0.Abstract:

0.逐句翻译

Floor positioning is an important aspect of indoor positioning technology, which is closely related to location-based services (LBSs).
楼层定位是室内定位技术的一个重要方面，它与基于位置的服务(lbs)密切相关。

Currently, floor positioning technologies are mainly based on radio signals and barometric pressure. The former are impacted by the multipath effect, rely on infrastructure support, and are limited by different spatial structures.
目前,楼层判断技术主要是基于无线电信号和气压。前者受多路径效应影响，依赖基础设施支撑，受不同空间结构的限制。

For the latter, the air pressure changes with the temperature and humidity, the deployment cost of the reference station is high, and different terminal models need to be calibrated in advance.
对于后者，气压随温度和湿度的变化而变化，参考站部署成本较高，需要提前校准不同的终端型号。
（大约就是气压本身理论上自己就可以确定一个楼层）

In view of these issues, here, we propose a novel floor positioning method based on human activity recognition (HAR), using smartphone builtin sensor data to classify pedestrian activities.
针对这些问题，本文提出了一种基于人类活动识别(HAR)的地板定位方法，利用智能手机内置的传感器数据对行人活动进行分类。

We obtain the degree of the floor change according to the activity category of every step and determine whether the pedestrian completes floor switching through condition and threshold analysis.
我们根据每一步的活动类别得到楼层变化的程度，通过条件分析和阈值分析来判断行人是否完成楼层切换。

Then, we combine the previous floor or the high-precision initial floor with the floor change degree to calculate the pedestrians’ real-time floor position.
然后，将前一层或高精度初始层与楼层变化程度相结合，计算行人的实时楼层位置。

A multi-floor office building was chosen as the experimental site and verified through the process of alternating multiple types of activities.
一个多层的办公大楼被选为实验场地，并通过多种活动的交替过程来验证。

The results show that the pedestrian floor position change recognition and location accuracy of this method were as high as 100%, and that this method has good robustness and high universality.
结果表明，该方法对行人楼层位置变化的识别和定位精度高达100%，具有较好的鲁棒性和较高的通用性。

It is more stable than methods based on wireless signals.
它比基于无线信号的方法更稳定。

Compared with one existing HAR-based method and air pressure, the method in this paper allows pedestrians to undertake long-term static or round-trip activities during the process of going up and down the stairs.
与现有的基于har的方法和气压相比，本文的方法允许行人在上下楼梯过程中进行长期的静态或往返活动。

In addition, the proposed method has good fault tolerance for the misjudgment of pedestrian actions.
此外，该方法对行人行为的误判具有良好的容错性。

0.2总结

文章说了基于气压和无线电信号（应该是指的wifi和wub这些）的不足：

1.气压虽然是可以得到海拔高的，但是气压受温度和湿度影响大，所以需要额外感知这两个东西的传感器，是一种额外的消耗。
2.无线信号存在：受多路径效应影响，依赖基础设施支撑，受不同空间结构的限制的问题。

然后提了自己的模式：

1.这个东西是在一个楼里进行测试的
2.这个东西是通过识别行为推算出行人所处楼层变化的，也就是有一个楼层作为参考的。

1. Introduction

1.1逐句翻译

第一段（室内定位非常重要、楼层区分在室内定位当中又非常重要）

Indoor positioning technology is one of the core technologies of artificial intelligence(AI) in the future [1].
室内定位技术是未来人工智能(AI)的核心技术之一。

It has been widely used in multiple industry sectors and markets [2], including shopping centers, hospitals, nursing homes, airports, railway stations, warehouses, parking lots, and prisons/detention centers.
它已被广泛应用于多个行业和市场[2]，包括购物中心，医院，养老院，机场，火车站，仓库，停车场，监狱/拘留中心。

Today, high-rise and multi-storey buildings are widely distributed. In multi-storey indoor environments, users need floor information in the vertical dimension, alongside positions on a 2D plane.
今天，高层和多层建筑分布广泛。在多层室内环境中，用户需要在垂直维度上的楼层信息，以及在二维平面上的位置信息。
（现在都是楼，室内位置你总是得需要楼层位置的）

With the wide application of indoor location services, the demand for floor positioning information is increasing [3], especially in emergency rescue situations [4].
随着室内定位服务的广泛应用，对楼层定位信息的需求越来越大，尤其是在紧急救援情况下。

In a multi-floor indoor environment, an indoor positioning system (IPS) is sensitive to floor location [5], and floor recognition functions make indoor positioning systems more effective.
在多层室内环境中，室内定位系统(IPS)对楼层位置[5]非常敏感，而楼层识别功能使室内定位系统更加有效。

In some cases, it is not easy to obtain the floor location information, such as in complex multi-floor environments, for people with limited vision [6], or in conditions with weak indoor light or fire smoke.
在某些情况下，获取楼层位置信息并不容易，例如在复杂的多层环境中，对于视力[6]有限的人，或在室内光线较弱或有火灾烟雾的情况下。

Highly similar multi-storey parking lots have caused trouble for people trying to find their cars.
高度相似的多层停车场给人们找车带来了麻烦。

The correct floor plan map in an indoor positioning system depends on theright floor positioning, and accurate floor judgment can effectively reduce the search time in the fingerprint-based method matching stage, while improving the positioning accuracy and reducing the computational overhead [7].
室内定位系统中正确的平面图取决于正确的楼层定位，准确的楼层判断可以有效减少基于指纹的方法匹配阶段的搜索时间，同时提高定位精度，减少计算开销[7]。

In the Indoor Positioning and Indoor Navigation (IPIN) competition, the competition area is usually a multi-floor building.
在室内定位和室内导航(IPIN)比赛中，比赛区域通常是多层建筑。

In 2018, at the Microsoft indoor localization competition hosted by the international conference on Information Processing in Sensor Networks (IPSN), a 3D Track was used [8].
2018年，在传感器网络信息处理国际会议(IPSN)主办的微软室内定位竞赛中，使用了[8]3D Track。

Therefore,during the competition, the participants need to first solve the problem of floor positioning.
因此，在比赛中，参赛者需要首先解决楼层定位问题。

In general, floor positioning plays an important role in the field of indoor positioning.
总的来说，楼层定位在室内定位领域中起着重要的作用。

第二段（将无线定位存在的问题）

The common floor positioning technologies mainly include radio-based flooridentification technologies [9–19], floor determination methods based on barometric pressure [6,20–24], and floor positioning methods based on inertial sensors [25–30]. They can be used alone, or in combination [31–35], to perform floor positioning. However, the first two methods have some limitations.
译文：常用的楼层定位技术主要有基于无线电的楼层识别技术[9-19]、基于气压的楼层确定方法[6,20 - 24]和基于惯性传感器的楼层定位方法[25-30]。它们可以单独使用，也可以组合使用[31-35]来进行地板定位。然而，前两种方法有一些局限性。

The radio-based method is dependent upon widespread wireless signal infrastructure support [36].
基于无线电的方法依赖于广泛的无线信号基础设施支持[36]。

The effective positioning depends on a stable wireless network structure [37].
有效的定位依赖于稳定的无线网络结构[37]。
（这里应该说的是因为手机热点或是其他什么情况所以不稳定的状态，在实际应用的时候可以采用几次数据取当中都出现的mac地址作为标记，那些只出现一两次或是突然连续不出现的数据几乎是不能用的。）

Most floor recognition algorithms are based on the difference or sudden change in wireless signals between different floors [10,38].
大多数楼层识别算法都是基于不同楼层之间无线信号的差异或突然变化[10,38]。（因为穿越钢筋混凝土变化很大）

This type of method has high precision and good universality, but also some limitations.
这种方法精度高，通用性好，但也存在一定的局限性。

For the wireless signal method, the floor positioning accuracy is affected by the access point (AP) deployment conditions;
对于无线信号方式，AP (access point)部署条件会影响楼层定位精度;

multipath effects are likely to cause large fluctuations in the wireless signal strength (received signal strength indicator (RSSI)) [2,39–41], leading to a large error in indoor positioning [1], and floor positioning also suffers from this issue.
多径效应可能导致无线信号强度(接收信号强度指标(RSSI))波动较大[2,39 - 41]，导致室内定位[1]误差较大，楼层定位也存在此问题
（多径效应（multipath effect）：指电磁波经不同路径传播后，各分量场到达接收端时间不同，按各自相位相互叠加而造成干扰，使得原来的信号失真，或者产生错误。比如电磁波沿不同的两条路径传播，而两条路径的长度正好相差半个波长，那么两路信号到达终点时正好相互抵消了（波峰与波谷重合）

Differences in the internal spatial structures of multiple floors will also affect the accuracy of floor positioning
多层楼内部空间结构的差异也会影响楼层定位的准确性
（例如中空区域等都会影响）

第三段（介绍了基于气压的方法的不足）

Methods based on barometric pressure also have some shortcomings.
基于气压的方法也有一些缺点。

For these methods, although the floor recognition accuracy is high, the universality is inadequate.
对于这些方法，虽然地板识别精度较高，但通用性较差。
（因为气压这个东西是随着各种条件不断变化的）

Air pressure is easily affected by environmental changes, such as the indoor temperature and humidity [15,30,42].
气压易受室内温度、湿度等环境变化的影响[15、30、42]。

When pedestrians stay in a certain position for a long time, changes in the corresponding air pressure will cause errors in recognition of the floor [42].
行人长时间停留在某一位置时，相应气压的变化会引起对地板[42]的识别错误。（在同一个位置也会不断变化引起误判）

Further, different barometer terminals need to be calibrated in advance [43,44].
此外，不同的气压表端子需要提前校准[43,44]。（也就是气压器本身也存在设备异构性）

Methods based on a reference base station require additional deployment and data communication, and some smartphones lack barometers [45].
基于参考基站的方法需要额外的部署和数据通信，而且一些智能手机缺乏气压计[45]。
（如果是部署相对观测站，这就要求额外使用一些通讯协议）

The above shortcomings have affected the popularization of this method [21].
以上缺点影响了该方法[21]的推广。

In addition, it can be difficult to obtain high-accuracy flooring information due to a high similarity in radio signals and the small barometric pressure difference between adjacent floors in a multi-storey environment with staggered floor structures, a low story height, stairwells [46], or atrium structures [17,18].
此外，在楼层结构交错、楼层高度低、楼梯井[46]或中庭结构的多层环境中，由于无线电信号高度相似，相邻楼层之间的气压差很小，因此很难获得高精度的楼层信息[17,18]。

第四段（介绍文献[25][27][29]的不足）

Existing HAR methods based on acceleration sensors can adapt to multi-floor structures and perform well under certain test conditions, but they need to be improved in terms of fault tolerance and action switching.
现有基于加速度传感器的HAR方法能够适应多层结构，在一定的测试条件下也能取得良好的性能，但在容错和动作切换方面还需要进一步改进。
（大约就是传统的行为识别已经很不错了，但是就是还不大鲁棒）

There remain omissions in the stand still state or going back and forth when humans going up and down the stairs.
当人们上下楼梯时，在站立静止状态或来回走动时仍有遗漏。

The Ftrack method proposed by Ye [25] uses an acceleration sensor to calculate the time spent taking the elevator, or the number of steps taken when climbing stairs between any two floors, through information exchange when users meet and from the users’ trajectories.
e[25]提出的Ftrack方法使用一个加速度传感器，通过用户相遇时的信息交换和用户的轨迹来计算乘坐电梯的时间，或者在任何两层楼之间爬楼梯的步数。

The feature data of the current floor are deduced and stored in a database.
推导出当前楼层的特征数据并存储在数据库中。

In the positioning stage, the floor positioning can be realized according to the time taken or the number of steps.
在定位阶段，可根据所花的时间或步数来实现楼层定位。

In this method, the traversal of all floors and landmarks is needed in advance to obtain complete reference values, and to realize the omnibearing floor positioning.
该方法需要提前遍历所有楼层和地标，以获得完整的参考值，实现全方位的楼层定位。

This method cannot reflect the process of going up and down stairs, and does not consider pedestrian round trips and stays.
这种方法不能反映上下楼梯的过程，不考虑行人往返和停留。

In the literature [27], smartphone pedometers and building shape
models have been used to fix the specific position of a user through a particle filter.
在文献[27]中，智能手机计步器和建筑形状模型已经被用来通过粒子过滤器来固定用户的特定位置。
（既然是粒子滤波就要求我们必须是知道地图等信息的）

The symmetry of stairs has been used to solve the positioning of multiple floors.
楼梯的对称被用来解决多层楼的定位问题。

This method needs to be improved in terms of its real-time performance, however, because conclusions can only be drawn after a certain period of time or motion state.
但该方法的实时性需要改进，因为只有经过一定的时间或运动状态后才能得出结论。

They will become invalid when users stay still for a while when going up/down stairs, or engage in back-and-forth walking, and the choice of shoe installation is not conducive to popularization of the applications.
当用户上下楼梯时静止不动，或来回走动时，就会失效，安装鞋的选择也不利于应用的普及。

In other literature [29], it has been pointed out that some smartphones do not contain barometers, so the height of multi-storey buildings cannot be obtained from their data.
在其他文献[29]中，有人指出，一些智能手机没有气压计，因此无法从其数据中获得多层建筑的高度。

Therefore, a pedestrian activity classification algorithm has been proposed to detect the activities of going up and down stairs, and in this way, the building height can be obtained.
因此，提出了一种行人活动分类算法来检测上下楼梯的活动，从而得到建筑高度。

Then, the HAR results combined with WLAN positioning can be used to realize floor positioning through Kalman filtering.
然后，利用HAR结果结合WLAN定位，通过卡尔曼滤波实现楼层定位。

This method does not consider the static states during back-and-forth movement, and static states when going up and down stairs,and it requires the all-the-way tracking of WLAN.
该方法不考虑来回移动时的静态状态和上下楼梯时的静态状态，需要对WLAN进行全程跟踪。

1.2总结

首先表示应用场景广泛：

1.室内定位是一个关键技术
2.室内定位当中楼层区分是比较重要的。首先，有很多应用场景本身就需要准确的楼层信息；其次，很多定位比赛当中也开始考虑分楼层的情况。
3.但是wifi、uwb以及气压等的方法还是存在问题。
4.其他研究者提出的基于惯性传感器的方法依然存在适用性不好的问题。

暂时跳过第二段，因为本次需求是使用深度学习的方法而这里介绍的主要是机器学习的方法。

3. Detection Scheme for Floor Changes

After obtaining the pre-acquired high-accuracy initial floor position, the floor positioning could be implemented recursively through the floor change detection scheme proposed in this study.
在获得预先获取的高精度初始楼层位置后，通过本研究提出的楼层变化检测方案递归实现楼层定位。
（就是说这个文章是实现的是一种变化识别）

The method of floor change detection is summarized as follows.
楼层变化检测的方法总结如下:

Firstly, different AC results for each step of the pedestrians were processed differently.
首先，对行人每步不同的AC结果进行不同的处理。

Then, based on the pre-stored floor stair library (see Table 2), each step’s status data could be calculated and saved. Floor change determination was also conducted (see Figure 3).
然后，根据预先存储的楼层楼梯库(见表2)，计算并保存每一步的状态数据。还进行了楼层变化的确定(见图3)。
（可以看到他这个东西也是需要预先存储一些库的）

Finally, the current floor position could be obtained based on the previous floor position or the high-accuracy initial floor recursion.
最后，可以根据之前的楼层位置或高精度的初始楼层递归得到当前楼层位置。

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