，还讨论了智能手机作为PDR传感器设备的可能性。" 基于PDR的室内定位技术是一种利用微型传感器和处理节点来追踪行人移动的方法，随着这些技术的不断发展，PDR系统在室内定位领域变得越来越实用。PDR系统通常结合惯性传感器（如加速度计和陀螺仪）和其他传感器（如磁力计），并运用特定领域的行走知识来跟踪用户运动。步态检测是PDR系统的基础，它涉及到识别行走周期中的脚步。通过分析加速度计和陀螺仪的数据，可以识别出行走中的脚步起始和结束点，从而计算出步长。接下来，步态特征分析用于确定行人的步态参数，如步频和步幅，这对于精确定位至关重要。惯性导航是PDR系统的核心，它利用连续的步骤信息和行走方向来估算位置。然而，由于传感器漂移，长时间的定位误差会逐渐累积，这就需要引入校正机制。其中，基于步态和方向的死航推算是常用方法，通过每一步的位移和转向角度来更新位置。为了减少误差，论文还探讨了利用建筑地图和粒子滤波器的技术。粒子滤波是一种概率数据关联方法，能有效地处理不确定性，通过与建筑地图的匹配，可以校正由PDR系统累积的定位误差。同时，将绝对位置固定（如通过Wi-Fi指纹定位或蓝牙信标）集成到PDR系统中，形成混合定位系统，进一步提高精度。此外，考虑到智能手机的广泛普及和内置的多种传感器，文章指出智能手机可以作为PDR的潜在传感设备。智能手机中的传感器组合（如加速度计、陀螺仪和磁力计）可以提供实时的运动数据，而其强大的处理器则能执行复杂的定位算法。 PDR技术在室内定位方面显示出了巨大潜力，它可以单独提供定位服务，或者与其他定位技术结合，以实现更准确的室内导航。随着技术的不断进步，PDR有望在零售、紧急响应、智能家居等众多领域发挥重要作用。

HARLE: A SURVEY OF INDOOR INERTIAL POSITIONING SYSTEMS FOR PEDESTRIANS 1283

Step Detection

Step Length

Heading

SHS

Particle Filter

Gyroscopes

INS

Accelerometers

Optical

Pressure

Electromyography (EMG)

Sensors

Position

GPS

Building Maps

Radio Fingerprints

RFID

Ultrasonic Ranging

RF Ranging

Statistical Model

Compass

(Magnetometer)

Barometer

SLAM

WiFi

1. [6]

2. [7]

3. [8]

4. [9]–[12]

5. [13]

6. [14]

7. [15]–[19]

8. [13], [16], [17], [19]–[21]

9. [16], [17], [21]

10. [16], [17], [21]

11. [22]–[25]

12. [12], [14]

13. [11], [13], [20], [26]

14. [15]–[18], [21], [27]–[31], [31]

15. [19], [21], [31], [32]

16. [9]–[12], [12], [15], [17], [19], [28], [29], [33]

17. [15], [15], [17], [19], [21], [26]–[29], [31]

18. [13], [19]

19. [32], [34]

20. [10]

21. [12], [19], [20]

22. [16]–[19]

23. [18]

24. [25], [29]

25. [30], [33]

26. [24]

Fig. 2. PDR conﬁgurations. System inputs are connected via annotated arrows to ellipses, which represent algorithms and system subunits. Arrow annotations

gi ve a numerical key into the list of literature references on the right..

location systems that improve over time as they learn

about the environment.

Both of these techniques are addressed in more detail later

in this survey. Figure 2 provides a graphical summary of all the

sensors and techniques that are surveyed herein. Sensors are

connected to system subunits (shown in ellipses) by arrows

that represent the ﬂow of data. For example, the arrows

starting at the Accelerometers label represent accelerometry

data that have been used to detect steps; to directly estimate

step lengths; and been combined with gyroscopes or with

gyroscopes and a compass to form an INS. For each ﬂow

of data, the ﬁgure gives a set of re ferences to literature that

uses that speciﬁc sensor or technique.

B. Ambulation

Many of the systems described here succeed by assuming

ambulatory motion and deriving related invar iants. Ambulation

itself can be characterised by the alternate ‘vaulting’ of the

body over a stiffened leg, with the fall being broken by the

opposing leg. At any given moment at least one foot is in

contact with the ground—there is no ﬂight phase as is found

when running. Instead the gait cycle is usually deﬁned in terms

of the phases occurring at a speciﬁc foot: the primary phases

are stance and swing. In its stance phase the foot of interest

is ﬁrmly planted on the ground, providing a pivot point over

which to vault. In the swing phase, the foot lifts from behind

the pedestrian and swings through to break the fall and enter

its stance phase.

The transition from stance to swing involves the foot

‘peeling’ from the ﬂoor, providing a ﬁnal push from the

toes. This event goes by many names, but toe-off and push-

off are the most common. The transition back to the stanc e

phase begins with the heel contacting the ﬂoor (the heel-strike

or foot-down event) before the foot ﬂattens (the foot-ﬂatten

event). The foot remains ﬂattened until the transition to the

swing begins, and the cycle restarts. The strong periodicity

in the movement coupled with the tendency of humans to

sustain a consistent pace allows for a variety of constraints to

be applied.

III. D

ETECTION OF THE GAIT CYCLE

The ﬁrst task of an SHS is the identiﬁcation of steps or

strides within the data. In fact, this is even used in many

INSs, as discussed shortly. At a minimum, these algorithms

must permit for accurate step counting, although many systems

also require accurate step segmentation. We can thus identify

two main algorithm types:

• Stance detection—algorithm s that identify periods of data

throughout which a given foot is planted on the ﬂoor.

To do this, the sensor is mounted to the foot. Typically

these are appropriate for step counting but give poor

segmentation output;

• Step cycle detection—algorithms that detect cycles in the

sensor data caused by the repetitive motion of walking.

This may involve searching for repeating data patterns or

for repeating events (e.g. the heel-strike). These are well

suited to step segmentation.

Typical stance detection algorithms are threshold-based.

The principle is that the sensor will be static during the stance

phase and the inertial sensors should report a corresponding

lack of activity that thresholding can easily identify. Most

algorithms threshold on the accelerometer magnitude [11],

[17], [35], although angular velocity thresholds have also been

used [21], [32], [36] and combinations have been trialed [27].

Even magnetometer thresholding can give usable stance detec-

tion under some circumstances [19]. In some cases applying

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