Fingerprint Library Research Articles

UWB-Assisted Bluetooth Localization Using Regression Models and Multi-Scan Processing.

Bluetooth devices have been widely used for pedestrian positioning and navigation in complex indoor scenes. Bluetooth beacons are scattered throughout the entire indoor walkable area containing stairwells, and pedestrian positioning can be obtained by the received Bluetooth packets. However, the positioning performance is sharply deteriorated by the multipath effects originating from indoor clutter and walls. In this work, an ultra-wideband (UWB)-assisted Bluetooth acquisition of signal strength value method is proposed for the construction of a Bluetooth fingerprint library, and a multi-frame fusion particle filtering approach is proposed for indoor pedestrian localization for online matching. First, a polynomial regression model is developed to fit the relationship between signal strength and location. Then, particle filtering is utilized to continuously update the hypothetical location and combine the data from multiple frames before and after to attenuate the interference generated by the multipath. Finally, the position corresponding to the maximum likelihood probability of the multi-frame signal is used to obtain a more accurate position estimation with an average error as low as 70 cm.

A Pseudo-Satellite Fingerprint Localization Method Based on Discriminative Deep Belief Networks

Pseudo-satellite technology has excellent compatibility with the BDS satellite navigation system in terms of signal systems. It can serve as a stable and reliable positioning signal source in signal-blocking environments. User terminals can achieve continuous high-precision positioning both indoors and outdoors without any modification to the navigation module. As a result, pseudo-satellite indoor positioning has gradually emerged as a research hotspot in the field. However, due to the complex and variable indoor radio propagation environment, signal propagation is interfered with by noise, multipath, non-line-of-sight (NLOS) propagation, etc. The geometric relation-based localization algorithm cannot be applied in indoor non-line-of-sight environments. Therefore, this paper proposes a pseudo-satellite fingerprint localization method based on the discriminative deep belief networks (DDBNs). The method acquires the model parameters of pseudo-satellite multi-carrier noise density signal strength in non-line-of-sight indoor spaces through a greedy unsupervised learning method and gradient descent-supervised learning method. It establishes a mapping relationship between the implied features of the pseudo-satellite multi-carrier noise density signal strength and indoor location, enabling pseudo-satellite fingerprint matching localization in indoor non-line-of-sight environments. In this paper, the performance of the positioning algorithm is verified in dynamic and static scenarios through numerous experiments in a laboratory environment. Compared to the commonly used localization algorithms based on fingerprint library matching, the results demonstrate that, in indoor non-line-of-sight test conditions, the system’s 2D static positioning has a maximum error of less than 0.24 m, an RMSE better than 0.12 m, and a 2σ (95.4%) positioning error better than 0.19 m. For 2D dynamic positioning, the maximum error is less than 0.36 m, the average error is 0.23 m, and the 2σ positioning error is better than 0.26 m. These results effectively tackle the challenge of pseudo-satellite indoor positioning in non-line-of-sight environments.

Research and Design of an Automated Security Event Analysis and Handling Framework Based on Threat Intelligence

In order to deeply explore and utilize the value of threat intelligence, strengthen research on attack organizations, and grasp the correlation between attack organizations, the author proposes the research and design of an automated security event analysis and handling framework based on threat intelligence. The author extracts the behavioral characteristics of the attack organization based on known APT attacks, and uses the machine learning framework Light GBM to establish a multi classification model to complete the analysis of unknown APT attack organizations. Through the study of multi-dimensional analysis of multi-source threat intelligence, attack organization correlation and judgment, an attack organization correlation and judgment system has been designed and implemented. The system includes six modules: threat intelligence collection module, threat intelligence multi-dimensional analysis module, attack organization fingerprint library module, attack organization correlation module, attack organization analysis module, and user module, providing attack organization correlation and judgment services for network security. The test results show that the intelligence reading and search query function can achieve the reading of various information of attack organizations, and achieve visual display of threat intelligence. The intelligence management function can achieve operations such as adding, deleting, and updating intelligence. The user management function of the system can achieve the management of administrator users and ordinary users. After testing, all functions of the system have been implemented and meet expectations.

Phytoplankton optical fingerprint libraries for development of phytoplankton ocean color satellite products

Phytoplankton respond to physical and hydrographic forcing on time and space scales up to and including those relevant to climate change. Quantifying changes in phytoplankton communities over these scales is essential for predicting ocean food resources, occurrences of harmful algal blooms, and carbon and other elemental cycles, among other predictions. However, one of the best tools for quantifying phytoplankton communities across relevant time and space scales, ocean color sensors, is constrained by its own spectral capabilities and availability of adequately vetted and relevant optical models. To address this later shortcoming, greater than fifty strains of phytoplankton, from a range of taxonomic lineages, geographic locations, and time in culture, alone and in mixtures, were grown to exponential and/or stationary phase for determination of hyperspectral UV-VIS absorption coefficients, multi-angle and multi-spectral backscatter coefficients, volume scattering functions, particle size distributions, pigment content, and fluorescence. The aim of this publication is to share these measurements to expedite their utilization in the development of new optical models for the next generation of ocean color satellites.

VTIL: A multi-layer indoor location algorithm for RSSI images based on vision transformer

As WiFi technology becomes more widespread and integrated, precise location tracking within complex indoor environments is gaining significance in contemporary public spaces.Nevertheless, challenges persist in indoor scenarios, including issues such as noise and multi-path effects stemming from the degradation of the Received Signal Strength Indicator (RSSI). Additionally, obstacles causing signal shading contribute to a decrease in the accuracy of indoor location tracking. Within this paper, we introduce an innovative indoor localization algorithm termed Vision Transformer Indoor Localization (VTIL). This algorithmleverages RSSI and Vision-Transformer (ViT) technologies to enhance indoor positioning accuracy. Initially, the RSSI fingerprints undergo normalization by scaling them to the maximum and minimum values. To mitigate the impact of noise and irrelevant features, the Principal Component Analysis (PCA) algorithmis then employed for effective feature extraction.Secondly, the RSSI fingerprint library is converted into an RSSI gray image library. Then the RSSI gray image is divided into several small blocks, and the position-coding input is performed in the form of a sequence. The ViT model divides the weight ratio of each block in the RSSI image to alleviate the impact of multi-path effects. According to the experimental results using public datasets, our approach achieves a noteworthy 37.26% reduction in the average distance estimation error when compared to existing indoor fingerprint localization algorithms.

Positioning by floors based on WiFi fingerprint

WiFi-based indoor positioning technology has gradually become a hot research topic in the field of indoor positioning, but the development of this technology has been facing the challenge of susceptibility to environmental interference. Therefore, in this paper, the kernel function method (KFM) with stronger interference resistance is used for positioning, and the adaptive algorithm is proposed for the time-consuming and laborious problem of manual parameter tuning, which incorporates the ideas of cross-validation and iteration. In addition, too many wireless access points (APs) mean higher computational cost and longer positioning time, so it is necessary to choose reasonable APs for positioning. In this paper, we use the random forest (RF) algorithm to assess the importance of APs and filter out a small number of APs with high importance. Considering the obvious differences in the WiFi signals received on different floors, a system framework for positioning by floors based on WiFi fingerprints is proposed. In the offline phase, the fingerprint library is first divided according to floors, and then perform separately AP selection and parameter tuning for each sub-fingerprint library. In the online phase, support vector machine is used to discriminate the floors first, and then KFM is used for planar positioning. Experiments are conducted on the public dataset, and the results show that the proposed algorithm has higher positioning accuracy, more robustness, and less time-consuming compared to several common algorithms.

Epidemiologic characteristics and drug resistance of isolated from blood culture escherichia coli in a hospital in Qinghai Province from 2016 to 2022

Objective: To explore the drug resistance of Isolated From Blood Culture Escherichia coli (E. coli) in a hospital in Qinghai over the past seven years, to evaluate the ability of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to analyze the homologous origin of E. coli, and to establish a protein fingerprint library to match with it, adjuvant clinical experience medication so as to provide the basis for the prevention and control of hospital-acquired infections. Methods: Retrospective analysis of blood cultures sent to hospitals from January 2016 to December 2022. Drug resistance and resistance changes in E. coli.A total of 1 841 E. coli strains were isolated from Qinghai Provincial People's Hospital from January 2016 to December 2022; all strains were identified by MALDI-TOF MS, and the VITEK2.0 drug sensitivity analyzer was applied for drug sensitivity analysis of the strains, and the mass spectrometry homology analysis and self-constructed protein fingerprint library were carried out by MALDI-Biotyper software; the protein fingerprint library was built by using WHONET5.6 software was used to statistically analyze the drug sensitivity results, SPSS23.0 software was used to analyze the relationship between fingerprint typing and drug sensitivity, and the χ2 test was used for intergroup comparisons. Results: A total of 1 841 strains of E. coli were detected in 4 582 positive blood culture specimens from January 2016 to December 2022, with a detection rate of 40.17%; the resistance rate of E. coli from blood sources to piperacillin/tazobactam and ceftriaxone was on the rise, and it was slightly decreased to cefepime, amikacin, levofloxacin, and sulfamethoxazole, and there was not much change to the rest of the drugs; After MALDI-Biotyper clustering analysis, the 1841 E. coli strains from Isolated From Blood Culture were classified into two major clusters and five subtypes, of which type Ⅰa1 accounted for about 40%, type Ⅰa2 accounted for about 2.7%, type Ⅰb accounted for about 3.8, type Ⅱa accounted for about 46%, and type Ⅱb accounted for about 7.5%. The detection rate of type Ⅰa1 E. coli was higher in general surgery (50.45%) and emergency surgery (50.92%), and the detection rate of type Ⅰb E. coli was higher in emergency medicine(10.05%)than in other departments. The drug sensitivity results of different subtypes were compared with each other, the resistance rate of type Ⅰa1 E. coli to cefepime was 21.3% higher than that of the remaining four types, and the difference was statistically significant (χ2=37.74,P=0.000); the resistance rate of type Ⅱ E. coli(>60%) to sulfamethoxazole was higher than that of type Ⅰ (<60%) as a whole, and the difference was statistically significant (χ2=15.248,P=0.004); and a preliminary database of homologous protein fingerprints of E. coli has been established E. coli homologous protein fingerprint library and validated. The drug susceptibility results of 1 288 E. coli strains in the validation set were statistically analyzed and compared with those in the training set. There was no significant difference(P>0.05). Conclusion: In recent years, the resistance rate of E. coli isolated from a hospital in Qinghai province to piperacillin/Tazobactam, cefepime, amicacin and other antibiotics has changed greatly. A fingerprint database of E. coli homologous protein was established, and it was found that the drug sensitivity data of E. coli were different among different fingerprint types. According to drug sensitivity, drug use could assist clinical experience and provide evidence for prevention and control of hospital illness.

Traffic spills the beans: A robust video identification attack against YouTube

Traffic-based video identification attack is a side-channel attack that allows an attacker to identify transmitted online videos from encrypted network traffic. Existing attacks face two types of challenges: 1) YouTube's improved streaming technology renders them invalid or impacted; 2) real-world diverse traffic makes them underperform. In this paper, we provide the first study of YouTube's technical improvements and reveal its three key features: i) the mechanism that replaces the MPD file, ii) variable combined segments, and iii) various buffering processes. Based on the above analysis, we propose a Robust and easily scalable Video Identification Attack against YouTube called RoVIA. RoVIA extracts video fingerprints from video file headers and reference sequences from video traffic. RoVIA uses the reference sequence to generate candidate sequences in the pre-built video fingerprint library and achieves video identification by measuring sequence similarity. Experimental results show that RoVIA achieves open-world accuracy of 0.967, 0.924, and 0.863 in open-world, fixed-quality, and poor network experiments. Furthermore, RoVIA's scalability allows the target classes to be adjusted by updating the fingerprint library without collecting new samples and retraining parameters. This study aims to demonstrate the vulnerabilities of YouTube streaming technology and the potential consequences of its malicious use. We also propose two YouTube-friendly countermeasures to help developers build effective defenses.

Employee Presence System Using Nodemcu For Fingerprint And Face Id Internet Of Things (Iot)

This research aims to design and implement an Internet of Things (IoT) based attendance system. The designed system integrates two types of attendance systems, face recognition-based attendance system (FRA) and fingerprint-based attendance system (FPA), with a server. FRA uses Python programming language and Openc CV library, while FPA is developed using ESP8266 MCU Node and AS608 Fingerprint scanner with Adafruit Fingerprint library. FRA and FPA are connected to a web server with a database engine via an internet connection and send attendance data using the HTTP_POST method. The server development uses Apache Webserver, PHP programming language, and MySQL database engine. The server has two primary purposes: to record attendance data sent by FPA and FRA and to generate attendance reports based on user requests. System testing is done on a local network. The results show that the subsystems and integrated systems work well.

Construction of a genome-wide SSR marker library in Gerbera hybrida: Insights into genetic variation and germplasm resources

Gerbera hybrida is a highly sought-after cut flower globally, renowned for its diverse flower color and pattern resulting from its complex inflorescence meristem architecture. However, the scarcity of genomic information and molecular markers has impeded efforts to comprehend the genetic diversity of G. hybrida. To address this, our study aimed to construct genome-wide simple sequence repeat (SSR) markers in G. hybrida, utilizing genomic data from an inbred line. We employed 170 gerbera cultivars exhibiting distinct flower patterns and colors to analyze genetic variation and diversity. Our analyses revealed highly polymorphic SSR markers, a total of 69 alleles (Na) were amplified by 14 pairs of primers, in which polymorphic information content (PIC) ranging from 0.380 to 0.700 with an average value of 0.583. The polymorphic results of the observed heterozygosity (Ho) and Shannon's information index (I) were 0.499 and 1.195, respectively, showing a high level of genetic diversity within the enriched sequences of the G. hybrida genome. Leveraging these markers with strong differentiation, we successfully constructed a unique fingerprint map encompassing 170 gerbera germplasm lines. This study involved the construction of an SSR fingerprint library for G. hybrida germplasm resources, employing genetic analysis for screening and molecular-level identification. The findings provide valuable insights into the identification of gerbera germplasm resources and kinship studies among various groups. Moreover, this study lays a solid foundation for future advancements in molecular design breeding improvement and germplasm resource development in G. hybrida.

Mapping the Acetylamino and Carboxyl Groups on Glycans by Engineered α-Hemolysin Nanopores.

Glycan is a crucial class of biological macromolecules with important biological functions. Functional groups determine the chemical properties of glycans, which further affect their biological activities. However, the structural complexity of glycans has set a technical hurdle for their direct identification. Nanopores have emerged as highly sensitive biosensors that are capable of detecting and characterizing various analytes. Here, we identified the functional groups on glycans with a designed α-hemolysin nanopore containing arginine mutations (M113R), which is specifically sensitive to glycans with acetamido and carboxyl groups. Molecular dynamics simulations indicated that the acetamido and carboxyl groups of the glycans produce unique electrical signatures by forming polar and electrostatic interactions with the M113R nanopores. Using these electrical features as the fingerprints, we mapped the length of the glycans containing acetamido and carboxyl groups at the monosaccharide, disaccharide, and trisaccharide levels. This proof-of-concept study provides a promising foundation for developing single-molecule glycan fingerprinting libraries and demonstrates the capability of biological nanopores in glycan sequencing.

GM(1,1)-Based Weighted K-Nearest Neighbor Algorithm for Indoor Localization

Along with the IoT technology, the importance of indoor positioning is increasing, but the accuracy of the traditional fingerprint positioning algorithm is negatively affected by the complex indoor environment. This issue of low indoor spatial geolocation localization accuracy when the signal is collected away from the present stage occurs due to the signal instability of the iBeacon in the traditional fingerprint localization algorithm, which generates a variety of factors such as object blocking and reflection, multipath effect, etc., as well as the scarcity of reference fingerprint data points. In response, this study proposes an inverse distance-weighted optimization WKNN algorithm for indoor localization based on the GM(1,1) model. By implementing GM(1,1) model pre-process leveling, the original fingerprint library was reconstructed into a large-capacity fingerprint database using the inverse distance-weighted interpolation method. The local inverse distance-weighted interpolation was used for interpolation, combined with the WKNN algorithm to complete the coordinate solution in real time. This effectively solved the issue of low localization accuracy caused by the large fluctuation of the received signal strength (RSS) sampling measurement data and the existence of few reference fingerprint datapoints in the fingerprint database. The results show that this algorithm reduced the average positioning error by 5.9% compared with ordinary kriging (OK) interpolation leveling and reduced the average positioning error by 18.2% compared with the indoor spatial location accuracy of the original fingerprint database, which can effectively improve the positioning accuracy and provide technical support for indoor location and navigation services.

Methods for power line communication carrier equipment identity authentication and wiretapping equipment locating considering physical layer characteristics

Power line communication (PLC) faces a wide range of illegal device access, wiretapping and data tampering. To ensure the confidentiality and integrity of data information, protection technologies based on data encryption and identity authentication are widely used. As one of the defense methods against wiretapping attacks, this paper takes into account the characteristics of the physical layer channel, and proposes a method for locating PLC wire-tapping equipment. First, the physical layer channel characteristics are extracted based on the communication protocol as the device fingerprint. Then the collected fingerprints are compared with the fingerprint library for device identity authentication. And when the identity authentication fails, a branch location method for wiretapping devices based on fingerprint transfer vectors is further proposed. Finally, by modeling the PLC channel and simulating eavesdropping scenarios, it is verified that the proposed method has a high accuracy rate of wiretapping equipment locating.

Rethinking Tag Collisions for Replay and Relay Attack Resistance in Backscatter Networks

Distinguishable physical layer features of radio frequency have the potential to serve as new fingerprints for authentication in backscatter networks. They have a definite advantage that backscatter tags do not have to run resource-intensive operations that commodity tags rarely implement. However, current physical layer authentication schemes impose substantial burdens on both service providers and users. Since physical layer features are highly susceptible to environmental factors, labor-intensive and time-consuming fingerprint library establishment is indispensable to make sufficient statistics for authentication. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TagDuet</i> , a collision-assisted authentication scheme that adopts an auxiliary tag to RFID systems, a typical type of backscatter networks, without the requirement of fingerprint library establishment. TagDuet places an independent backscatter tag in the proximity of a reader and utilizes the features in intended tag collisions to improve wireless security. Our phase cancellation decoding algorithm accurately decodes the collisions, which leads TagDuet fully compatible with the commodity RFID tags. TagDuet provides freshness, the paramount property to resist replay attacks, and robustness to relay attacks under FCC regulations for frequency hopping. We implement a prototype of TagDuet with commodity tags, evaluate the performance in randomized channels by the auxiliary tag, and demonstrate the resilience against replay and relay attacks.

Multiuser Physical-Layer Authentication Based on Latent Perturbed Neural Networks for Industrial Internet of Things

Recently, learning (DL)-based physical-layer authentication (PLA) has attracted much attention since artificial neural networks (ANNs) can be built to extract useful features from complex wireless environments, thus achieving high authentication performance and lightweight deployment in mobile edge computing (MEC)-Industrial Internet of Things (IIoT) scenario. However, the low latency characteristic of MEC makes it impossible to have much time to obtain sufficient signals for training the authentication system, which will cause over-fitting issues and deteriorate the authentication performance. Data augmentation is an effective method to address this problem. However, existing PLA with data augmentation can not generate representative and high-quality samples, consequently lacking generality in the actual identity authentication. To tackle this problem, a novel channel impulse response (CIR)-based multiuser authentication named latent perturbed neural networks (LPNNs) is proposed in this article, aiming at achieving high authentication performance even when trained a few data. Instead of relying on the generation of synthetic samples, the proposed LPNN adds Gaussian noise in the smooth latent space to avoid underdetermined and poor generalization, which has better interpretability. Specifically, to obtain a better understanding than a black box that connects input CIRs to authentication results, we defined Fingerprint Library and provided post-hoc explanations to answer the following question: which library examples explain the authentication results issued for a given CIR sample? Moreover, the simulations under the static and dynamic IIoT scenarios verify the superiority in authentication accuracy of the proposed LPNN over vanilla deep neural network (DNN) and convolutional neural network (CNN).

VULDEFF: Vulnerability detection method based on function fingerprints and code differences

The significant increase in Open Source Software has led to a sharp increase in code cloning vulnerabilities. Code similarity detection methods are usually used to detect these vulnerabilities. However, cloned code often modifies the original code to varying degrees, and the difference between vulnerable code and patch code can be very small. Traditional code similarity detection methods cannot effectively detect common mutation patterns in code cloning and distinguish vulnerable code from patch code. The paper proposes a vulnerability detection method named VULDEFF based on function fingerprints and code differences. This paper designs a lightweight function fingerprint method based on the Context Triggered Piecewise Hashing algorithm, which can characterize the basic syntax features of function source code. In particular, the fingerprint of the vulnerable function contains the syntax features, vulnerability features, and patch features of the vulnerable function. VULDEFF detects whether target function has vulnerabilities by searching target function fingerprint in the vulnerable function fingerprint library. Compared with five advanced software vulnerability detection tools, VULDEFF significantly reduces the false positive and false negative rates while ensuring the scalability of vulnerability detection. VULDEFF discovered 111 new vulnerabilities in 10 open source projects.

A Collection of Molecular Fingerprints of Single Aerosol Particles in Air for Potential Identification and Detection Using Optical Trapping-Raman Spectroscopy.

Characterization, identification, and detection of aerosol particles in their native atmospheric states remain a challenge. Recently, optical trapping-Raman spectroscopy (OT-RS) has been developed and demonstrated for characterization of single, airborne particles. Such particles in different chemical groups have been characterized by OT-RS in recent years and many more are being studied. In this work, we collected single-particle Raman spectra measured using the OT-RS technique and began construction of a library of OT-RS fingerprints that may be used as a reference for potential detection and identification of aerosol particles in the atmosphere. We collected OT-RS fingerprints of aerosol particles from eight different categories including carbons, bioaerosols (pollens, fungi, vitamins, spores), dusts, biological warfare agent surrogates, etc. Among the eight categories, spectral fingerprints of six groups of aerosol particles have been published previously and two other groups are new. We also discussed challenges, limitations, and advantages of using single-particle optical trapping-Raman spectroscopy for aerosol-particle characterization, identification, and detection.

BSSH: Bluetooth Indoor Positioning Strategy for Ship Cabin Based on Hierarchical Area

In the process of ship's ocean transportation, the monitoring of crew's work completion is a very important part in the ship safety management, and using indoor positioning technology can improve the construction of ship management system. However, the current indoor positioning technology is not ideal in the engine room environment in the face of the complex electromagnetic environment and spatial structure. In order to improve the positioning accuracy, this paper proposes a Bluetooth indoor positioning strategy for ship cabin based on hierarchical area (BSSH) for deploying and dividing the regions requiring indoor positioning and combines Bluetooth Low Energy (BLE) with Bluetooth fingerprint positioning technology. Processing the fingerprint library by using region hierarchy and proximity classification algorithms to obtain reliable results in the ship environment, the results show that better Beacon deployment and hierarchical area algorithms can lead to a localization accuracy of 80.38%.

A Rapid Method for Detecting Microplastics Based on Fluorescence Lifetime Imaging Technology (FLIM).

With the increasing use and release of plastic products, microplastics have rapidly accumulated in ecological environments. When microplastics enter the food chain, they cause serious harm to organisms and humans. Microplastics pollution has become a growing concern worldwide; however, there is still no standardized method for rapidly and accurately detecting microplastics. In this work, we used fluorescence lifetime imaging technology to detect four kinds of Nile red-stained and unstained microplastics, and the unique phasor fingerprints of different microplastics were obtained by phasor analysis. Tracing the corresponding pixels of the “fingerprint” in the fluorescence lifetime image allowed for the quick and intuitive identification of different microplastics and their location distributions in a mixed sample. In our work, compared with staining the four microplastics with a fluorescent dye, using the phasor “fingerprint library” formed by the autofluorescence lifetimes of the microplastics was more easily distinguished than microplastics in the mixed samples. The feasibility of this method was further tested by adding three single substances—SiO2, chitin and decabromodiphenyl ethane (DBDPE), and surface sediments to simulate interferent in the environment, and the results providing potential applications for the identification and analysis of microplastics in complex environments.

A study on the cabin indoor localization algorithm based on adaptive K-values

In ocean voyages, cabin indoor localization plays a considerably important role in ship safety management. Due to the special structure of ships and the influence of internal and external environment, the mainstream indoor positioning technology nowadays is not ideal for positioning inside the cabin. In order to improve the positioning accuracy, this article proposes an adaptive K-value-based cabin indoor positioning algorithm. The algorithm constructs a fingerprint library by collecting Received Signal Strength Indication data and carries out filtering; matches fingerprint information by adaptive K-values during the localization process, while using double K-nearest neighbor to reduce the influence of outlier points on the final localization; and finally uses the multi-point mass center method to determine the final localization results. The experimental results in the automated cabin laboratory show that the average positioning error of the algorithm is 1.74 m, which can well meet the cabin indoor positioning requirements.