GPS Signal Research Articles

An Improved GPS/INS Integration Based on EKF and AI During GPS Outages

Inertial navigation system (INS) is often integrated with satellite navigation systems to achieve the required precision at high-speed applications. In global navigation system (GPS)/INS integration systems, GPS outages are unavoidable and a severe challenge. Moreover, because of the usage of low-cost microelectromechanical sensors (MEMS) with noisy outputs, the INS will get diverged during GPS outages, and that is why navigation precision severely decreases in commercial applications. In this paper, we improve GPS/INS integration system during GPS outages using extended Kalman filter (EKF) and artificial intelligence (AI) together. In this integration algorithm, the AI receives the angular rates and specific forces from the inertial measurement unit (IMU) and velocity from the INS at [Formula: see text] and [Formula: see text]. Therefore, the AI has positioning and timing data of the INS. While the GPS signals are available, the output of the AI is compared with the GPS increment; so that the AI is trained. During GPS outages, the AI will practically play the GPS role. Thus, it can prevent the divergence of the GPS/INS integration system in GPS-denied environments. Furthermore, we utilize neural networks (NNs) as an AI module in five different types: multi-layer perceptron (MLP) NN, radial basis function (RBF) NN, wavelet NN, support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed approach, we utilize a real dataset that has been gathered by a mini-airplane. The results demonstrate that the proposed approach outperforms the INS and GPS/INS integration systems with the EKF during GPS outages. Meanwhile, the ANFIS also reached more than 47.77% precision compared to the traditional method.

A Real-Time Semantic Segmentation Method Based on STDC-CT for Recognizing UAV Emergency Landing Zones.

With the accelerated growth of the UAV industry, researchers are paying close attention to the flight safety of UAVs. When a UAV loses its GPS signal or encounters unusual conditions, it must perform an emergency landing. Therefore, real-time recognition of emergency landing zones on the ground is an important research topic. This paper employs a semantic segmentation approach for recognizing emergency landing zones. First, we created a dataset of UAV aerial images, denoted as UAV-City. A total of 600 UAV aerial images were densely annotated with 12 semantic categories. Given the complex backgrounds, diverse categories, and small UAV aerial image targets, we propose the STDC-CT real-time semantic segmentation network for UAV recognition of emergency landing zones. The STDC-CT network is composed of three branches: detail guidance, small object attention extractor, and multi-scale contextual information. The fusion of detailed and contextual information branches is guided by small object attention. We conducted extensive experiments on the UAV-City, Cityscapes, and UAVid datasets to demonstrate that the STDC-CT method is superior for attaining a balance between segmentation accuracy and inference speed. Our method improves the segmentation accuracy of small objects and achieves 76.5% mIoU on the Cityscapes test set at 122.6 FPS, 68.4% mIoU on the UAVid test set, and 67.3% mIoU on the UAV-City dataset at 196.8 FPS on an NVIDIA RTX 2080Ti GPU. Finally, we deployed the STDC-CT model on Jetson TX2 for testing in a real-world environment, attaining real-time semantic segmentation with an average inference speed of 58.32 ms per image.

Key environmental factors affecting perceptions of security of night-time walking in neighbourhood streets: A discussion based on fear heat maps

BackgroundWalking plays an important role in residents’ health. After experiencing rapid development, most cities in China have gradually entered the process of urban renewal. Therefore, improving the quality of neighbourhood streets in the old city has become an important issue. The sense of security at night has become an important part of street reconstruction. Therefore, finding out the key environmental factors that affect the security of night-time walking in the streets of the old city has become an important prerequisite for street reconstruction. MethodsFirstly, according to the theoretical explanation of security in the existing literature, the environmental variables that may affect the security of night-time walking are determined. Besides, we chose the typical neighbourhood streets in the old town that need to be improved urgently, and recruited 32 subjects in an on-site experiment to record. Through wearable devices, the galvanic skin response, (GPS), and panoramic video signals generated by the subject during the experiment. The high arousal environment nodes of the subjects were determined by Galvanic skin response GSR signal and GPS signal. According to the semi-structured interview of the subjects, we valenced these high-arousal environment nodes and established a map of each subject’s fear emotional environment nodes. Aggregated into a street walking fear map by kernel density estimation. Finally, we selected the environment nodes with different degrees of fear on the map, established the optimal regression model of night-time walking security through the optimal subset regression method, and finally identified the critical environmental factors. Results and conclusionThe key environmental influences are street transparency, illegal parking level, environmental cleanliness level, light-transmittance intensity form building, CCTV density, and crowd activity intensity. The above key factors suggest that theEyes on the Street (EoS) theory seems to occupy a more important position in the relevant theories of street night-time security research. It also suggests that the privacy of subjects needs to be considered when improving the security of the street at night.

A GPS-Adaptive Spoofing Detection Method for the Small UAV Cluster

The small UAV (unmanned aerial vehicle) cluster has become an important trend in the development of UAVs because it has the advantages of being unmanned, having a small size and low cost, and ability to complete many collaborative tasks. Meanwhile, the problem of GPS spoofing attacks faced by submachines has become an urgent security problem for the UAV cluster. In this paper, a GPS-adaptive spoofing detection (ASD) method based on UAV cluster cooperative positioning is proposed to solve the above problem. The specific technical scheme mainly includes two detection mechanisms: the GPS spoofing signal detection (SSD) mechanism based on cluster cooperative positioning and the relative security machine optimal marking (RSOM) mechanism. The SSD mechanism starts when the cluster enters the task state, and it can detect all threats to the cluster caused by one GPS signal spoofing source in the task environment; when the function range of the mechanism is exceeded, that is, there is more than one spoofing source and more than one UAV is attacked by different spoofing sources, the RSOM mechanism is triggered. The ASD algorithm proposed in this work can detect spoofing in a variety of complex GPS spoofing threat environments and is able to ensure the cluster formation and task completion. Moreover, it has the advantages of a lightweight calculation level, strong applicability, and high real-time performance.

A Simultaneous Localization and Mapping System Using the Iterative Error State Kalman Filter Judgment Algorithm for Global Navigation Satellite System

Outdoor autonomous mobile robots heavily rely on GPS data for localization. However, GPS data can be erroneous and signals can be interrupted in highly urbanized areas or areas with incomplete satellite coverage, leading to localization deviations. In this paper, we propose a SLAM (Simultaneous Localization and Mapping) system that combines the IESKF (Iterated Extended Kalman Filter) and a factor graph to address these issues. We perform IESKF filtering on LiDAR and inertial measurement unit (IMU) data at the front-end to achieve a more accurate estimation of local pose and incorporate the resulting laser inertial odometry into the back-end factor graph. Furthermore, we introduce a GPS signal filtering method based on GPS state and confidence to ensure that abnormal GPS data is not used in the back-end processing. In the back-end factor graph, we incorporate loop closure factors, IMU preintegration factors, and processed GPS factors. We conducted comparative experiments using the publicly available KITTI dataset and our own experimental platform to compare the proposed SLAM system with two commonly used SLAM systems: the filter-based SLAM system (FAST-LIO) and the graph optimization-based SLAM system (LIO-SAM). The experimental results demonstrate that the proposed SLAM system outperforms the other systems in terms of localization accuracy, especially in cases of GPS signal interruption.

Evaluation of L-band GPS signal attenuation to multiple vegetations using ground-based measurements

ABSTRACT Global Navigation Satellite System Reflectometry (GNSS-R) is a remote sensing technique that can be regarded as a bistatic radar system. GNSS-R uses GNSS signals as signal sources and obtains the Earth’s surface environmental parameters, such as soil moisture (SM), by receiving the L-band microwave signal reflected from the Earth’s surface. However, surface vegetation could be one of the main factors influencing the accuracy of GNSS-R land applications since plants, including branches and leaves, attenuate GNSS signals. Additionally, the evaluation of signal attenuation caused by the plant canopy is quite difficult. In this paper, we study the attenuations of received L1- and L2-band GPS signals to the vegetation leaf area index (LAI) for different types of plants. The relationship between the attenuation of the GPS signal-to-noise ratio (SNR) (both above and below the canopy) and the LAI is established through field experiments. The results show that the mean SNR received in the L2 band is lower than that in the L1 band for each satellite but with a larger standard deviation (SD). The sensitivities of L1- and L2-band signals to the LAI are revealed, revealing greater sensitivity and a relatively good Pearson correlation coefficient (R) for lower elevation angles and vegetation biomass. In addition, the sensitivity and R of L2-band signals to the LAI are lower than those of L1-band signals. This study is significantly valuable for improving the quantitative representation of error estimates for GNSS-R SM retrieval. The established model can be employed in GNSS land applications and aid in solving signal surface-scattering problems in which accurate signal estimates are important.

Fiducial Markers Aided Position Estimation for Vertical Landing

Autonomous landing is a critical step in unmanned aerial vehicles (UAVs) and requires accurate position information. In cases where GPS signals are unavailable or obstructed, vision-based approaches can provide support for landing capabilities. In this study, a vision-based position estimation algorithm is being developed in conjunction with markers used in vertical take-off and landing (VTOL) systems of UAVs. The developed framework is designed to be compatible with various types of visual markers. The Kalman Filter is used to the calculated position to correct measurement errors and reduce the uncertainty of the position estimation. The developed algorithm is extensively tested in a simulation environment. The positions of a quadrotor aircraft are compared with real measurements to analyze the performance of the proposed vision-based position estimation algorithm. The results demonstrate an acceptable level of accuracy for the algorithm. This study discusses the potential of using visual markers and integrating Kalman filtering to improve the accuracy of positioning in vertical takeoff and landing systems of the UAVs. The development of a vision-based position estimation algorithm can enhance the reliability and precision of autonomous landing capabilities and enable successful landings in situations where GPS signals are limited or unavailable.

Multipath Impact on Multi-Frequency and Multi-Constellation Code Measurements from Brazilian GNSS Stations

Although Global Navigation Satellite Systems (GNSS) positioning can achieve high accuracy in line-of-sight conditions, multipath remains a dominant source of error. Multipath occurs when the reflected signals reach the receiver in addition to the direct ones. Different systems design can impact the multipath effect, such as the signal modulation and chipping rate. In the context of multifrequency and multi-constellation scenario that we are achieving with four operational global constellations, this paper compares the multipath impact in different signals of GPS, GLONASS, Galileo and BeiDou. For the experiment, one week of data from 35 Brazilian Network for Continuous Monitoring of the GNSS Systems (RBMC) stations in June 2021 was processed. The multipath index was estimated based on the code- inus-carrier combination. For the first frequency of each system, similar results were obtained for GPS, GLONASS and BeiDou (approximately 56 cm). The Galileo BOC/AltBOC modulation offers better resistance to multipath, with a multipath index of 37 cm for E1 and of 12 cm for E5. Considering all selected stations, the multipath index of GPS L1 varied from 38 cm to 61 cm.

Estimated Impacts Induced by The Magnetic Activity Index (Dst) on the Local Total Electronic Content (TEC) in Africa Equatorial Zone

The state of equatorial ionosphere has been studied from the determination of the Total Electron Content (TEC) based on the correction of errors propagation of GPS signals due to ionospheric disturbances using the dual frequency method. In our study, we considered tree stations of the equatorial area (adis, bjco and nklg) in January 2012. Firstly, the monthly survey of the TEC has revealed that the magnetic storm days coincide with the days that the TEC Rinex around 70 UCET. Secondly, the daily survey of the TEC reveals two TEC ( TEC/RINEX and the TEC/CODG) has revealed that during magnetics storms days, the interval between curves of TEC observed between 10: 00PM to 16:00PM (UT) is more important (around 10 UCET) when the Dst is strongly negative whatever the nature of the magnetic storm, and the interval between curves of TEC observed between 17:00 to 20:00PM (UT) is function of the nature of the magnetic storm.

Vehicle tracking system using working sensors of a partially functional android mobile phone

Vehicle tracking has found its applications in a variety of domains. Most of the currently available vehicles tracking systems are based upon using some expensive tracking units that employ only GPS signals to determine position and have very high monthly subscription charges from their service providers. To reduce the cost for such applications, this paper proposes the use of a partially damaged or low-cost android device with GSM capability and some other useful working sensors to periodically report the location and then display and monitor the determined location set on a remote digital map. This device can be integrated with a vehicle and made to boot up and start tracking automatically as soon as the vehicle’s ignition is turned on and stop tracking and shutdown automatically after the vehicle’s ignition is turned off. These devices may find application in fleet management, public transport systems, etc. as discussed inside this paper.

GPS signal acquisition based on deep convolutional neural network and post-correlation methods

GPS signal acquisition based on deep convolutional neural network and post-correlation methods

Continuous ground monitoring of vegetation optical depth and water content with GPS signals

Abstract. Satellite microwave remote sensing techniques can be used to monitor vegetation optical depth (VOD), a metric which is directly linked to vegetation biomass and water content. However, these large-scale measurements are still difficult to reference against either rare or not directly comparable field observations. So far, in situ estimates of canopy biomass or water status often rely on infrequent and time-consuming destructive samples, which are not necessarily representative of the canopy scale. Here, we present a simple technique based on Global Navigation Satellite Systems (GNSS) with the potential to bridge this persisting scale gap. Because GNSS microwave signals are attenuated and scattered by vegetation and liquid water, placing a GNSS sensor under a vegetated canopy and measuring changes in signal strength over time can provide continuous information about VOD and thus on vegetation biomass and water content. We test this technique at a forested site in southern California for a period of 8 months. We show that variations in GNSS signal-to-noise ratios reflect the overall distribution of biomass density in the canopy and can be monitored continuously. For the first time, we show that this technique can resolve diurnal variations in VOD and canopy water content at hourly to sub-hourly time steps. Using a model of canopy transmissivity to assess these diurnal signals, we find that temperature effects on the vegetation dielectric constant, and thus on VOD, may be non-negligible at the diurnal scale or during extreme events like heat waves. Sensitivity to rainfall and dew deposition events also suggests that canopy water interception can be monitored with this approach. The technique presented here has the potential to resolve two important knowledge gaps, namely the lack of ground truth observations for satellite-based VOD and the need for a reliable proxy to extrapolate isolated and labor-intensive in situ measurements of biomass, canopy water content, or leaf water potential. We provide recommendations for deploying such off-the-shelf and easy-to-use systems at existing ecohydrological monitoring networks such as FluxNet or SapfluxNet.

Robust Autonomous Vehicle Computer-Vision-Based Localization in Challenging Environmental Conditions

In this paper, we present a novel autonomous vehicle (AV) localization design and its implementation, which we recommend to employ in challenging navigation conditions with a poor quality of the satellite navigation system signals and computer vision images. In the case when the GPS signal becomes unstable, other auxiliary navigation systems, such as computer-vision-based positioning, are employed for more accurate localization and mapping. However, the quality of data obtained from AV’s sensors might be deteriorated by the extreme environmental conditions too, which infinitely leads to the decrease in navigation performance. To verify our computer-vision-based localization system design, we considered the Arctic region use case, which poses additional challenges for the AV’s navigation and might employ artificial visual landmarks for improving the localization quality, which we used for the computer vision training. We further enhanced our data by applying affine transformations to increase its diversity. We selected YOLOv4 image detection architecture for our system design, as it demonstrated the highest performance in our experiments. For the computational platform, we employed a Nvidia Jetson AGX Xavier device, as it is well known and widely used in robotic and AV computer vision, as well as deep learning applications. Our empirical study showed that the proposed computer vision system that was further trained on the dataset enhanced by affine transformations became robust regarding image quality degradation caused by extreme environmental conditions. It was effectively able to detect and recognize images of artificial visual landmarks captured in the extreme Arctic region’s conditions. The developed system can be integrated into vehicle navigation facilities to improve their effectiveness and efficiency and to prevent possible navigation performance deterioration.

PILOT: High-Precision Indoor Localization for Autonomous Drones

In many scenarios, unmanned aerial vehicles (UAVs), aka drones, need to have the capability of autonomous flight to carry out their missions successfully. In order to fly autonomously, drones need to know their location constantly. Then, based on their current position and the final destination, navigation commands will be generated and drones will be guided to their destination. Localization can be easily carried out in outdoor environments using GPS signals and drone inertial measurement units (IMUs). However, such an approach is not feasible in indoor environments or GPS-denied areas. In this paper, we propose a localization scheme for drones called PILOT (High-Precision Indoor Localization for Autonomous Drones), which is specifically designed for indoor environments. PILOT relies on ultrasonic acoustic signals to estimate the target drone's location. To obtain a precise final estimation of the drone's location, PILOT deploys a three-stage localization scheme. The first two stages provide robustness against the multi-path fading effect of indoor environments and mitigate the ranging error. Then, in the third stage, PILOT deploys a simple yet effective technique to reduce the localization error induced by the relative geometry between transmitters and receivers, which significantly reduces the height estimation error. The performance of PILOT was assessed in different scenarios, and the results indicate that it achieves centimeter-level accuracy for three-dimensional drone localization.

Validation of Alogo Move Pro: A GPS-Based Inertial Measurement Unit for the Objective Examination of Gait and Jumping in Horses.

Quantitative information on how well a horse clears a jump has great potential to support the rider in improving the horse's jumping performance. This study investigated the validation of a GPS-based inertial measurement unit, namely Alogo Move Pro, compared with a traditional optical motion capture system. Accuracy and precision of the three jumping characteristics of maximum height (Zmax), stride/jump length (lhorz), and mean horizontal speed (vhorz) were compared. Eleven horse-rider pairs repeated two identical jumps (an upright and an oxer fence) several times (n = 6 to 10) at different heights in a 20 × 60 m tent arena. The ground was a fiber sand surface. The 24 OMC (Oqus 7+, Qualisys) cameras were rigged on aluminum rails suspended 3 m above the ground. The Alogo sensor was placed in a pocket on the protective plate of the saddle girth. Reflective markers placed on and around the Alogo sensor were used to define a rigid body for kinematic analysis. The Alogo sensor data were collected and processed using the Alogo proprietary software; stride-matched OMC data were collected using Qualisys Track Manager and post-processed in Python. Residual analysis and Bland-Altman plots were performed in Python. The Alogo sensor provided measures with relative accuracy in the range of 10.5-20.7% for stride segments and 5.5-29.2% for jump segments. Regarding relative precision, we obtained values in the range of 6.3-14.5% for stride segments and 2.8-18.2% for jump segments. These accuracy differences were deemed good under field study conditions where GPS signal strength might have been suboptimal.

Data + pilot biases in modern GNSS signals

While traditional GNSS signals are always modulated with navigation data, various modern signals provide a distinct pilot channel without data modulation to support long coherent integration times. Intra-signal biases between the data and pilot components of such signals are evaluated for satellites of the GPS, Galileo, BeiDou-3, and QZSS constellations using measurements from a dedicated set of receivers. Peak values of about 2 ns are obtained for the GPS L5 signal, while slightly smaller values of up to 1 ns apply for the B1C and B2a signals of BeiDou-3 as well as the QZSS L1C signal. For Galileo E1 and E5a/b, data + pilot biases are confined to less than 0.1–0.3 ns, which is typically less than other pseudorange errors for these signals. Fully negligible values of < 0.05 ns are obtained for the L1C and L2C signals of GPS as well as the L2C signal of QZSS in accord with expectations for time-multiplexed or interlaced modulations. To support consistent processing of multi-GNSS data in heterogeneous networks, biases between combined data + pilot tracking and pilot-only tracking are derived in a dedicated zero-baseline receiver test bed. The analysis confirms the general understanding that biases between combined and pilot-only pseudoranges amount to a fixed fraction of the corresponding data + pilot biases. This fraction depends on the power sharing of the data and pilot component in the respective signals and amounts to 50% in most cases. The results of this study are expected to remove the prevailing problem of two distinct receiver groups in the generation of satellite clock and bias products by the International GNSS Service and to enable a rigorous and consistent use of these products by multi-GNSS users.

Analysis of GNSS Data for Earthquake Precursor Studies Using IONOLAB-TEC in the Himalayan Region

Earthquake precursors are the indicators that appear before an earthquake. The release of radon gas, ionospheric disturbances, anomalous animal behavior, and so on are examples of seismic and aseismic events. Ionospheric perturbations can be proved to be a reliable method in earthquake prediction. The GNSS data detect changes in the ionosphere through the time lag of the transmitted GPS signals recorded at the Earth-based receivers. A negative TEC anomaly is caused by the stress released from the rocks before the earthquake, which elevates positive ions or p-holes in the atmosphere and decreases the ions in the ionosphere. A positive TEC anomaly follows this because of the increase in ions in the ionosphere. The ionospheric disruption in the Himalayan region is examined before five random earthquakes. For this, data from 15 separate GNSS stations are investigated using IONOLAB-TEC. A promising total electron content (TEC) data estimate with a temporal resolution of 30 s was analyzed. The results of the TEC data analysis depict the anomaly a month before the five earthquakes, followed by the later perturbation in the earthquake preparation zone. TEC anomalies are enhanced more by the uniform spatial distribution of GNSS stations in the epicentral region than by randomly distributed stations. The results of IONOLAB-TEC and the widely used GPS-TEC software were compared. Owing to its temporal resolution, IONOLAB-TEC has edge over the GPS-TEC software in that it can identify even the slightest negative anomalies before an earthquake.

A Novel Approach for GNSS Signal Tracking Based on Sliding Mode Control

Designing a robust carrier tracking loop for GNSS receivers is a challenging task under strong scintillation, multipath, clock uncertainty, interference, and vehicle high dynamics. The conventional tracking methods such as proportional integral filter (PIF), Wiener filter (WF), and Kalman filter (KF) are known to perform well under given GNSS signal model parameters and noise characteristics. If it is not the case, then performance deteriorates and may result in loss of the carrier lock. To circumvent this problem, we propose a robust control and filtering method based on the well-known sliding mode approach. In this paper, a sliding mode control and state estimation framework is developed for GNSS carrier tracking. The efficacy of the proposed method is proved by simulation studies for GPS(L1) signal. From static to high dynamics operating cases are simulated for proving its usage on the high dynamics vehicles such as high speed trains, flighter jets, and missile systems. The achieved results are promising for use of the proposed method on these vehicles and the receiver operating in the adverse (weak GNSS signal) conditions.

The Superiority of Multi-GNSS L5/E5a/B2a Frequency Signals in Smartphones: Stochastic Modeling, Ambiguity Resolution, and RTK Positioning

The emerging Internet of Things (IoT) applications, such as intelligent transportation based on vehicular-lane accurate positioning, have a growing demand for precise and reliable positioning with Global Navigation Satellite Systems (GNSSs). It is desirable to use GNSS modules in smartphones to achieve highprecision positioning. The GNSS modules in some brands of smartphones thus far are able to track the new L5 signals of GPS and QZSS, E5a signals of Galileo and B2a signals of BeiDou-3. The L5/E5a/B2a signals have higher quality due to their signal structure, which provides an important potential for highprecision positioning in smartphones. In this paper, we will study the quality of L5/E5a/B2a signals, and their superiorities in integer ambiguity resolution (IAR) and precise positioning with respect to the L1/E1/B1 signals from GPS, QZSS, Galileo and Beidou-2/3 satellites. The signal quality is evaluated in terms of observation precision, multipath, double-differenced ambiguity fractions and ambiguity dilution of precision (ADOP). In addition, we propose a new weighting model that takes into account the variation range of carrier-to-noise density ratio (C/N0). The results indicate that the Beidou-3 B2a signal has comparable quality to L5/E5a signals of other systems, and all of them are better than the L1/E1/B1 signals. However, the ambiguity fractions of B2a signals diverge abruptly in some periods, resulting in the unsuccessful ambiguity fixing. The L5/E5a/B2a signals can generally obtain higher IAR fix-rate and positioning accuracies than the L1/E1/B1 signals. The new weighting model can capture the smartphone noise characteristics better than the traditional weighting model, thus improving the positioning accuracy.

An Autonomous Underwater Vehicle Simulation With Fuzzy Sensor Fusion for Pipeline Inspection

Underwater pipeline inspection is an important topic in off-shore subsea operations. Remotely operated vehicles (ROVs) can play an important role in multiple application areas including military, ocean science, aquaculture, shipping, and energy. However, using ROVs for inspection is not cost-effective, and the fixed leak detection sensors mounted along the pipeline have limited precision. Although the cost can be significantly reduced by applying autonomous underwater vehicles (AUVs), the unstable current, low visibility, and loss of GPS signal make the navigation of AUVs underwater very challenging. Previous studies have been conducted on coordinate-based, vision-based, and fusion-based navigation algorithms. However, the coordinate-based algorithms suffered from the denial of GPS signals while the vision-based methods typically relied on terrain and landscape knowledge that required collection prior to the mission. As a result of these issues, a navigation system for an AUV that incorporates vision and sonar sensors is presented in this article. In a robot operating system (ROS)/Gazebo-based simulation environment, the AUV had the ability to find and navigate toward the pipeline and continuously traverse along its length. Additionally, with a chemical concentration sensor mounted on the AUV, the system demonstrated the capability of inspecting the pipeline and reporting the leak point with a resolution of 3 m along the pipeline.