
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

69927
10.1038/s41598-024-69927-x
Article
3D indoor area recognition for personnel security using integrated UWB and barometer approach
Yang Fan 1
Liu Delong 2
Gong Xiaodong gongxiaodong@whu.edu.cn

2
Chen Ruizhi 2
Hyyppä Juha 3
1 grid.454193.e 0000 0004 1789 3597 Shaoguan Power Supply Bureau, Guangdong Power Grid Company, Shaoguan, 512000 China
2 https://ror.org/033vjfk17 grid.49470.3e 0000 0001 2331 6153 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430072 China
3 https://ror.org/01zv3gf04 grid.434062.7 0000 0001 0791 6570 Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, 02150 Espoo, Finland
6 9 2024
6 9 2024
2024
14 208462 5 2024
10 8 2024
© The Author(s) 2024
2024
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Real-time location tracking is essential for personal security in industrial scenes, e.g. monitoring worker safety in power substations. Ultra wideband (UWB) technology is suitable for indoor positioning thanks to its high penetration capability, high ranging accuracy and low power consumption. However, UWB based trilateration positioning requires high workload for field deployment of base stations. Indoor complex topology results in multipath and non-line of sight (NLOS) conditions of UWB signals, and degrades the positioning performance in terms of accuracy and reliability. This paper proposes a three-dimensional (3D) area recognition solution by integrating UWB time of flight (TOF) ranging and barometer measurements. The proposed solution utilizes a multi-tier distributed joint probabilistic inference model, which accomplishes the indoor 3D area recognition exploiting multiple clustering and prediction algorithms of machine learning. The field experiments showed that the proposed method can achieve an accuracy of 3D area recognition of more than 99.2%. The proposed method improves the computing efficiency by 93%. The errors of improved differential barometric height estimation method are less than 1 m, which means a success rate of 100% for floor identification, given a floor separation of 3–4 m. The proposed solution is suitable for personnel security applications of industrial scenes, which requires reliable real-time area information rather than just coordinates.

Subject terms

Information technology
Engineering
Electrical and electronic engineering
China Southern Power Grid co. Limited Science and Technology ProgramGDKJXM20220188 Yang Fan Postdoctoral Fellowship Program of CPSFGZB20230538 Gong Xiaodong Natural Science Foundation of Hubei Province of China2023AFB081 2024AFD403 Gong Xiaodong LIESMARS Special Research Fundingissue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Substations are one of the most critical facilities in power internet of things (IoT) services and applications. Routine inspection of such facilities is necessary to prevent serious accidents such as fire, explosion and power outage of the power grid. Real-time location tracking is essential for the security of the inspection personnel in these industrial scenes1. In general context, location tracking is required in a variety of location based services (LBS) applications. 3D area recognition is one of location tracking means. Compared to fine positioning that resolve a coordinate of user end, area recognition is more straightforward for location tracking based security monitoring applications, such as electronic fence.

Location tracking can be achieved using different technologies for outdoor and indoor environments. Global navigation satellite systems (GNSS) can offer high-precision location information for outdoor users. However, in the complex indoor environment, the satellite signal will be severely attenuated or even completely blocked by the wall and other building structures. Therefore, GNSS cannot provide stable and reliable location information indoors2–4. Many indoor positioning techniques have been developed for indoor location tracking with different levels of accuracy, such as Wi-Fi5, bluetooth low energy (BLE)6, acoustic sources7, light sources8, 5G9,10, ultra wideband (UWB)11,12 and integrated sensors13–15. Among these techniques, UWB technology is preferable for high-precision indoor location tracking due to its advantages of strong anti-interference, strong penetration ability, high ranging accuracy and low power consumption. However, when UWB is used for fine positioning indoors, complex indoor topology causes multipath and non-line of sight (NLOS) conditions, and degrades the performance of UWB based trilateration positioning. Reliable and real-time indoor location tracking still have remaining challenging in complex indoor environments.

Rather than fine positioning, this study proposes a three-dimension (3D) indoor area recognition solution by integrating UWB and barometer measurements. Different from fine trilateration positioning approach, the proposed area recognition solution utilizes the fingerprinting approach. Hence, in contrast to the fine positioning method, the area recognition approach streamlines the field deployment of UWB base stations and diminishes on-site workload by eliminating the need for precise geometric placement and calibration of base station coordinates. In order to improve the accuracy of 3D area recognition in intricate indoor environments with non-line-of-sight (NLOS) conditions, this research introduces a multi-tier distributed joint probabilistic model (MTD-JPM). This model predicts 3D indoor area information by utilizing the inner boundary buffer fingerprinting (IBBF) technique, which is created through integrated UWB and barometer measurements. IBBF is a novel fingerprinting model designed to gather UWB ranging data within the inner boundary buffer of each positioning area, thereby reducing the time required for data surveying. Compared with traditional fingerprinting positioning methods that divide a space of interest into a grid of reference points, the proposed MTD-JPM solution employs a dynamic surveying method that collects the fingerprinting reference data in the on-the-move way. Consequently, data collection exhibits significantly improved efficiency and reduces on-site workload. The main contributions of our study are summarized as follows:First, this paper proposes an efficient 3D area recognition solution using the integrated UWB and barometer measurements. Different from traditional positioning approach, the proposed solution employs the fingerprinting approach based on a newly defined concept of inner boundary buffer. By such, the proposed solution is advantageous, for example, it has no requirement in the layout of UWB base stations and it has adequate area recognition performance even in NLOS environments.

Second, in the proposed solution, the inner boundary buffer fingerprinting reference samples are collected using the dynamic on-the-move surveying, and a multi-tier distributed IBBF radio map generation method is developed to produce the area inner boundary buffer fingerprinting attributes by extracting the UWB and barometer sub-area related features. The multi-tier distributed IBBF effectively improves the precision and computing efficiency of area recognition, even in the NLOS environments.

Finally, a multi-tier distributed joint probabilistic model is developed for 3D area recognition using the IBBF radio map and integrated UWB and barometer measurements. A quality controlling and assurance mechanism is designed to improve the performance of 3D area recognition, especially when there are outlier measurements. The proposed area recognition is featured with high recognition precision, computational efficiency, and low on-site deployment workload.

The rest of this paper is structured as follows: Sect “Related work” presents the related work. Sect “Methods” presents the mathematical method of the proposed MTD-JPM algorithm. Sect “Experiments and discussions” describes the experiments and results. It is concluded with Sect “Conclusions”.

Related work

UWB and other radio signals have been applied for indoor location tracking based on the positioning approach, which estimates the coordinates of user tags. The conventional algorithms for indoor location tracking include machine learning and deep learning. Machine learning has been widely adopted for fingerprint-based indoor localization because of its potency in delineating relationships between received signal strength indicator (RSSI) information and labels accurately16–18. A deep learning-based localization system uses the raw distance information for model training and testing and the model predicts the user’s current positions19,20. The conventional positioning approaches includes distance based triangulation method and RSSI based fingerprint method. The most state-of-the-art works of UWB indoor positioning are developed with the triangulation based positioning approach, which estimates a coordinate of user tag using ranging measurements and the pre-calibrated coordinates of a number of UWB base stations. The triangulation positioning performance is vulnerable to NLOS and multipath signals caused by wall blockage. Usually, an adequate number of anchors are needed to ensure the positioning accuracy. Hence, this approach requires a lot of field workload for field deployment of the adequate base stations and coordinate calibration of the base stations. Fingerprinting based location recognition is another low-cost approach for recognizing the area range where a user belongs to. This approach does not require the coordinate calibration of base stations, and is robust even when there are biased measurements, e.g. NLOS signals. Normally, the fingerprinting-based area recognition system contains two main realizing approaches to improve the efficient and accuracy of area recognition in LOS and NLOS environments: one is to optimize the structure of offline surveying fingerprinting data, and the other is to enhance the use of environment context data.

Many offline survey methods have been developed over decades of extensive research to reduce labor costs. Among them, the walking-surveying approach has gained much attention. The work in21 proposed a fingerprinting integrity assessment algorithm to detect the changes in access points and a periodic adaptive estimate algorithm to determine the update period for each reference point. The active fingerprinting collection mode was designed to update the radio map efficiently. The work in22 proposed a dynamic on-the-move surveying method of radio map to enhance the surveying efficiency using a self-developed mobile geo-reference system, and radio map is constructed using much sparser measurements and sparse principle. The fundamental concept of walking-surveyed recognition involves moving along a designated route while continuously gathering fingerprinting data, as opposed to remaining stationary at specific grid points. The walking-surveyed method accelerates offline surveying in comparison to traditional static manual surveying, while also guaranteeing complete boundary buffer coverage of the target area for area recognition.

Many effective area recognition methods have been reported to enhance the use of environmental context data. The work in23 proposed a multi-feature floor localization algorithm that uses random forest classifiers to achieve accurate classification results by combining the features that are invariant to the types or sites of stairs for the classification of plane walking, going upstairs, and going downstairs moments. The work in24 proposed an enhanced indoor positioning solution using spatial context knowledge that is extracted from the sparse dynamic fingerprints to reduce the computational time and storage space complexity. Moreover, the work in25 proposed an access-point (AP)-centered window radio map generation network that extracts parts of a radio map in the form of a window based on AP floor plan coordinates to reduce the training time and improve the radio map prediction accuracy. This method is more suitable for static radio map surveying than for IBBF based radio maps, because the model requires many signal measurements collected at each point as training data.

Unlike existing algorithms that use offline surveying fingerprinting and environmental context data for 3D area recognition, our proposed MTD-JPM employs a machine learning framework that can handle walking-surveyed heterogeneous data. The proposed method does not estimate a coordinate of user tag, rather recognize the pre-defined area where a user tag belongs to. This framework enables 3D area recognition based on area fingerprinting information only, without requiring additional context data. Moreover, we apply MTD-JPM to the widely used back-propagation (BP) neural network algorithm to enhance the accuracy of 3D area recognition using the IBBF radio map and integrated UWB and barometer measurements. As a result, the proposed fingerprinting approach requires fewer field workload of deployment and calibration, and the performance is robust even when there are NLOS signals in practical scenarios of industry applications.

Methods

The UWB and barometer integrated 3D indoor area recognition frame is depicted in Fig. 1. In the offline phase, the IBBF data is measured between the UWB base station and the tag using double-sided two-way ranging (DS-TWR)26. A quality control mechanism for walking-surveyed IBBF data and a multi-tier distributed IBBF database generation method is proposed for processing IBBF data, resulting in the creation of a regional recognition fingerprinting database. In the online stage, the MTD-JPM based recognition algorithm is used to perform 3D area recognition.Figure 1 Framework of proposed 3D area recognition solution using integrated UWB and barometer measurements.

Walking surveying and quality control of IBBF data

This paper introduces the walking surveying IBBF method for 3D area recognition to tackle the issues of low efficiency and high workload associated with fingerprint collection27 in the conventional fingerprinting positioning method. The typical approach for fingerprint data collection involves partitioning a single room into grid points of the same size and conducting multiple extended static collections of fingerprint data at each grid center point, as illustrated in Fig. 2. If the positioning region is large, such as multi-story buildings and multiple rooms, the fingerprinting collection workload of commonly used fingerprinting positioning will be very high. However, the inner boundary buffer of each area is established from the polygon area with a certain width for IBBF collection, as shown in Fig. 3. Note that the buffer setup is to address the impact of UWB ranging noise, which is related with the environments, including building structure layout and radio interference, and others. The selection of buffer width is based on the noise level. The ranging information from each UWB base station is obtained by walking surveyed using the time of flight (TOF) method within the sampling inner boundary buffer. The UWB TOF ranging data is stored in the IBBF database along with the corresponding area label. This will significantly reduce the fingerprinting workload and enhance the coverage of area information. However, the process of walking surveyed IBBF collection involves the following three problems: (1) zero value detection. Sometimes, only some base station data can be occasionally received, which means that the ranging value of a certain base station is zero. (2) Observational gross error identification. The ranging data may contain gross errors because of the multipath effect and non-line-of-sight in the complex indoor environment. (3) Noise smoothing. Due to the human movement and tag holding during data collection, there will be some noise interference. Without controlling the data, the quality of the established fingerprinting database will be compromised, which will affect the accuracy of the final area positioning. To address these problems, this paper proposes a set of quality control mechanisms for IBBF to improve the quality of the fingerprinting database.Figure 2 Deployment of commonly used fingerprinting reference points.

Figure 3 Deployment of proposed IBBF surveying point.

This paper proposed the zero-value quality control mechanism (ZQCM) for the original IBBF data by interpolating the missing ranging data based Hermite interpolation. Suppose that X=x0,x1,...,xi,...,xN are the index of IBBF samples, D=d0,d1,...,di,...,dN are the corresponding collected TOF ranging data of IBBF, the TOF measurement model can be expressed as follows:1 di=fxi,i=1,2,⋯,N

where f of xi represents a continuous function of di. N is size value of the sampling set. The mean difference of the n-th nodes in the Hermite interpolation polynomial28 is:2 α(n)(x0)=limxn→x0fx0,x1,...,xn=1n!f(n)(x0)

where f(n)(x0) represents the n-th derivative at x0. Then the n-th order Hermite interpolated polynomial is:3 Pn(x)=f(x0)+α(1)(x0)(x-x0)+⋯+α(n)(x0)(x-x0)n

where Pn(x) represents the n-th order Hermite interpolation for the missing value of the original IBBF.

Following the ZQCM processing, the zero value in the original IBBF are replaced using Hermite interpolation. However, the original IBBF data still contain the observational gross error. This paper proposes a gross error quality control mechanism (GQCM) to identify and eliminate such error. Typically, observational gross error data exhibit deviations exceeding three times the median absolute deviation (MAD)29 within the sample set. It can be expressed as follows:4 MAD=median(|di-median(D)|)

where i=1,2,...,N, N is size value of the sampling set. median() represents the median of the value. If TOF ranging value di is an observational gross error, it satisfies the following model:5 |di-median(D)|>3MAD

TOF ranging values that meet the condition of the above equation are considered gross errors and will be excluded. Even after processing zero values and rejecting gross errors, the TOF ranging data still contain noise interference. Therefore, this paper proposes a noise smoothing quality control mechanism (NQCM) that utilizes the moving Gaussian weighted average filtering30 method to filter and smooth the TOF ranging data. The filtered value d~i for TOF ranging value di is:6 d~i=∑i=1Nwidi

where i=1,2,...,N, N is size value of the sampling set. The weight wi for each ranging value di can be expressed as follows:7 wi=1σ2πe-(di-μ)22σ2

where μ is the mean of the sampling set and σ is the standard deviation of the ranging value the sampling set.

Furthermore, we propose a barometric measurement method to improve the quality of atmospheric pressure data for walking surveyed IBBF. The commonly used method uses a fixed base station as the barometric reference point31. With an increase in distance from the fixed base station floor, the atmospheric connectivity between different floors weakens, leading to an escalation in the differential barometric pressure error. The proposed barometric measurement method does not rely on a fixed barometric reference point. The base station’s barometric data is used as the initial barometric reference point. Following a change in the measurement point floor, the barometric data from a period before the floor change is utilized as the new reference point for calculating differential barometric pressure. As shown in Fig. 4, we assume that the pedestrian’s initial position is on the i-th floor. Before the pedestrian goes upstairs, we use the base station’s barometric data as the initial barometric reference point. Since the pedestrian's height remains relatively constant while walking on level ground, we infer an upward movement if the barometric difference exceeds a predefined threshold over a certain period. After going upstairs, we utilize the barometric data from the floor before going upstairs as the new barometric reference point for calculating the differential barometric pressure. Consequently, our proposed method for differential barometric height measurement mitigates altimetry errors arising from variations in atmospheric conditions across different floors.Figure 4 The barometric measurement method for walking surveyed IBBF.

Differential barometric altimetry32,33 includes the calibration the air pressure of reference point and the measurement of height difference between a sample point and the reference point. The model of air pressure and altitude based on the isothermal atmosphere assumption is as follows:8 h -h0= 18410(1 +Tm273.15)lgP0P

where h0 represents the elevation of datum point. Tm=0.5(T0+T) represents the average value of reference point temperature T0 and sample point temperature T, which can be approximated as Tm=T0=T in the indoor environment. P0 indicates the atmospheric pressure at the reference point. P represents the atmospheric pressure at sample point.

In this paper, the sampling set is obtained using a sliding window approach with a duration of one second. Third-order Hermite interpolation is applied to process the ZQCM on the target sampling set obtained from the sliding window, where the TOF ranging value is zero, including original observational zero values and detected gross error values. The count of zero ranging values is recorded, reflecting the data quality of the base station and serving as a basis for different base stations. Finally, following the implementation of the proposed quality control mechanisms, the IBBF database D~ is expressed as follows:9 D~=d~11d~12d~21d~22⋯d~1NP1⋯d~2NP2⋮⋮d~M1d~M2⋱⋮⋮⋯d~MNPM

where N is the total number of UWB base station. M is the size value of IBBF sample points. Pj,j=1,2,...,M represents the atmospheric pressure collection data at j-th sample point. After the proposed quality control mechanisms, the IBBF quality is improved and corresponding IBBF database generation method is introduced in next part.

Multi-tier distributed IBBF database generation method

In a complex indoor environment, the original IBBF database tends to be extensive due to the lack of an efficient fingerprint management method. To reduce the real-time search time in the IBBF database, a multi-tier distributed IBBF database generation method is constructed. The K-means clustering algorithm (KM) is employed to cluster the gathered data and partition the fingerprint database into multi-tier distributed sub-databases. During online area recognition, the UWB ranging value vector collected online is compared with the center of each sub-database to determine the area recognition result. Nevertheless, the K-means clustering algorithm feces two primary challenges: firstly, the initial clustering center is randomly chosen from the data, leading to varying convergence speeds and clustering outcomes; secondly, the clustering result cannot be ensured to be globally optimal, making it susceptible to local optima. Therefore, in order to solve these problems, this paper proposes a multi-tier distributed algorithm for IBBF database generation, referred to as LSAKM in this article. The LSAKM leverages the global optimization capability and rapid convergence of the Lion swarm algorithm (LSA)34–36 to optimize the selection of the initial clustering center. By utilizing the lion swarm optimization algorithm, the globally optimal clustering center can be determined and subsequently employed as the initial clustering center for the K-means algorithm to perform clustering. Therefore, this LSA optimization solution mitigates the shortcomings arising from the random selection of the initial clustering center. The specific algorithm process is elaborated upon below.

(1) Lion group initialization steps.

Suppose the search space is N-dimensional (meaning the number of UWB base stations is N), and there are M lions (meaning the number of IBBF fingerprints is M). The lion group has MAdult adult lions, including one male lion, i.e., the lion king, and the rest are female lions. The number of cubs is N-MAdult. It can be expressed as follows:10 MAdult=Mβ2≤MAdult≤M2

where β is the proportion of adult lions in the lion swarm. The position of the i-th lion is xi=(xi1,xi2,...,xiN).

(2) Lion king guarding territory steps.

Lion king moves around the best quality food in small areas, updating its position as following:11 xik+1=gk(1+γ‖pik-gk‖)

where gk represents the optimal position of the k-generation lion group. γ is a random number that conforms to the normal distribution n(0,1). pik is the historical optimal position of the i-th lion in the k-th generation.

(3) Lioness cooperative hunting steps.

The lioness cooperates with another lioness while hunting, updating her position as following:12 xik+1=pik+pck2(1+αfγ)

where pck is the historically optimal position of a cooperative lioness randomly selected from among the lionesses. αf is the lioness movement disturbance factor. Let the lioness search for food in a large area, and then the search scope gradually narrows to balance the global search ability and local search ability, as shown in:13 αf=step∗exp(-30tT)10step=0.1(high¯-low¯)

where t represents the number of current iterations. T represents the maximum number of iterations. step represents the maximum step size of the lion's movement. high¯ and low¯ represents respectively the mean of the maximum and minimum values of the lion's range of activity in each dimension.

(4) Lion cubs following steps.

Lion cubs follow the lion king and lioness movements and update their positions according to three behaviors:14 xik+1=pik+gk21+αcγ,pik+pmk21+αcγ,pik+g¯k21+αcγ,q<1313≤q<2323≤q≤1

where pmk is the k-generation historical best position of the lioness followed by the lion cub. g¯k=high¯+low¯-gk indicates the position where the cub is driven. αf=step(T-tT) is the cub movement disturbance factor. q is a random number that conforms to a uniform distribution U(0,1).

To mitigate the issue of randomizing the initial cluster center, the lion optimization algorithm is employed to determine the global optimal solution, which is subsequently utilized as the initial cluster center in the K-means algorithm prior to cluster division. The LSAKM selects a fitness function based on Euclidean distance, as shown in:15 f(uj)=∑i=1M‖uj-si‖

where uj represents the j-th cluster center and the total number of cluster center is O. M represents the total number of data records. si indicates i-th data. ‖uj-si‖ represents the Euclidean distance from the i-th data to the j-th cluster center. According to the nearest neighbor principle, each piece of data divides into the cluster represented by the cluster center closest to it, as shown in:16 E(si)=min‖si-uj‖

where E(si) represents the cluster of sampling data si. Finally, after the proposed LSAKM, the multi-tier distributed IBBF database D^ can be expressed as follows:17 D^=D^1D^2⋮D^j

The original IBBF database is partitioned into multi-tier distributed sub-databases using the proposed LSAKM method. The multi-tier distributed IBBF database, serving as an efficient fingerprint management method, decreases the time required for real-time area recognition within the IBBF database. In alignment with the proposed multi-tier distributed IBBF database, an MTD-JPM based recognition algorithm is introduced in the following section.

MTD-JPM based recognition algorithm

A multi-tier distributed joint probabilistic model is proposed for area recognition using integrated UWB and barometer data. This model utilizes the area recognition probability from the Manhattan distance based weighted K-nearest neighbors method (MDWKNN) and the adaptive momentum estimation based BP neural network method (AMEB) proposed in this paper. Since these area recognition probabilities are independent, the joint probability can more effectively capture the probability features of IBBF in various regions, resulting in more reliable and accurate position recognition outcomes. Assume that the MTD-JPM of the sample is P∨ as follows:18 P∨=PAMDWKNNPBAMEB=pA1pB1,pA2pB2,...pAnpBn

where PAMDWKNN={pA1,pA2,...pAn} is the probability of a sample belonging to each region predicted by the MDWKNN method, and PBAMEB={pB1,pB2,...pBn} is the probability of a sample belonging to each region predicted by the AMEB method. n is the total number of region.

The weighted K-nearest neighbors (WKNN) based methods37,38 commonly use the Euclidean distance (ED)39 as the similarity measurement of fingerprinting vectors. Compared with the ED, Manhattan distance (MD)40 has lower computing complexity. This study selects Manhattan distance as the similarity measurement of WKNN, and its calculation formula is as follows:19 MD=∑k=1m|dik-dk|

The quality of UWB ranging measurements is variable due to non-line-of-sight conditions, personnel movement and various noise in complex indoor environments. To improve the stability and accuracy of area recognition, the ranging data from different base stations are assigned varying weights depending on the frequency of zero values and gross errors. Assuming that there are a total of n regions and m base stations, the sum of the zero value and gross error of the k-th base station in the process of the i-th region is Countij, and the weight assigned to the ranging value of the base station is as following:20 wik=Countik∑k=1mCountik

The weighted Manhattan distance is:21 MD∨=∑k=1mwikdik-dk

In addition, the weight normalization processing is as following:22 w~i=wi∑i=1Kwi

After obtaining the normalized weight of each fingerprinting, the probability P of each region can be calculated. Assuming that the number of K fingerprints in the j-th region is l. The probability that the location point belongs to the j-th region is PjMDWKNN as following:23 PjMDWKNN=∑i=1lw~ji

where w~ji represents the normalized weight of i-th fingerprints belonging to the j-th region.

We propose an adaptive momentum estimation (AMEB) BP neural network that combines the advantages of momentum gradient descent (momentum) algorithm and root mean square propagation (RMSProp) algorithm. This approach addresses the issues related to slow training speed and vulnerability to local optima commonly encountered in conventional gradient descent methods. By incorporating the concept of momentum, the model substitutes the original gradient with the exponentially weighted average gradient for parameter update, thereby mitigating gradient oscillation. The model calculates the weight and bias adjustments by dividing the accumulated gradient values by the historical parameters accumulation in each parameter update iteration, leading to a reduction in update magnitude and facilitating quicker convergence. The gradient momentum and gradient weighted mean squared values can be expressed as follows:24 v∇wt=βv∇wt-1+(1-β1)∇wt-1v∇bt=βv∇bt-1+(1-β1)∇bt-1s∇wt=βs∇wt-1+(1-β2)∇wt-12s∇bt=βs∇bt-1+(1-β2)∇bt-12

where v∇wt-1 and v∇bt-1 represent the accumulated gradient momentum of weights and biases in the previous t-1 rounds respectively. ∇wt-1 and ∇bt-1 respectively represent the gradients of weights and biases. β1 represents the momentum coefficient. β2 represents the weighting coefficient of mean square value of gradient.

The softmax function is used to convert the output of the output layer neurons into predicted probabilities for each region that the sample belongs to, and its calculation formula is:25 p^i=Softmax(yi)=eyi∑i=1neyi

where n represents the number of regions and yi represents the output of the i-th neuron in the output layer. The region with the highest predicted probability is taken as the result of 3D position sensing. The cross-entropy function was used as the loss function in this paper. It can be expressed as follows:26 loss=∑i=1N∑j=1n-pijlogp^ijN

where pij and p^ij respectively represent the true probability and predicted probability of the i-th training data belonging to j-th region. N is the total number of training data.

After obtaining area recognition probability of MDWKNN and AMEB, the MTD-JPM can calculate the probability of each region. The 3D area recognition result of user S can be estimated according to the MTD-JPM from following formula:27 argmaxSPi∨,s.t.i=1,2,⋯,n

Hence, the highest probability area is the target location region of sample points. The experimental evaluation in the following section demonstrates the enhanced accuracy and computational efficiency of the proposed 3D area recognition algorithm.

Experiments and discussions

Experimental protocol

The experimental scenes include the first floor, second floor and third floor of the Experimental Building of Wuhan University, and the real scene is shown in Fig. 5. The floor plans of the first, second and third floors and the division of areas are shown in Fig. 5a–c. Four UWB base stations are placed in the four corners of the corridor on the second floor. The first testbed is divided into seven areas A1–A7. The second testbed is divided into 20 areas A8~A27, and the third testbed is also divided into seven areas A28~A34. The experimental scenes include computer rooms, conference rooms, corridors, halls, lounges and other scenarios. In this experiment, the sampling frequency of the UWB base station is 20 Hz, and a total of 9424 pieces of data were collected, which took 8 min. The buffer width of the inner boundary is set to 0.3 m.Figure 5 Experimental scene. (a) First floor plan and zoning. (b) Second floor plan and zoning. (c) Third floor plan and zoning.

The chip used in the UWB base station is the DW1000 chip, as shown in Fig. 6. The working frequency band range of the UWB module is 6.24–6.74 GHz, the bandwidth is 500 MHz, the output power spectral density is − 23 dBm/MHz, the antenna gain is 3 db, and the effective distance is 50 m. The self-developed main base station and the auxiliary base station can communicate with each other, and the TOF-based ranging algorithm is built into the base station. In the data collection stage, the tag sends the data packet to the four base stations through broadcasting. The calculated distance value and RSSI data are sent to the main base station connected to the serial port. The main base station is connected to the computer through the serial port, and then the data is sent to the data collection software.Figure 6 Hardware architecture of the area recognition module. (a) UWB main base station. (b) UWB auxiliary base station. (c) UWB tag.

Experimental results of IBBF quality control mechanisms

This section describes an experiment conducted to validate the effectiveness of the quality control mechanisms for walk-surveying IBBF. The experiment employs handheld UWB tags for IBBF collection within the inner boundary buffer of each region, capturing real-time ranging data from each sampling point to the four UWB base stations and the corresponding region number. Subsequent to the IBBF collection in each area, the fingerprint data undergoes a quality control process, which consists of three steps: zero value quality control on UWB TOF ranging data, gross error quality control, and noise control. Figures 7, 8, 9 shows the comparison of quality control results of UWB TOF ranging data. Figure 7 illustrates the intermittent absence of base station ranging data during IBBF collection. Interpolates the missing data effectively and fits the dataset accurately. It also records the count of zero values, indicating the data quality. Figure 8 demonstrates the GQCM capability in identifying sporadic significant fluctuations in the ranging values. The detection of gross errors varies among different UWB base stations, suggesting the quality of the UWB base station data. Figure 9 shows that that post-NQCM processing results in improved precision and smoothness of the ranging data, reducing noise interference. After ZQCM, GQCM, and NQCM processing, the IBBF ranging data from each base station exhibits reduced fluctuations and significantly enhanced data quality, thereby improving the accuracy of subsequent fingerprint matching for position estimation.Figure 7 The UWB TOF ranging performance of ZQCM.

Figure 8 The UWB TOF ranging performance of GQCM.

Figure 9 The UWB TOF ranging performance of NQCM.

Experimental results of LSAKM

This section presents an experiment to verify the localization performance of the LSAKM for generating IBBF database. Figure 10 illustrates the comparison of localization performance across various K values using four methods: WKNN, KM + WKNN, LSAKM + WKNN, and the proposed LSAKM+MDWKNN. The MP, MR, and MF1 values of the proposed area recognition method, which is based on the LSAKM fingerprinting library clustering division and MDWKNN algorithm, exhibit the highest performance. At K value of 3, the highest MP, MR, and MF1 achieved are 99.9%, 99.91%, and 99.9%, respectively. The WKNN method achieves the highest MP, MR, and MF1 values at K values of 1 and 2, which are 97.45%, 97.43%, and 97.44%, respectively. The KM+WKNN method achieves the highest MP, MR, and MF1 values at K values of 1 and 2, with values of 97.49%, 97.48%, and 97.49%, respectively. The LSAKM+WKNN method achieves the highest MP, MR, and MF1 values at K values of 1 and 2, the highest MP, MR, and MF1 values of 97.62%, 97.55%, and 97.59% were obtained, respectively. The experimental results indicate the utilization of LSAKM clustering method leads to a slightly enhancement in localization accuracy of approximately 0.2%. Substituting the Euclidean distance with the weighted modified Manhattan distance can enhance the localization accuracy by approximately 3%. The results demonstrate the high effectiveness of the clustering algorithm in enhancing the real-time localization. Additionally, the proposed LSAKM + MDWKNN achieves the shortest localization time of 0.52 s at K value of 1. This represents an 89% improvement compared to the 4.67 s required by WKNN.Figure 10 Comparison of area recognition performance from different K-value area recognition algorithms: (a) mean precision; (b) mean recall; (c) mean F1 score; (d) total recognition time.

Experimental results of 3D area recognition

Sampling of barometric data was performed by traversing each floor of the test area and assessing the accuracy of different barometric altimetry methods. The base station was placed on a tripod with a height of 1 m on the first floor to serve as the initial barometric reference point. Both the base station and the tag were equipped with built-in barometric pressure sensors. The height of the tag from the ground was 1 m. The temperature during sampling was 15 °C. Each floor has a height of 4.2 m. Given the height of the base stations as the initial height of 0 m, the heights of the following floors are 0 m, 4.2 m, 8.4 m, 12.6 m, 16.8 m, 21.0 m, 25.2 m, 29.4 m, and so on.

The variation of air pressure data is collected at the measurement points on each floor. The height changes at the measurement points are determined using the improved differential air pressure altimetry method, resulting in slight fluctuations in heights between the same floors due to minor vertical movements while walking. The precise location of each measurement point on the floor is determined with 100% accuracy. The variation of height error at each measurement point is shown in Fig. 11 The height errors at all measurement points were less than 0.8 m, with most floors having errors less than 0.4 m, and a few floors falling within the range of 0.4–0.7 m.Figure 11 Floor positioning performance. (a) Air pressure data. (b) Calculated height values. (c) Calculated height error.

The cumulative distribution function (CDF) of the height difference solution based on hypsometric, barometric, differential altimetry and the improved differential altimetry proposed in this paper is illustrated in Fig. 12. The figure illustrates that the improved differential barometric altimetry method has better floor positioning accuracy compared to other barometric methods used in this experiment. The probability of the error being less than 1 m and 0.5 m for the improved differential barometric height measurement method is 100% and 85%, respectively. For differential barometric altimetry, the probability of the error being less than 1 m and 0.5 m is 94% and 60%, respectively. The probability of the error being less than 1 m and 0.5 m for hypsometric altimetry is 75% and 43%, respectively. For barometric altimetry, the probability of the error being less than 1 m and 0.5 m is 75% and 50%, respectively. Therefore, the proposed floor identification method based on differential barometer measurements demonstrates sufficient accuracy and reliability, achieving a 100% success rate in floor identification.Figure 12 Cumulative distribution function of height errors of different methods.

The experiment results from Fig. 13 and Table 1 demonstrate that the proposed MTD-JPM 3D area recognition method, utilizing integrated UWB and barometer measurements, achieves an average accuracy, recall and F1 value exceeding 99.2% for 3D area recognition across all the tested areas. This represents a 4–5% improvement compared to the previously reported BP and WKNN fingerprinting methods. In the context of personnel security applications, an area recognition accuracy of exceeding 99% is necessary, and the improvement of 4–5% is adequately significant.Figure 13 Comparison of 3D area recognition performance of different algorithms. (a) Precision. (b) Recall. (c) F1 Score.

Table 1 Comparison of 3D area recognition performance of different algorithms.

Algorithm	MP (%)	MR (%)	M F1 (%)	
WKNN41	94.58	95.62	95.09	
BP42	94.32	94.01	94.16	
Ours MTD-JPM	99.20	99.28	99.24	
Significant values are given in bold.

Experimental results of computing time consumption

Various values of the number of clusters O impact both the accuracy and the consumption time of area recognition. By varying the size of the clustering number O from 1 to 20, and the average precision of area recognition is calculated. Figure 14 illustrates the area recognition performance of the four methods. In terms of area recognition accuracy, the mean precision fluctuates with the increase of the cluster number as shown in Fig. 14a, but the proposed LSAKM+MDWKNN algorithm achieves the highest area recognition accuracy. The highest mean precision is 99.91% when the number of clusters O is 13. In terms of area recognition efficiency, it can be seen from Fig. 14b that the time consumption is smaller than WKNN algorithm after clustering in all cases. With an increase in the number of clusters, the time consumption for area recognition decreases. This shows that the clustering algorithm can improve the real-time performance of area recognition, and the area recognition time of the proposed LSAKM+WKNN algorithm is the smallest for most of the number of clusters. Taking into account both area recognition accuracy and efficiency, the cluster number O is selected as 13. The computational complexity of the four methods is O(n). The total time consumption of the proposed LSAKM+MDWKNN method is 0.34 s when the number of clusters O is 13, which is only 7.05% of that of the WKNN method (4.82 s). In other words, the computing efficiency is enhanced by approximately 93%.Figure 14 Comparison of area recognition performance from different O value algorithm. (a) Mean precision. (b) Total recognition time-consuming.

As with the majority of studies, the design of the current study is subject to limitations. There are three major limitations in this study that could be addressed in future research. First, this study focuses on the application to handheld tags. In the future, a fingerprint database could be established in various postures, such as placing the tag in a pocket or attaching it to the arm. Second, this study does not address the problem of fingerprint database update. The original fingerprint database can be revised and updated according to environmental changes in the future work. Third, this study only uses UWB as positioning source. Future studies could explore multi-source fusion incorporating positioning sources like Wi-Fi, Bluetooth, geomagnetism, or IMU to enhance positioning accuracy and robustness.

Conclusions

This paper proposes a machine learning based 3D area recognition solution that utilizes integrated UWB ranging and barometer measurements to construct the IBBF radio map. This proposed 3D area recognition solution reduces the on-site deployment workload and ensures reliable area recognition performance, even in indoor NLOS conditions. The proposed MTD-JPM 3D area recognition method achieves an average accuracy of 99.2%, significantly outperforming other state-of-the-art area recognition methods. The proposed multi-tier distributed IBBF radio map improves the computing efficiency by 93%. The errors of improved differential barometric height estimation method are within 1 m, resulting in a 100% success rate for floor identification, with a floor separation of 3–4 m.

In future work, the fingerprinting-based 3D area recognition method will be developed, incorporating both UWB and barometer technologies to accommodate different body positions. A comprehensive database of fingerprints captured in various postures will be created, enabling the identification of pedestrian postures through the analysis of data from multiple sensors for accurate area recognition across different body positions. Additionally, the proposed method will include strategies for updating the fingerprinting database to ensure adaptability.

Author contributions

Conceptualization, F.Y. and D.L.; methodology, F.Y., D.L. and X.G.; software, F.Y., D.L. and X.G.; validation, F.Y., D.L. and X.G.; formal analysis, F.Y. and D.L.; investigation, F.Y. and D.L.; resources, F.Y. and D.L.; data curation, F.Y., D.L. and X.G.; writing—original draft preparation, F.Y., D.L., R.C., J.H. and X.G.; writing—review and editing, R.C., J.H. and X.G.; visualization, F.Y. and D.L.; supervision, R.C., J.H. and X.G.; project administration, F.Y.; funding acquisition, F.Y. and X.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported in part by China Southern Power Grid co. Limited Science and Technology Program, grant number GDKJXM20220188, in part by the Postdoctoral Fellowship Program of CPSF under Grant GZB20230538, in part by the Natural Science Foundation of Hubei Province of China under Grant 2023AFB081 and Grant 2024AFD403, in part by Wuhan knowledge innovation research program under Grant 2022010801010109, in part by Wuhan AI innovation research programme under Grant 2023010402040029, in part by Shenzhen Science and Technology Program under Grant JCYJ20210324123611032, in part by Fundamental Research Fund Program of LIESMARS, and in part by LIESMARS Special Research Funding.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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