North Direction Research Articles

Inspection robot GPS outages localization based on error Kalman filter and deep learning

In urban environments, inspection robots face complex terrain and variable motion states, posing high demands on their positioning systems. Although the integration of Micro-Electro-Mechanical Systems Inertial Navigation Systems (MEMS-INS) with the Global Positioning System (GPS) provides continuous positioning information, high buildings and tunnels in cities can block GPS signals, leading to signal interruptions and increased positioning errors. During GPS outages, MEMS-INS gradually accumulates errors, severely affecting positioning accuracy. To address this issue, this paper proposes an adaptive error state Kalman Filter (AESKF), which employs an adaptive mechanism to eliminate the noise impact of MEMS-INS and reduce reliance on the process model. Additionally, a deep learning framework based on the Self-Attention mechanism of the Transformer and a custom loss function Long Short-Term Memory (LSTM) module is proposed to predict position increments of the inspection robot. Combining AESKF with Transformer-LSTM achieves optimized positioning accuracy of the inspection robot during GPS outages in dynamic urban environments. Simulation and practical experimental results demonstrate that the combination of AESKF and Transformer-LSTM significantly improves positioning accuracy. Compared to other mature methods, the Root Mean Square Error (RMSE) of positioning is reduced by up to 83.64 % in the north direction and 89.56 % in the east direction. When the GPS signal interruption lasts for 10 s and 60 s, the maximum position error standard deviation (STD) is 0.1186 m and 1.0417 m, respectively.

Remote marine precise point positioning with baseline length and troposphere-constrained models of the receivers for the oceanographic research vessel

ABSTRACT Research vessels are typically equipped with multiple receivers for positioning and attitude measurement; however, there is no data interaction or model fusion to implement the Precise Point Positioning (PPP) for these receivers. Therefore, a Constrained PPP (CPPP) is established by using the baseline length and tropospheric constraints of two receivers to improve the vessel’s remote marine positioning performance. Based on two integrated Real-Time Kinematic (RTK) receivers, marine experiments are conducted at a distance of 300–600 km from land. Compared to the ground environment, the Root Mean Square error (RMS) of the multipath and the STandard Deviation (STD) of the carrier-to-noise ratio in the marine environment are increased by 2.87 and 2.6 times, respectively. The length constraints reduce the RMS of positioning in the East, North, and Up (ENU) directions by about 0.053 (67%), 0.020 (34%), and 0.054 m (34%), respectively, and also rapidly recover positioning after interruptions, achieving positioning with errors in the ENU-directions of 10, 15, and 20 cm, respectively, within 3 minutes. When the baseline length and tropospheric constraints are combined, with or without the addition of the Zenith Wet Delay bias (dZWD), the average STD can be reduced by about 0.060 m (43%) and the accuracy of the up-positioning can be significantly improved. The correlation of dZWD to the up-positioning deviation of du=−2.161*dZWD is obtained by simulation. Since the acquisition of dZWD depends on the PPP accuracy, it is recommended that dZWD be set to compensate for unmodeled receiver errors when the STD of the up-positioning is superior to 0.06 m.

Surat’s sustainable future: analyzing compact urban development and population density trends

Rapid urbanization is a challenge for the cities of developing countries like India. The share of an urban area is rapidly increasing in dynamically growing cities. Due to this, the land use is altered, causing sprawl and unplanned development. The consumption of resources is greater, and the impact on the surroundings is negative. The compact city concept is always more efficient and cost effective compared to unplanned organic growth. The compact city optimizes available land resources with its maximum possible capacity in accordance with other available natural resources. The primary study objective is to assess the growth trend of Surat city using satellite images, geographic information system techniques, and census data for the last few years. The analysis considers population density for different city wards from 1991 to 2020. Based on the land use land cover change carried out using the maximum likelihood method from the satellite images, the rate of spreading out, its direction, and causes are also found. In this study, population density is a crucial attribute of the compact city concept. The study shows that wardwise density is increasing in a specific direction and making that portion of the city a dense, compact development. This is observed in ward numbers 48, 42, and 35 in the east, north, and southeast directions from Central Business District, respectively. The average population density in persons per sq. km is continuously increasing from 5006 to 20329 at city level which also shows the compact city development for Surat. The governing authorities must incorporate the compact city concept into the planning policy for sustainable urban development. By this, policymakers can understand housing needs to promote affordable housing, invest in infrastructure development and public services, and identify areas where sustainable transportation options, such as public transit, walking, or cycling, need improvement.

The structural elements controlling the subsurface mineralization as deduced from the integration of magnetic and self-potential methods, Wadi Khosh El-Daba area, southern Sinai, Egypt

Significant copper mineralization are observed to be associated, in most cases, with some radioactive anomalies related to increased uranium and thorium contents in Southern Sinai, Egypt. These mineralizations are related to intense post-magmatic, structurally controlled hydrothermal activities in many localities. Wadi Khosh El-Daba area is one of these locations which is mainly composed of monzogranites dissected by andesite dyke and pegmatite vein. This work deals with the application of ground magnetic and self-potential methods to establish the magnetic sources governing the mineralization, to interpret the significant trends that affected the study area, and to delineate the mineralized zones. The magnetic data were processed, enhanced and interpreted using advanced techniques, such as the first-order vertical derivative (FVD), the tilt derivative (TD), Euler deconvolution (ED), and source parameter image (SPI). The main structural lineaments/contacts that control the distribution of hydrothermal alterations and mineral distributions are oriented to the NW–SE, NE–SW, and NNW–SSE directions. The self-potential (SP) data show that there are numerous mineralized zones; most of them are distributed mainly along the structural lineaments deduced from the magnetic study. The quantitative interpretation revealed that the mineralized bodies are at shallow depths ranging from 5.6 m to 12.7 m, with half-width from 5 m to 13 m. These bodies are shallow to moderate dipping towards the south and north directions. Based on this study an anomaly map is originated to locate the preferred structural elements that may be controlling the mineralization in the area for further future exploration.

Application of IMU/GPS Integrated Navigation System Based on Adaptive Unscented Kalman Filter Algorithm in 3D Positioning of Forest Rescue Personnel.

Utilizing reliable and accurate positioning and navigation systems is crucial for saving the lives of rescue personnel and accelerating rescue operations. However, Global Navigation Satellite Systems (GNSSs), such as GPS, may not provide stable signals in dense forests. Therefore, integrating multiple sensors like GPS and Inertial Measurement Units (IMUs) becomes essential to enhance the availability and accuracy of positioning systems. To accurately estimate rescuers' positions, this paper employs the Adaptive Unscented Kalman Filter (AUKF) algorithm with measurement noise variance matrix adaptation, integrating IMU and GPS data alongside barometric altitude measurements for precise three-dimensional positioning in complex environments. The AUKF enhances estimation robustness through the adaptive adjustment of the measurement noise variance matrix, particularly excelling when GPS signals are interrupted. This study conducted tests on two-dimensional and three-dimensional road scenarios in forest environments, confirming that the AUKF-algorithm-based integrated navigation system outperforms the traditional Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Adaptive Extended Kalman Filter (AEKF) in emergency rescue applications. The tests further evaluated the system's navigation performance on rugged roads and during GPS signal interruptions. The results demonstrate that the system achieves higher positioning accuracy on rugged forest roads, notably reducing errors by 18.32% in the north direction, 8.51% in the up direction, and 3.85% in the east direction compared to the EKF. Furthermore, the system exhibits good adaptability during GPS signal interruptions, ensuring continuous and accurate personnel positioning during rescue operations.

Identification of proper species that can be used to monitor and decrease airborne Sb pollution.

Heavy metal concentrations, which have increased continuously in the environment this century due to anthropogenic factors, severely threaten human and environmental health. Antimony (Sb) is one of the most toxic and harmful heavy metals in terms of human and environmental health. Therefore, the priority research subjects are monitoring the alteration of Sb pollution in the airborne and reducing pollution. This study was conducted to conclude the proper species to monitor and drop airborne Sb contamination on trees grown in Düzce, the 5th most polluted European city. This study examined samples taken from Pseudotsuga menziesii, Cupressus arizonica, Pinus pinaster, Picea orientalis, and Cedrus atlantica, and the Sb concentration changes based on tree species, route, tissue, and age range in the last 40years were evaluated. The study hypothesizes that Sb concentration varies depending on (1) tree species, (2) direction, (3) plant tissue, and (4) age range, all confirmed in this study. In conclusion, the maximum concentrations were achieved in the outer bark and east (5.45µgg-1) and north directions (6.72µgg-1), with high traffic density. In addition, the mining and industrial places (sources of metal pollution) are not close to the study area. Therefore, it was concluded that traffic pollution was the primary source of Sb pollution in the study area. The study revealed that C. arizonica is the most suitable species for monitoring and reducing the change in Sb pollution because the highest Sb concentration (4.47µgg-1) in wood (the largest organ) was obtained in C. arizonica.

Multi-Source Data-Based Investigation of Spatiotemporal Heterogeneity and Driving Mechanisms of Coupling and Coordination in Human Settlements in Urban Agglomeration in the Middle Reaches of the Yangtze River

In the information age, the new wave of the information technology revolution has profoundly changed our mode of production and way of life. Pseudo human settlements (PHS), consisting of digits and information, have become increasingly important in human settlements (HS) systems, and become a strong support for the high-quality development of global HS. Against this background, clarifying the spatiotemporal heterogeneity and driving mechanisms of the coupling and coordination between the PHS and real human settlements (RHS) is of great significance to the high-quality development of HS and providing a reasonable explanation of today’s man–land relationship. Therefore, we developed a theoretical framework system for describing PHS–RHS coupling and coordination based on multi-source data such as internet socialization, public utility, and remote sensing images, etc. Taking the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR), which is the key region consolidating China’s “two horizontal and three vertical” urbanization strategy, as a case study area, we have comprehensively analyzed the spatiotemporal heterogeneity of the coupling and coordination of PHS and RHS and its driving mechanism in UAMRYR during the period of 2011–2021, by comprehensively applying the modified coupling coordination degree (CCD) and other models. The results show are as follows: (1) Temporal process—The CCD exhibited a reverse L-shaped increasing trend. The CCD class varied significantly, with the extremely uncoordinated and severely uncoordinated classes present at the beginning of the study period and disappearing toward the end of the study period, while the well coordinated and highly coordinated classes were absent at the beginning of the study period and appeared toward the end of the study period. (2) Spatial pattern—The CCD exhibited an equilateral triangle-shaped, core–margin spatial pattern and a characteristic of core polarization. Overall, the spatial distribution of the CCD exhibited a characteristic of “high in the central region, low in the eastern and western regions, and balanced in the south–north direction”. (3) Dynamic evolution—The CCD increased more rapidly in the north-eastern direction than in the south-western direction; the CCD exhibited north-eastward migration and dispersion, and the spatial variability decreased. (4) Driving mechanisms—The primary factors affecting the CCD varied significantly over time. The living system was dominant in the PHS, whereas the human system was dominant in the RHS. The PHS had a greater effect than the RHS on the CCD. The study broadens the research scope of human settlements geography, establishes a scientific foundation for advancing urban HS construction in the UAMRYR, and offers theoretical support for the high-quality development of cities in the UAMRYR.

GNSS/IMU/map-matching feedback integration with adaptive GNSS accuracy estimation by using low-quality sensors for vehicle localization in urban canyon

In urban canyons, the performance of vehicle navigation systems based on Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) is notably compromised due to satellite signal obstructions and severe multipath effects. This article proposes a GNSS/IMU/Map-Matching feedback (MMF) integration with the adaptive GNSS accuracy estimation to provide accurate and robust positions for vehicles in urban canyons. This system integrates self-constraints and MMF to control the drift of low- quality IMU. To avoid fatal positioning errors caused by incorrect MMF, motion states are categorized into straight-line driving and corner turning, with different criteria set based on their characteristics to validate MMF. Furthermore, GNSS accuracy is estimated adaptively with MMF. Extensive experiments in Hong Kong’s dense urban canyons demonstrated significantly improved positioning accuracy of 37% and 51% in the north and east directions, respectively, outperforming traditional GNSS/IMU/Map configurations. The results demonstrate the proposed approach’s marked enhancement in urban vehicular navigation.

Doppler Positioning with LEO Mega-Constellation: Equation Properties and Improved Algorithm

Doppler positioning, as an early form of positioning, has regained significant research interest in the context of Low Earth Orbit (LEO) satellites.Given the LEO mega-constellation scenario, the objective function of Doppler positioning manifests significant nonlinearity, leading to ill-conditioning challenges for prevalent algorithms like iterative least squares (LS) estimation, especially in cases where inappropriate initial values are selected. In this study, we investigate the causes of ill-posed problems from two perspectives. Firstly, we analyze the linearization errors of the Doppler observation equations in relation to satellite orbital altitude and initial value errors, revealing instances where traditional algorithms may fail to converge. Secondly, from an optimization theory perspective, we demonstrate the occurrence of convergence to locally non-unique solutions for Doppler positioning. Subsequently, to address these ill-conditioning issues, we introduce Tikhonov regularization terms in the objective function to constrain algorithm divergence, with a fitted model for the regularization coefficient. Finally, we conduct comprehensive simulation experiments in both dynamic and static scenarios to validate the performance of the proposed algorithm. On the one hand, when the initial values are set to 0, our algorithm achieves high-precision positioning, whereas the iterative LS fails to converge. On the other hand, in certain simulation scenarios, the iterative LS converges to locally non-unique solutions, resulting in positioning errors exceeding 50 km in the north and east directions, several hundred kilometers in the vertical direction, and velocity errors surpassing 120 m/s. In contrast, our algorithm demonstrates typical errors of a position error of 6.8462 m, velocity error of 0.0137 m/s, and clock drift error of 8.3746 × 10−6 s/s.This work provides an effective solution to the sensitivity issue of initial points in Doppler positioning and can serve as a reference for the algorithm design of Doppler positioning receivers with LEO mega-constellations.

Performance study of CPV/T systems with different tracking methods

Abstract This article built the model of concentrated photovoltaic and thermal (CPV/T) systems under the double-axis tracking mode in the east, west, south, and north directions, as well as the single-axis tracking mode in the north and south directions. According to this model, the function of the CPV/T system could be researched. The article established 3D models of large-scale CPV/T systems in different tracking modes and used this model to study the occlusion problem of CPV/T systems. Taking into account cosine loss, edge loss, and occlusion, this paper established energy reception models for different trace modes of the CPV/T system. The energy reception model calculated that the annual energy reception rate of the CPV/T system in the single-axis tracking mode was not a patch on the double-axis tracking CPV/T system. Subsequently, a scaled CPV/T total energy mathematical model was established under these two tracking mods in the article. Combined with the non-steady energy model, the annual total photoelectricity and photothermal output of the CPV/T systems in the single-axis tracking mode and the double-axis tracking mode with an area of 1600 m2 in Xi’an were calculated. The photoelectricity and photothermal outputs of the double-axis tracking mode were 169216 kW·h and 856183 kW·h, respectively. The single-axis tracking node had an annual output of 126898 kW·h for photoelectricity and 637490 kW·h for photothermal.

Mapping air quality variations in industrial and agricultural zones: Understanding spatial and temporal patterns of selected air pollutants

With the escalating threat of air pollution on the biota, the need for research on pollutants has become imperative in industrial, urbanized and heavily populated cities. This study aimed to determine the level of major outdoor atmospheric pollutants (PM10, PM2.5, TVOCs, and HCHO) in specific locations including industrial and agricultural zones. Field measurements for this research, spanning 52 locations, were collected in the spring season using a portable instrument, and spatial variation maps were developed using ArcGIS software. The study measured PM10 concentrations ranging from 31 μg/m3 to 267 μg/m3 and PM2.5 concentrations ranging from 27 μg/m3 to 253 μg/m3. PM2.5 levels at most sites exceeded USEPA, WHO, and PEQS standards, while PM10 levels generally met these standards. The highest values of coarse and fine particulate matter were observed in areas dominated by emissions from the textile industry. Furthermore, particulate matter concentrations showed spatial variations, increasing towards the North and East directions. The Total Volatile Organic Compounds (TVOCs) concentrations, ranging from 0 mg/m3 to 4.019 mg/m3, exceeded Hong Kong SAR standards at five locations, thereby elevating health risks for residents in these areas. Similarly, formaldehyde levels, ranging from 0 mg/m3 to 0.754 mg/m3, surpassed the Hong Kong SAR standard at seven locations, indicating a substantial threat of exposure for the local population. In general, air quality at most field sampling sites was registered as UNQUALIFIED but was also observed to be FRESH at a few locations. This research is significant for understanding the levels of major pollutants so that the concerned authorities can take appropriate measures to tackle air pollution by introducing pollution control technologies in industries, switching to renewable energy options, and managing road dust.

A damping method of grid strapdown inertial navigation system for polar region

Abstract The conventional north-pointing inertial navigation system (INS) always points to the geographic north. However, when the system crosses the pole in the polar region, the geographic north quickly changes with 180 degrees, and the rate of change of the geographic north direction will be infinite. Secondly, when the latitude approaches to 90 degrees, which will lead to singularities in math for the angular velocity caused by vehicle motion. With its special definition and mechanization, the grid coordinate system can solve the above problems. Nevertheless, the error analysis of the grid strapdown INS shows that there are Schuler periodic and Foucault periodic oscillations, which badly degrade the accuracy of the strap-down INS. To address the problem, the external horizontal damping technology is adopted in this paper, wherein the external velocity is provided by the Doppler velocity log (DVL). Finally, the feasibility and the effectiveness of the external damping technology were verified by simulation.

Dual Kalman Filter Based on a Single Direction under Colored Measurement Noise for INS-Based Integrated Human Localization

For inertial-based integrated pedestrian navigation, the navigation environment might affect the positioning accuracy in different directions. Meanwhile, complex filtering algorithms can reduce computational efficiency. Therefore, one dual Kalman filter (KF) based on a single direction under a colored measurement noise (CMN) scheme is developed herein to improve the robustness and operational efficiency. The proposed method involves designing a data fusion model for the KF that integrates data from an inertial navigation system (INS) and ultrawideband (UWB). Subsequently, the INS/UWB integrated model-based KF under CMN (cKF) will be derived. Then, two sub-cKFs are proposed to fuse the data in the east and north directions, respectively. The empirical findings highlight the superior performance of the proposed approach over the KF for position estimation accuracy and runtime reduction, demonstrating its effectiveness.

Distortions in Judged Spatial Relations in Large Language Models

We present a benchmark for assessing the capability of large language models (LLMs) to discern intercardinal directions between geographic locations and apply it to three prominent LLMs: GPT-3.5, GPT-4, and Llama-2. This benchmark specifically evaluates whether LLMs exhibit a hierarchical spatial bias similar to humans, where judgments about individual locations’ spatial relationships are influenced by the perceived relationships of the larger groups that contain them. To investigate this, we formulated fourteen questions focusing on well-known U.S. cities. Seven questions were designed to challenge the LLMs with scenarios potentially influenced by the orientation of larger geographical units, such as states or countries, whereas the remaining seven targeted locations were less susceptible to such hierarchical categorization. Among the tested models, GPT-4 exhibited superior performance with 55 percent accuracy, followed by GPT-3.5 at 47 percent and Llama-2 at 45 percent. The models showed significantly reduced accuracy on tasks with suspected hierarchical bias. For example, GPT-4’s accuracy dropped to 33 percent on these tasks, compared to 86 percent on others. The models identified the nearest cardinal direction in most cases, however, reflecting their associative learning mechanism, thereby embodying human-like misconceptions. We discuss avenues for improving the spatial reasoning capabilities of LLMs.

Innovative landslide disaster monitoring: unmanned aerial vehicle-deployed GNSS technology

Real-time monitoring technologies of surface deformation represented by the global navigation satellite system (GNSS) are essential for landslides early warning. Monitoring methods such as GNSS require on-site manual installation. Therefore, it becomes nearly impossible to deploy surface monitoring equipment when landslides are located in high mountain valleys that are challenging for personnel to access, and/or in hazardous situations. We propose an intelligent real-time monitoring and early-warning technology that employs an unmanned aerial vehicle (UAV) as a carrier to deploy GNSS equipment. A system was initially designed with five components: an adaptive sampling GNSS receiver, an intelligent cooperative network transmission module, UAV-dropped GNSS equipment, special delivery UAV, and an intelligent monitoring and early warning cloud platform. This new technology was applied to a real landslide in Gansu Province, China, in 2020. The mean absolute error of 30 days in the east, north, and upward directions were 1.2, 1.3, and 2.9 mm/d, respectively, comparing the deformation velocity between the traditional monitoring station and the UAV-dropped monitoring station. This observation facilitated the successful prediction of a landslide hazard on 27 January 2021, allowing for a timely alert to be issued. Collectively, the results of this study established that UAV-deployed GNSS technology can accomplish unmanned deployment of GNSS equipment for landslide monitoring to ensure early warning issuance in inaccessible and/or high-risk areas.

A shipboard integrated navigation algorithm based on smartphone built-in GNSS/IMU/MAG sensors

The rapid development of built-in sensors in smartphones has inspired a variety of ubiquitous public navigation applications. Nevertheless, previous works have focused on vehicle and pedestrian navigation by smartphones, with limited research on shipboard navigation. In this contribution, we designed a multi-sensors fusion navigation algorithm that integrates the global navigation satellite systems (GNSS), inertial measurement unit (IMU), and magnetometer (MAG) to achieve high-precision horizontal positioning on shipboard for smartphones. Considering the changeable motion state of a ship and the setting of non-holonomic constraint (NHC) noise, a magnetometer-based turning detection method is proposed, and a NHC model with adaptive noise is constructed based on the turning detection results. We conducted a lake experiment in Taku Forts Lake, Tianjin, using sensor data collected from a Xiaomi MI8 through our self-developed ’Sensor Logger’ app. The experimental results demonstrated a high success rate of 94.22 % for magnetometer-based turning detection which can effectively detect turning motions. Compared to the traditional algorithm without constraint information, the roll, pitch and yaw accuracy improve by approximately 34.7 %, 17.9 %, and 57.9 %, respectively. Additionally, the position and velocity accuracy in the east and north directions improve to 0.0926 m, 0.1779 m, 0.0548 m/s, and 0.0589 m/s, respectively, representing enhancements of about 10.7 %, 7.4 %, 10.5 %, and 6.4 %. Overall, the proposed algorithm efficiently improves the shipboard navigation accuracy of smartphones, especially in terms of attitude accuracy.

Potential distribution of three invasive agricultural pests in China under climate change

Invasive pests reduce biodiversity and ecosystem service functions, thereby leading to economic and also agricultural losses. Banana skipper (Erionota torus Evans), red palm weevil (Rhynchophorus ferrugineus), and coconut caterpillar (Opisina arenosella Walker) are invasive insect pests in the palm-growing regions and they have had serious consequences for the planting of bananas (Musa nana), palms (Trachycarpus fortune) and coconut (Cocos nucifera). Based on screened occurrence data, the present research utilized Maximum Entropy model (Maxent) to simulate the distribution dynamics of these three invasive insects in China, under current and future climate (2050s, 2070s, 2090s) in two shared socio-economic pathways (SSPs: 126 and 585) of the newly released coupled model intercomparison project phase6 (CMIP6). The results show that: (1) Under current and future climate conditions, all model groups exhibited an AUC value exceeding 0.92, which shows that the model prediction results are very good;(2) The suitable habitat area of E. torus Evans remains relatively stable with some expansion in the SSP126 of 2090s and some contraction in the SSP585 of 2090s. The suitable habitat area of R. ferrugineus showed an overall contraction, with substantial contraction in the SSP585 of 2090s.The suitable habitat area of O. arenosella has an overall expansion, with the most pronounced expansion in the SSP585 of 2070s; (3) The current centroid of suitable habitats for R. ferrugineus and E. torus Evans is located in Guangxi Province and wholely shift toward the south direction under future climate. The centroid of suitable habitats for O. arenosella is currently located in the northeastern maritime area of Hainan Province and will shift toward the north direction under future climate; (4) Temperature, precipitation and Human disturbance factors (Population density and Human influence index) were crucial variables for describing the distribution of the three species. For E. torus Evans in particular, percentage contributions of Population density was up to 31.4, which is only 0.1 different from ranked first Bio19 (Precipitation of the coldest quarter). The dynamics of habitats of these three species and the correlating driver factors proposed in this work provide essential insights into future spatial management of the three invasive insects in China. Our work is necessary and timely in identifying newly areas at high risk of expansion of the three invasive insects in the future, then suggesting strategic control measures to prevent their spread, and finally providing scientific evidence for the early prevention and rapid response to the three invasive insects.

LEO-Enhanced GNSS/INS Tightly Coupled Integration Based on Factor Graph Optimization in the Urban Environment

Precision point positioning (PPP) utilizing the Global Navigation Satellite System (GNSS) is a traditional and widely employed technology. Its performance is susceptible to observation discontinuities and unfavorable geometric configurations. Consequently, the integration of the Inertial Navigation System (INS) and GNSS makes full use of their respective advantages and effectively mitigates the limitations of GNSS positioning. However, the GNSS/INS integration faces significant challenges in complex and harsh urban environments. In recent years, the geometry between the user and the satellite has been effectively improved with the advent of lower-orbits and faster-speed Low Earth Orbit (LEO) satellites. This enhancement provides more observation data, opening up new possibilities and opportunities for high-precision positioning. Meanwhile, in contrast to the traditional extended Kalman filter (EKF) approach, the performance of the LEO-enhanced GNSS/INS tightly coupled integration (TCI) can be significantly improved by employing the factor graph optimization (FGO) method with multiple iterations to achieve stable estimation. In this study, LEO data and the FGO method were employed to enhance the GNSS/INS TCI. To validate the effectiveness of the method, vehicle data and simulated LEO observations were subjected to thorough analysis. The results suggest that the integration of LEO data significantly enhances the positioning accuracy and convergence speed of the GNSS/INS TCI. In contrast to the FGO GNSS/INS TCI without LEO enhancement, the average enhancement effect of the LEO is 22.16%, 7.58%, and 10.13% in the north, east, and vertical directions, respectively. Furthermore, the average root mean square error (RMSE) of the LEO-enhanced FGO GNSS/INS TCI is 0.63 m, 1.21 m, and 0.85 m in the north, east, and vertical directions, respectively, representing an average improvement of 41.91%, 13.66%, and 2.52% over the traditional EKF method. Meanwhile, the simulation results demonstrate that LEO data and the FGO method effectively enhance the positioning and convergence performance of GNSS/INS TCI in GNSS-challenged environments (tall buildings, viaducts, underground tunnels, and wooded areas).

Aerosol-PM2.5 Dynamics: In-situ and satellite observations under the influence of regional crop residue burning in post-monsoon over Delhi-NCR, India

The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 μg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 μg/m³ (24 h) by 27–34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention.

Monitoring of Land Use/Land Cover Change and Statistical Analysis of Change within the Scope of Urban Sprawl; North Cyprus Case

Monitoring land use and land cover (LULC) change is one of the main methods used in assessing landscape dynamics and defining environmental health at different spatio-temporal scales. In this study, the LULC changes between 2013 and 2021 in some centers (Lefkoşa, Girne, Gazimağusa, and Lapta) located in the Northern Cyprus Island were monitored through UA and GIS technologies, and a projection for the year 2050 was created within the scope of the settlements. The results showed that, specifically for settlements, there was an increase of 65.59% in the Girne region, 33.60% in the Lapta region, 66.87% in the Lefkoşa region, and 83.50% in the Gazimağusa region, and this increase was mostly in the north and west directions. As a result of statistical analysis, the 2050 projection reveals that settlement centers will spread significantly. It is anticipated that the results of this study will be a reference for decision-makers within the scope of land management and urban settlement planning in Northern Cyprus.