Sample self-selection using dual teacher networks for pathological image classification with noisy labels.

Deep neural networks (DNNs) involve advanced image processing but depend on large quantities of high-quality labeled data. The presence of noisy data significantly degrades the DNN model performance. In the medical field, where model accuracy is crucial and labels for pathological images are scarce and expensive to obtain, the need to handle noisy data is even more urgent. Deep networks exhibit a memorization effect, they tend to prioritize remembering clean labels initially. Therefore, early stopping is highly effective in managing learning with noisy labels. Previous research has often concentrated on developing robust loss functions or implementing training constraints to mitigate the impact of noisy labels; however, such approaches have frequently resulted in underfitting. We propose using knowledge distillation to slow the learning process of the target network rather than preventing late-stage training from being affected by noisy labels. In this paper, we introduce a data sample self-selection strategy based on early stopping to filter out most of the noisy data. Additionally, we employ the distillation training method with dual teacher networks to ensure the steady learning of the student network. The experimental results show that our method outperforms current state-of-the-art methods for handling noisy labels on both synthetic and real-world noisy datasets. In particular, on the real-world pathological image dataset Chaoyang, the highest classification accuracy increased by 2.39 %. Our method leverages the model's predictions based on training history to select cleaner datasets and retrains them using these cleaner datasets, significantly mitigating the impact of noisy labels on model performance.

AGR2 and FOXA1 as prognostic markers in ER-positive breast cancer.

BACKGROUND: The prognostic role of either forkhead box A1 (FOXA1) or anterior gradient 2 (AGR2) in breast cancer has been found separately. Considering that there were interplays between them depending on ER status, we aimed to assess the statistical interaction between AGR2 and FOXA1 on breast cancer prognosis and examine the prognostic role of the combination of them by ER status. METHODS: AGR2 and FOXA1 expression in tumor tissues were evaluated with tissue microarrays by immunohistochemistry in 915 breast cancer patients with follow up data. The expression levels of these two markers were treated as binary variables, and many different cutoff values were tried for each marker. Survival and Cox proportional hazard analyses were used to evaluate the relationship between AGR2, FOXA1 and prognosis, and the statistical interaction between them on the prognosis was assessed on multiplicative scale. RESULTS: Statistical interaction between AGR2 and FOXA1 on the PFS was significant with all the cutoff points in ER-positive breast cancer patients but not ER-negative ones. Among ER-positive patients, the poor prognostic role of the high level of FOXA1 was significant only in patients with the low level of AGR2, and vice versa. When AGR2 and FOXA1 were considered together, patients with low levels of both markers had significantly longer PFS compared with all other groups. CONCLUSIONS: There was a statistical interaction between AGR2 and FOXA1 on the prognosis of ER-positive breast cancer. The combination of AGR2 and FOXA1 was a more useful marker for the prognosis of ER-positive breast cancer patients.

Combined low levels of H4K16ac and H4K20me3 predicts poor prognosis in breast cancer.

BACKGROUND: Results of previous studies about the prognostic roles of histone H4 lysine 16 acetylation (H4K16ac) and histone H4 lysine 20 trimethylation (H4K20me3) in breast cancer were inconsistent. Cellular experiments revealed the interplays between H4K16ac and H4K20me3, but no population study explored the interaction between them on the prognosis. METHODS: H4K16ac and H4K20me3 levels in tumors were evaluated by immunohistochemistry for 958 breast cancer patients. Hazard ratios for overall survival (OS) and progression-free survival (PFS) were estimated using Cox regression models. Interaction was assessed on multiplicative scale. Concordance index (C-index) was calculated to verify the predictive performance. RESULTS: The prognostic roles of the low level of H4K16ac or H4K20me3 were significant only in patients with the low level of another marker and their interactions were significant. Moreover, compared with joint high levels of both them, only the combined low levels of both them was associated with a poor prognosis but not the low level of single one. The C-index of the clinicopathological model combined the joint expression of H4K16ac and H4K20me3 [0.739 for OS; 0.672 for PFS] was significantly larger than that of the single clinicopathological model [0.699 for OS, P < 0.001; 0.642 for PFS, P = 0.003] or the model combined with the single H4K16ac [0.712 for OS, P < 0.001; 0.646 for PFS, P < 0.001] or H4K20me3 [0.724 for OS, P = 0.031; 0.662 for PFS, P = 0.006]. CONCLUSIONS: There was an interaction between H4K16ac and H4K20me3 on the prognosis of breast cancer and the combination of them was a superior prognostic marker compared to the single one.

Interaction of reproductive tract infections with estrogen exposure on breast cancer risk and prognosis.

BACKGROUND: Reproductive tract infections influenced a series of inflammatory processes which involved in the development of breast cancer, while the processes were largely affected by estrogen. The present study aimed to explore the associations of breast cancer risk and prognosis with reproductive tract infections and the modification effects of estrogen exposure. METHODS: We collected history of reproductive tract infections, menstruation and reproduction from 1003 cases and 1107 controls and a cohort of 4264 breast cancer patients during 2008-2018 in Guangzhou, China. We used logistic regression model to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for risk; Cox model was applied to estimate the hazard ratios (HRs) and 95% CIs for progression-free survival (PFS) and overall survival (OS). RESULTS: It was found that previous reproductive tract infections were negatively associated with breast cancer risk (OR = 0.80, 95%CI, 0.65-0.98), particularly for patients with more menstrual cycles (OR = 0.74, 95%CI, 0.57-0.96). Patients with previous reproductive tract infections experienced better OS (HR = 0.61; 95% CI, 0.40-0.94) and PFS (HR = 0.84; 95% CI, 0.65-1.09). This protective effect on PFS was only found in patients with more menstrual cycles (HR = 0.52, 95% CI:0.34-0.79, Pinteraction = 0.015). CONCLUSIONS: The findings suggested that reproductive tract infections may be protective for the initiation and development of breast cancer, particularly for women with a longer interval of lifetime estrogen exposure.

An Unambiguous Synchronization Scheme for GNSS BOC Signals Based on Reconstructed Correlation Function.

Binary offset carrier (BOC) modulation is a new modulation method that has been gradually applied to the Global Satellite Navigation System (GNSS) in recent years. However, due to the multi-peaks in its auto-correlation function (ACF), it will incur a false lock and generate synchronization ambiguous potentially. In this paper, an unambiguous synchronization method based on a reconstructed correlation function is proposed to solve the ambiguity problem. First, through the shape code vector constructed in this paper, the general cross-correlation function (CCF) expression of the BOC modulated signal will be obtained. Based on the features of the signal correlation function, it is decomposed into a matrix form of trigonometric functions. Then, it generates two local signal waves using a specific method, then the proposed method is implemented to obtain a no-side-peak correlation function by reconstructing the cross-correlation between the received signal and the two local signals. Simulations showed that it fully eliminates the side-peak threat and significantly removes the ambiguity during the synchronization of the BOC signals. This paper also gives the improved structure of acquisition and tracking. The detailed theoretical deduction of detection probability and code tracking error is demonstrated, and the corresponding phase discrimination function is given. In terms of de-blurring ability and detection probability performance, the proposed method outperformed other conventional approaches. The tracking performance was superior to the comparison methods and the phase discrimination curve only had a zero-crossing, which successfully removed the false lock points. In addition, in multipath mitigation, it outperformed the ACF of the BOC signal, and performs as well as the autocorrelation side-peak cancellation technique (ASPeCT) for BOC(kn,n) signals.

A Smart Realtime Service to Broadcast the Precise Orbits of GPS Satellite and Its Performance on Precise Point Positioning.

At present, Global Position System (GPS) navigation ephemeris mainly broadcasts satellite orbits with meter-level precision for standard point positioning and precise relative positioning. With the rapid development of real-time precise point positioning (PPP), the receiver or smartphone has begun to demand more and more convenient, continuous, and reliable access to real-time services of precise orbits. Therefore, this study proposes a solution of utilizing the 18-parameter ephemeris to directly broadcast ultra-rapid precise predicted orbits with centimeter-level precision for real-time PPP. For the first time in GPS, the difference in the PPP results between the precise orbits and the calculated orbits broadcasted from the generated ephemeris parameters is supplied as follows: (1) During the validity period of 2 h, root mean square (RMS) of the relative distance offsets between the results of PPP with the precise orbits and the results of PPP the 18-parameter ephemeris is only 0.0098 m. (2) Within 15 min after the validity period of 2 h, RMS of the relative distance offsets between the results of PPP with the precise orbits and the results of PPP with the predicted orbits by 18-parameter ephemeris is only 0.0057 m. Consequently, the 18-parameter ephemeris is feasible and advisable to broadcast precise predicted orbits for real-time PPP applications. Compared with the classic precise orbits broadcast mode with the orbit corrections defined by the radio technical commission for maritime services standards 10403.2 (RTCM), the mode of broadcasting the precise orbits with the 18-parameter ephemeris achieved the following improvements in convenience, continuity, and reliability: (1) The calculation of satellite position is the same as that of the navigation ephemeris excluding the additional correction operations required to the RTCM; (2) the amount of broadcast parameters was reduced by 20 times; (3) the length of the validity period was expanded 120 times, where the longer valid period helped to overcome the orbit corrections loss caused by RTCM stream failures; and (4) within 15 min after the validity period, the predicted orbits with an accuracy of 2 cm could still be provided by the 18-parameter ephemeris, which can ensure the real-time services of precise orbits in the case of a 15 min communication interruption of the RTCM orbit correction data stream.

A Cycle Slip Repair Method Against Ionospheric Effects and Observational Noises for BDS Triple-Frequency Undifferenced Phases.

The cycle slip detection (CSD) and cycle slip repair (CSR) are easily affected by ionospheric delay and observational noise. Aiming at mitigating the above disadvantage, a new BeiDou navigation satellite system (BDS) triple-frequency CSR method (BTCSR) is proposed for the undifferenced phase. BTCSR learns from the classic triple-frequency CSR (CTCSR), with combinations of phases and pseudoranges in correcting ionospheric delay and optimizing observational noise. Different from CTCSR, though, BTCSR has made the following improvements: (1) An optimal model of calculating cycle slip combination is established, which further takes into account the minimization of the effect of residual ionospheric error after the correction. The calculation of cycle slip combination is obtained with the root mean squared errors (0.0646, 0.1261, 0.1069) of cycles, resulting in CSR success rate of 99.9927%, and the wavelengths (4.8842,3.5738,8.1403) of m. (2) A discriminant function is added to guarantee the CSR correctness. This function utilizes epoch-difference value of the ionosphere-free and geometry-free phase to select the correct cycle slip value, which eliminates the interference of large pseudorange errors in determining the final cycle slip. Consequently, the performances of BTCSR and CTCSR have been compared. For the real BDS pseudorange observation with additional 1.5 m errors, which can cover situations of 99.96% pseudorange noise, results of CTCSR show failure, but results of BTCSR keep correct. Moreover, BTCSR has made the following improvements relative to the geometry-free cycle slip detection method (GFCSD) and Melboune-Wubbena cycle slip combination detection method (MWCSD): (1) During a moderate magnetic storm of level 6, CSR testing, with the BDS monitoring station in a low latitude region, showed that some failures occur in GFCSD because of severe ionospheric variation, but BTCSR could correctly identify and fix cycle slips. (2) For the BDS observation data with an additional 1.5 m error on the actual pseudoranges, MWCSD exhibited failures, but the repair results of BTCSR were correct and reliable. (3) For the special slips of (0,59,62) cycles, and equal slips of (1,1,1) cycles on (B1,B2,B3), that are hard to detect by GFCSD and MWCSD, respectively, BTCSR could repair these correctly. Finally, BTCSR obtains reliable repair results under large pseudorange errors and severe ionospheric variations, and the cut-off elevation larger than 10 degrees is the suggested background.

An Indoor DFEC Ranging Method for Homologous Base Station Based on GPS L1 and BeiDou B1 Signals.

High-precision navigation and positioning technology for indoor areas has become one of the research hotspots in the current navigation field. However, due to the complexity of the indoor environment, this technology direction is also one of the research difficulties. At present, our common indoor positioning methods are WIFI, Bluetooth, LED, ultrasound and pseudo satellite. However, due to the problem of inaccurate direct or indirect ranging, the positioning accuracy is usually affected, which makes the final application difficult to achieve. In order to avoid the ranging limitations of the existing methods, a new dual-frequency entanglement constraint (DFEC) ranging method based on homologous base station is proposed in this paper. The relationship between the homologous characteristics of dual-frequency signals and the phase relationship within the cycle is used to estimate the current carrier phase adjustment the true value of the cycle count is used to get rid of the constraints of the ranging conditions and improve the ranging accuracy. In order to verify the feasibility of this method, the wired environment test and the typical characteristic points of wireless environment are tested and analyzed respectively. The analysis results show that in the wired environment, the transmitting base station and the receiving terminal will introduce a ranging error of one wavelength; in the wireless environment, due to the influence of spatial noise and multipath, the error of the estimation of the whole cycles of the ranging value increases significantly. And this phenomenon is most obvious especially in the region where the signal is shaded, but the error estimate that satisfies ± 1 wavelength still accounts for 90%. Based on this, we conduct multiple observation data collection at five typical feature points, and used existing MATLAB positioning algorithms to conduct positioning error tests. The analysis found that under this error condition, the positioning accuracy was about 0.6 m, and 93% of the points met the 1-m positioning accuracy.

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning.

In view of the inability of Global Navigation Satellite System (GNSS) to provide accurate indoor positioning services and the growing demand for location-based services, indoor positioning has become one of the most attractive research areas. Moreover, with the improvement of the smartphone hardware level, the rapid development of deep learning applications on mobile terminals has been promoted. Therefore, this paper borrows relevant ideas to transform indoor positioning problems into problems that can be solved by artificial intelligence algorithms. First, this article reviews the current mainstream pedestrian dead reckoning (PDR) optimization and improvement methods, and based on this, uses the micro-electromechanical systems (MEMS) sensor on a smartphone to achieve better step detection, stride length estimation, and heading estimation modules. In the real environment, an indoor continuous positioning system based on a smartphone is implemented. Then, in order to solve the problem that the PDR algorithm has accumulated errors for a long time, a calibration method is proposed without the need to deploy any additional equipment. An indoor turning point feature detection model based on deep neural network is designed, and the accuracy of turning point detection is 98%. Then, the particle filter algorithm is used to fuse the detected turning point and the PDR positioning result, thereby realizing lightweight cumulative error calibration. In two different experimental environments, the performance of the proposed algorithm and the commonly used localization algorithm are compared through a large number of experiments. In a small-scale indoor office environment, the average positioning accuracy of the algorithm is 0.14 m, and the error less than 1 m is 100%. In a large-scale conference hall environment, the average positioning accuracy of the algorithm is 1.29 m, and 65% of the positioning errors are less than 1.50 m which verifies the effectiveness of the proposed algorithm. The simple and lightweight indoor positioning design scheme proposed in this article is not only easy to popularize, but also provides new ideas for subsequent scientific research in the field of indoor positioning.

Research on Remote GPS Common-View Precise Time Transfer Based on Different Ionosphere Disturbances.

Propagation path delays are a major error for the remote precise time transfer of common view; these path delays contain the ionosphere and troposphere impact, while the contributions of the ionosphere and the troposphere from common-view satellites to receivers on the ground tend to become uncorrelated when the distance between these receivers increases. In order to select the appropriate ionospheric correction method for common view under different distances between receivers, a detailed test using multi-source data under different ionosphere disturbances are carried out in this paper. Here, we choose three different ionosphere disturbance methods and analyze the advantages and disadvantages of these methods for common-view time transfer and time comparison. At last, we put forward a suitable ionospheric correction method for different distances common view. The RMS shows that the method proposed for 3000 km remote common view can achieve 2.5 ns.

RTK/Pseudolite/LAHDE/IMU-PDR Integrated Pedestrian Navigation System for Urban and Indoor Environments.

This paper presents an evaluation of real-time kinematic (RTK)/Pseudolite/landmarks assistance heuristic drift elimination (LAHDE)/inertial measurement unit-based personal dead reckoning systems (IMU-PDR) integrated pedestrian navigation system for urban and indoor environments. Real-time kinematic (RTK) technique is widely used for high-precision positioning and can provide periodic correction to inertial measurement unit (IMU)-based personal dead reckoning systems (PDR) outdoors. However, indoors, where global positioning system (GPS) signals are not available, RTK fails to achieve high-precision positioning. Pseudolite can provide satellite-like navigation signals for user receivers to achieve positioning in indoor environments. However, there are some problems in pseudolite positioning field, such as complex multipath effect in indoor environments and integer ambiguity of carrier phase. In order to avoid the limitation of these factors, a local search method based on carrier phase difference with the assistance of IMU-PDR is proposed in this paper, which can achieve higher positioning accuracy. Besides, heuristic drift elimination algorithm with the assistance of manmade landmarks (LAHDE) is introduced to eliminate the accumulated error in headings derived by IMU-PDR in indoor corridors. An algorithm verification system was developed to carry out real experiments in a cooperation scene. Results show that, although the proposed pedestrian navigation system has to use human behavior to switch the positioning algorithm according to different scenarios, it is still effective in controlling the IMU-PDR drift error in multiscenarios including outdoor, indoor corridor, and indoor room for different people.

Enhanced Heuristic Drift Elimination with Adaptive Zero-Velocity Detection and Heading Correction Algorithms for Pedestrian Navigation.

As pedestrian dead-reckoning (PDR), based on foot-mounted inertial sensors, suffers from accumulated error in velocity and heading, an improved heuristic drift elimination (iHDE) with a zero-velocity update (ZUPT) algorithm was proposed for simultaneously reducing the error in heading and velocity in complex paths, i.e., with pathways oriented at 45°, curved corridors, and wide areas. However, the iHDE algorithm does not consider the changes in pedestrian movement modes, and it can deteriorate when a pedestrian walks along a straight path without a pre-defined dominant direction. To solve these two problems, we propose enhanced heuristic drift elimination (eHDE) with an adaptive zero-velocity update (AZUPT) algorithm and novel heading correction algorithm. The relationships between the magnitude peaks of the y-axis angular rate and the detection thresholds were established only using the readings of the three-axis accelerometer and the three-axis gyroscopic, and a mechanism for constructing temporary dominant directions in real time was introduced. Real experiments were performed and the results showed that the proposed algorithm can improve the still-phase detection accuracy of a pedestrian at different movement motions and outperforms the iHDE algorithm in complex paths with many straight features.

Adaptive Spoofing Suppression Algorithm for GNSS Based on Multiple Antennas Array.

The signals of navigation satellites are easily affected by spoofing interference, causing the wrong position, speed or Universal Time Coordinate of the receiver to be calculated. Traditional detection and suppression algorithms are used only to eliminate the spoofing signals, which may lead to an insufficient number of satellites for positioning. An adaptive spoofing suppression algorithm (ASSA) based on a multiple antenna array is proposed in this study. The ASSA can use the cross-correlation gain of multiple antenna array to adaptively generate nulling and realize the simultaneous suppression of multiple spoofing signals. Moreover, ASSA does not need to capture and track spoofing separately, thus reducing the complexity of implementation and calculation. Experiments were conducted to verify the proposed system under different conditions, and the results show that ASSA can suppress multiple spoofings with little impact on positioning performance. Under the condition of spoofing, ASSAs were (2.22 m, 2.41 m, 4.43 m) in the static test and (2.27 m, 2.43 m, 4.64 m) in the kinematic test, which are good positioning performances for both. In addition, the ASSA is applied before capturing signals, which is beneficial to identifying and eliminating spoofing earlier and faster.

Precise Point Positioning Algorithm for Pseudolite Combined with GNSS in a Constrained Observation Environment.

In urban canyon environments, Global Navigation Satellite System (GNSS) satellites are heavily obstructed with frequent rise and fall and severe multi-path errors induced by signal reflection, making it difficult to acquire precise, continuous, and reliable positioning information. To meet imperative demands for high-precision positioning of public users in complex environments, like urban canyons, and to solve the problems for GNSS/pseudolite positioning under these circumstances, the Global Navigation Satellite System (GNSS) Precision Point Positioning (PPP) algorithm combined with a pseudolite (PLS) was introduced. The former problems with the pseudolite PPP technique with distributed pseudo-satellites, which relies heavily on known points for initiation and prerequisite for previous high-precision time synchronization, were solved by means of a real-time equivalent clock error estimation algorithm, ambiguity fixing, and validation method. Experiments based on a low-cost receiver were performed, and the results show that in a weak obstructed environment with low-density building where the number of GNSS satellites was greater than seven, the accuracy of pseudolite/GNSS PPP with fixed ambiguity was better than 0.15 m; when there were less than four GNSS satellites in severely obstructed circumstances, it was impossible to obtain position by GNSS alone, but with the support of a pseudolite, the accuracy of PPP was able to be better than 0.3 m. Even without GNSS, the accuracy of PPP could be better than 0.5 m with only four pseudolites. The pseudolite/GNSS PPP algorithm presented in this paper can effectively improve availability with less GNSS or even without GNSS in constrained environments, like urban canyons in cities.

Unambiguous Acquisition/Tracking Technique Based on Sub-Correlation Functions for GNSS Sine-BOC Signals.

The autocorrelation function (ACF) of the Binary Offset Carrier modulation (BOC) signal for Global Navigation Satellite System (GNSS) has multiple peaks, ambiguity is easily generated during the synchronization of the baseband signal. Some methods have been proposed to remove the ambiguity, but the performance is not suitable for high-order BOC signals or does not maintain narrow correlation characteristics. This paper proposes a sub-function reconstruction synchronization algorithm to solve this problem, of which the key is to design a new local auxiliary code: the local Pseudo-Random Noise (PRN) code is divided into several new codes with different delays. The auxiliary code performs a coherent integration operation with the received signal. Then, a correlation function without any positive side peaks is obtained by multiplying the two correlation results to make the acquisition/tracking completely unambiguous. The paper gives a design scheme of navigation signal acquisition/tracking and deduces the theoretical analysis of detection performance. The phase discrimination function is provided. The performance of the method is analyzed from both theoretical and simulation aspects. Compared with the Binary phase shift keying-like (BPSK-LIKE) method, Subcarrier Phase Cancellation (SCPC) method and the Autocorrelation Side-Peak Cancellation Technique (ASPeCT) method, the proposed method has the best detection probability for the acquisition, which is 0.5 dB-Hz better than ASPeCT. For tracking, the proposed method performs best in terms of phase-detection curve, anti-multipath performance, and anti-noise performance. For high-order BOC signals, the SRSA technique successfully removes the false lock points, and there is only one multipath error envelope, and the code tracking error is almost the same as the ASPeCT method.

A New DGNSS Positioning Infrastructure for Android Smartphones.

One's position has become an important piece of information for our everyday lives in a smart city. Currently, a position can be obtained easily using smartphones that is equipped with low-cost Global Navigation Satellite System (GNSS) chipsets with accuracy varying from 5 m to 10 m. Differential GNSS (DGNSS) is an efficient technology that removes the majority of GNSS errors with the aid of reference stations installed at known locations. The sub-meter accuracy can be achieved when applying the DGNSS technology on the advanced receivers. In 2016, Android has opened the accesses of raw GNSS measurements to developers. However, most of the mid and low-end smartphones only provide the data using the National Marine Electronics Association (NMEA) protocol. They do not provide the raw measurements, and thus do not support the DGNSS operation either. We proposed a DGNSS infrastructure that correct the standalone GNSS position of smartphones using the corrections from the reference station. In the infrastructure, the position correction is generated considering the GNSS satellite IDs that contribute to the standalone solution in smartphones, and the position obtained is equivalent to the solution of using the range-domain correction directly. To serve a large number of smartphone users, a Client/Server architecture is developed to cope with a mass of DGNSS positioning requests efficiently. The comparison of the proposed infrastructure against the ground truth, for all field tests in open areas, showed that the infrastructure achieves the horizontal positioning accuracy better than 2 m. The improvement in accuracy can reach more than 50% for the test in the afternoon. The infrastructure brings benefits to applications that require more accuracy without requiring any hardware modifications.

Doppler Differential Positioning Technology Using the BDS/GPS Indoor Array Pseudolite System.

A Global Satellite Navigation System (GNSS) cannot provide normal location services in an indoor environment because the signals are blocked by buildings. The Beidou satellite navigation system (BDS)/GPS indoor array pseudolite system is proposed to overcome the problems of indoor positioning with conventional pseudolite, such as time synchronization, ambiguity resolution and base stations. At the same time, an algorithm for Doppler differential positioning is proposed to improve the indoor positioning accuracy and the positioning coverage of the system, which uses the Doppler difference equation and Known Point Initialization (KPI) to determinate the velocity and position of the receiver. Experiments were conducted to verify the proposed system under different conditions; the average positioning error of the Doppler differential positioning algorithm was 7.86 mm in the kinematic test and 2.9 mm in the static test. The results show that BDS/GPS indoor array pseudolite system has the potential to make indoor positioning achieve sub-centimeter precision. Finally, the positioning error of the proposed algorithm is also analyzed, and the data tests show that the dilution of precision (DOP) and cycle- slips have a significant impact on the indoor positioning accuracy; a cycle-slip of a half-wavelength can cause positioning errors of tens of millimeters. Therefore, the Doppler-aided cycle-slip detection method (DACS) is proposed to detect cycle-slips of one cycle or greater than one, and the carrier phase double difference cycle-slip detection method (CPDD) is used to detect cycle slips of a half-wavelength.

An Innovative Fingerprint Location Algorithm for Indoor Positioning Based on Array Pseudolite.

Since the signals of the global navigation satellite system (GNSS) are blocked by buildings, accurate positioning cannot be achieved in an indoor environment. Pseudolite can simulate similar outdoor satellite signals and can be used as a stable and reliable positioning signal source in indoor environments. Therefore, it has been proposed as a good substitute and has become a research hotspot in the field of indoor positioning. There are still some problems in the pseudolite positioning field, such as: Integer ambiguity of carrier phase, initial position determination, and low signal coverage. To avoid the limitation of these factors, an indoor positioning system based on fingerprint database matching of homologous array pseudolite is proposed in this paper, which can achieve higher positioning accuracy. The realization of this positioning system mainly includes the offline phase and the online phase. In the offline phase, the carrier phase data in the indoor environment is first collected, and a fingerprint database is established. Then a variational auto-encoding (VAE) network with location information is used to learn the probability distribution characteristics of the carrier phase difference of pseudolite in the latent space to realize feature clustering. Finally, the deep neural network is constructed by using the hidden features learned to further study the mapping relationship between different carrier phases of pseudolite and different indoor locations. In the online phase, the trained model and real-time carrier phases of pseudolite are used to predict the location of the positioning terminal. In this paper, by a large number of experiments, the performance of the pseudolite positioning system is evaluated under dynamic and static conditions. The effectiveness of the algorithm is evaluated by the comparison experiments, the experimental results show that the average positioning accuracy of the positioning system in a real indoor scene is 0.39 m, and the 95% positioning error is less than 0.85 m, which outperforms the traditional fingerprint positioning algorithms.

An Improved WiFi Positioning Method Based on Fingerprint Clustering and Signal Weighted Euclidean Distance.

WiFi fingerprint positioning has been widely used in the indoor positioning field. The weighed K-nearest neighbor (WKNN) algorithm is one of the most widely used deterministic algorithms. The traditional WKNN algorithm uses Euclidean distance or Manhattan distance between the received signal strengths (RSS) as the distance measure to judge the physical distance between points. However, the relationship between the RSS and the physical distance is nonlinear, using the traditional Euclidean distance or Manhattan distance to measure the physical distance will lead to errors in positioning. In addition, the traditional RSS-based clustering algorithm only takes the signal distance between the RSS as the clustering criterion without considering the position distribution of reference points (RPs). Therefore, to improve the positioning accuracy, we propose an improved WiFi positioning method based on fingerprint clustering and signal weighted Euclidean distance (SWED). The proposed algorithm is tested by experiments conducted in two experimental fields. The results indicate that compared with the traditional methods, the proposed position label-assisted (PL-assisted) clustering result can reflect the position distribution of RPs and the proposed SWED-based WKNN (SWED-WKNN) algorithm can significantly improve the positioning accuracy.