0.Abstract:

0.逐句翻譯

Floor positioning is an important aspect of indoor positioning technology, which is closely related to location-based services (LBSs).
樓層定位是室內定位技術的一個重要方面，它與基于位置的服務(lbs)密切相關。

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,樓層判斷技術主要是基于無線電信號和氣壓。前者受多路徑效應影響，依賴基礎設施支撐，受不同空間結構的限制。

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.
對于后者，氣壓隨溫度和濕度的變化而變化，參考站部署成本較高，需要提前校準不同的終端型號。
（大約就是氣壓本身理論上自己就可以確定一個樓層）

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.
針對這些問題，本文提出了一種基于人類活動識別(HAR)的地板定位方法，利用智能手機內置的傳感器數據對行人活動進行分類。

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.
我們根據每一步的活動類別得到樓層變化的程度，通過條件分析和閾值分析來判斷行人是否完成樓層切換。

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，將前一層或高精度初始層與樓層變化程度相結合，計算行人的實時樓層位置。

A multi-floor office building was chosen as the experimental site and verified through the process of alternating multiple types of activities.
一個多層的辦公大樓被選為實驗場地，并通過多種活動的交替過程來驗證。

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.
結果表明，該方法對行人樓層位置變化的識別和定位精度高達100%，具有較好的魯棒性和較高的通用性。

It is more stable than methods based on wireless signals.
它比基于無線信號的方法更穩定。

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.
與現有的基于har的方法和氣壓相比，本文的方法允許行人在上下樓梯過程中進行長期的靜態或往返活動。

In addition, the proposed method has good fault tolerance for the misjudgment of pedestrian actions.
此外，該方法對行人行為的誤判具有良好的容錯性。

0.2總結

文章說了基于氣壓和無線電信號（應該是指的wifi和wub這些）的不足：

• 1.氣壓雖然是可以得到海拔高的，但是氣壓受溫度和濕度影響大，所以需要額外感知這兩個東西的傳感器，是一種額外的消耗。
• 2.無線信號存在：受多路徑效應影響，依賴基礎設施支撐，受不同空間結構的限制的問題。

然后提了自己的模式：

• 1.這個東西是在一個樓里進行測試的
• 2.這個東西是通過識別行為推算出行人所處樓層變化的，也就是有一個樓層作為參考的。

1. Introduction

1.1逐句翻譯

第一段（室內定位非常重要、樓層區分在室內定位當中又非常重要）

Indoor positioning technology is one of the core technologies of artificial intelligence(AI) in the future [1].
室內定位技術是未來人工智能(AI)的核心技術之一。

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.
它已被廣泛應用于多個行業和市場[2]，包括購物中心，醫院，養老院，機場，火車站，倉庫，停車場，監獄/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，高層和多層建筑分布廣泛。在多層室內環境中，用戶需要在垂直維度上的樓層信息，以及在二維平面上的位置信息。
（現在都是樓，室內位置你總是得需要樓層位置的）

With the wide application of indoor location services, the demand for floor positioning information is increasing [3], especially in emergency rescue situations [4].
隨著室內定位服務的廣泛應用，對樓層定位信息的需求越來越大，尤其是在緊急救援情況下。

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.
在多層室內環境中，室內定位系統(IPS)對樓層位置[5]非常敏感，而樓層識別功能使室內定位系統更加有效。

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.
在某些情況下，獲取樓層位置信息并不容易，例如在復雜的多層環境中，對于視力[6]有限的人，或在室內光線較弱或有火災煙霧的情況下。

Highly similar multi-storey parking lots have caused trouble for people trying to find their cars.
高度相似的多層停車場給人們找車帶來了麻煩。

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].
室內定位系統中正確的平面圖取決于正確的樓層定位，準確的樓層判斷可以有效減少基于指紋的方法匹配階段的搜索時間，同時提高定位精度，減少計算開銷[7]。

In the Indoor Positioning and Indoor Navigation (IPIN) competition, the competition area is usually a multi-floor building.
在室內定位和室內導航(IPIN)比賽中，比賽區域通常是多層建筑。

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，在傳感器網絡信息處理國際會議(IPSN)主辦的微軟室內定位競賽中，使用了[8]3D Track。

Therefore,during the competition, the participants need to first solve the problem of floor positioning.
因此，在比賽中，參賽者需要首先解決樓層定位問題。

In general, floor positioning plays an important role in the field of indoor positioning.
總的來說，樓層定位在室內定位領域中起著重要的作用。

第二段（將無線定位存在的問題）

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.
譯文：常用的樓層定位技術主要有基于無線電的樓層識別技術[9-19]、基于氣壓的樓層確定方法[6,20 - 24]和基于慣性傳感器的樓層定位方法[25-30]。它們可以單獨使用，也可以組合使用[31-35]來進行地板定位。然而，前兩種方法有一些局限性。

The radio-based method is dependent upon widespread wireless signal infrastructure support [36].
基于無線電的方法依賴于廣泛的無線信號基礎設施支持[36]。

The effective positioning depends on a stable wireless network structure [37].
有效的定位依賴于穩定的無線網絡結構[37]。
（這里應該說的是因為手機熱點或是其他什么情況所以不穩定的狀態，在實際應用的時候可以采用幾次數據取當中都出現的mac地址作為標記，那些只出現一兩次或是突然連續不出現的數據幾乎是不能用的。）

Most floor recognition algorithms are based on the difference or sudden change in wireless signals between different floors [10,38].
大多數樓層識別算法都是基于不同樓層之間無線信號的差異或突然變化[10,38]。（因為穿越鋼筋混凝土變化很大）

This type of method has high precision and good universality, but also some limitations.
這種方法精度高，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;
對于無線信號方式，AP (access point)部署條件會影響樓層定位精度;

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.
多徑效應可能導致無線信號強度(接收信號強度指標(RSSI))波動較大[2,39 - 41]，導致室內定位[1]誤差較大，樓層定位也存在此問題
（多徑效應（multipath effect）：指電磁波經不同路徑傳播后，各分量場到達接收端時間不同，按各自相位相互疊加而造成干擾，使得原來的信號失真，或者產生錯誤。比如電磁波沿不同的兩條路徑傳播，而兩條路徑的長度正好相差半個波長，那么兩路信號到達終點時正好相互抵消了（波峰與波谷重合）

Differences in the internal spatial structures of multiple floors will also affect the accuracy of floor positioning
多層樓內部空間結構的差異也會影響樓層定位的準確性
（例如中空區域等都會影響）

第三段（介紹了基于氣壓的方法的不足）

Methods based on barometric pressure also have some shortcomings.
基于氣壓的方法也有一些缺點。

For these methods, although the floor recognition accuracy is high, the universality is inadequate.
對于這些方法，雖然地板識別精度較高，但通用性較差。
（因為氣壓這個東西是隨著各種條件不斷變化的）

Air pressure is easily affected by environmental changes, such as the indoor temperature and humidity [15,30,42].
氣壓易受室內溫度、濕度等環境變化的影響[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].
行人長時間停留在某一位置時，相應氣壓的變化會引起對地板[42]的識別錯誤。（在同一個位置也會不斷變化引起誤判）

Further, different barometer terminals need to be calibrated in advance [43,44].
此外，不同的氣壓表端子需要提前校準[43,44]。（也就是氣壓器本身也存在設備異構性）

Methods based on a reference base station require additional deployment and data communication, and some smartphones lack barometers [45].
基于參考基站的方法需要額外的部署和數據通信，而且一些智能手機缺乏氣壓計[45]。
（如果是部署相對觀測站，這就要求額外使用一些通訊協議）

The above shortcomings have affected the popularization of this method [21].
以上缺點影響了該方法[21]的推廣。

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，在樓層結構交錯、樓層高度低、樓梯井[46]或中庭結構的多層環境中，由于無線電信號高度相似，相鄰樓層之間的氣壓差很小，因此很難獲得高精度的樓層信息[17,18]。

第四段（介紹文獻[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.
現有基于加速度傳感器的HAR方法能夠適應多層結構，在一定的測試條件下也能取得良好的性能，但在容錯和動作切換方面還需要進一步改進。
（大約就是傳統的行為識別已經很不錯了，但是就是還不大魯棒）

There remain omissions in the stand still state or going back and forth when humans going up and down the stairs.
當人們上下樓梯時，在站立靜止狀態或來回走動時仍有遺漏。

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方法使用一個加速度傳感器，通過用戶相遇時的信息交換和用戶的軌跡來計算乘坐電梯的時間，或者在任何兩層樓之間爬樓梯的步數。

The feature data of the current floor are deduced and stored in a database.
推導出當前樓層的特征數據并存儲在數據庫中。

In the positioning stage, the floor positioning can be realized according to the time taken or the number of steps.
在定位階段，可根據所花的時間或步數來實現樓層定位。

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.
該方法需要提前遍歷所有樓層和地標，以獲得完整的參考值，實現全方位的樓層定位。

This method cannot reflect the process of going up and down stairs, and does not consider pedestrian round trips and stays.
這種方法不能反映上下樓梯的過程，不考慮行人往返和停留。

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.
在文獻[27]中，智能手機計步器和建筑形狀模型已經被用來通過粒子過濾器來固定用戶的特定位置。
（既然是粒子濾波就要求我們必須是知道地圖等信息的）

The symmetry of stairs has been used to solve the positioning of multiple floors.
樓梯的對稱被用來解決多層樓的定位問題。

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.
但該方法的實時性需要改進，因為只有經過一定的時間或運動狀態后才能得出結論。

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.
當用戶上下樓梯時靜止不動，或來回走動時，就會失效，安裝鞋的選擇也不利于應用的普及。

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.
在其他文獻[29]中，有人指出，一些智能手機沒有氣壓計，因此無法從其數據中獲得多層建筑的高度。

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，提出了一種行人活動分類算法來檢測上下樓梯的活動，從而得到建筑高度。

Then, the HAR results combined with WLAN positioning can be used to realize floor positioning through Kalman filtering.
然后，利用HAR結果結合WLAN定位，通過卡爾曼濾波實現樓層定位。

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.
該方法不考慮來回移動時的靜態狀態和上下樓梯時的靜態狀態，需要對WLAN進行全程跟蹤。

1.2總結

首先表示應用場景廣泛：

• 1.室內定位是一個關鍵技術
• 2.室內定位當中樓層區分是比較重要的。首先，有很多應用場景本身就需要準確的樓層信息；其次，很多定位比賽當中也開始考慮分樓層的情況。
• 3.但是wifi、uwb以及氣壓等的方法還是存在問題。
• 4.其他研究者提出的基于慣性傳感器的方法依然存在適用性不好的問題。

暫時跳過第二段，因為本次需求是使用深度學習的方法而這里介紹的主要是機器學習的方法。

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.
在獲得預先獲取的高精度初始樓層位置后，通過本研究提出的樓層變化檢測方案遞歸實現樓層定位。
（就是說這個文章是實現的是一種變化識別）

The method of floor change detection is summarized as follows.
樓層變化檢測的方法總結如下:

Firstly, different AC results for each step of the pedestrians were processed differently.
首先，對行人每步不同的AC結果進行不同的處理。

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，根據預先存儲的樓層樓梯庫(見表2)，計算并保存每一步的狀態數據。還進行了樓層變化的確定(見圖3)。
（可以看到他這個東西也是需要預先存儲一些庫的）

Finally, the current floor position could be obtained based on the previous floor position or the high-accuracy initial floor recursion.
最后，可以根據之前的樓層位置或高精度的初始樓層遞歸得到當前樓層位置。

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