An integrated land vehicle navigation system based on context awareness

In the complex urban environments, land vehicle navigation purely relying on GNSS cannot satisfy user needs due to the loss of satellite signals caused by obstructions such as buildings, tunnels, and trees. To solve this problem, we introduce a GPS-/MSINS-/magnetometer-integrated urban navigation system based on context awareness. In this system, the data from the Micro Strapdown Inertial Navigation System (MSINS) are used to analyze and detect the context knowledge of vehicles, whose sensor errors can be compensated by the heuristic drift reduction algorithm for different motion situations. When GPS is available, the vehicle position can be estimated by unscented Kalman Filter, whereas in the case of GPS outages, the vehicle attitude is provided by an attitude and heading reference system and the motion constraints-aided algorithm is used to complete the positioning. In the experiment validation, the integrated navigation system is set up by low-cost inertial sensors. The result shows that the proposed system can achieve high accuracy when GPS is available. For most of the time without GPS, the system can guarantee the positioning precision of 10 m and compensate the errors of MSINS effectively, which fully satisfies positioning needs in complex urban environments.     

GPS/MEMS INS integrated system for navigation in urban areas

This paper evaluates the performance of a tightly coupled GPS/INS integrated system based on low cost MEMS IMUs in dense urban areas, and investigates two different methods to improve its performance. The first method used is to derive observations from two different constraint equations reflecting the behavior of a typical land vehicle. The first constraint equation is derived assuming that the vehicle does not slip and always remains in contact with the ground. If these assumptions are true the velocity of the vehicle in the plane perpendicular to the forward direction should be zero. The second constraint equation is derived from the fact that the height does not change much in a short time interval in a land vehicular environment. Thus, when a GPS outage occurs (partial/complete), the integrated system combines the INS and constraints-derived virtual measurements to keep the position and velocity errors bounded. This method is suitable for use in real-time applications. The second method is specifically suitable for a post-mission application and involves the use of Rauch-Tung-Striebel (RTS) smoother. The designed system performance is evaluated using two data sets collected in dense urban areas. The obtained results demonstrate the effectiveness of different algorithms considered, in controlling the INS error growth, and indicates the potential of MEMS IMUs for use in land vehicle navigation applications.     

GNSS/INS组合导航系统初始对准及其故障修复研究

正全球卫星导航系统可在无遮蔽环境中为用户提供高精度、低频率的导航定位服务,但在复杂环境中,GNSS信号易被遮挡或干扰。惯性导航系统经过初始化后,能够独立自主提供高频率、连续的位置、速度和姿态信息。通过二者合理结合,能够在遮蔽或半遮蔽环境中为用户提供连续且可靠的导航解。本文重点研究了GNSS/INS组合导航系统初始化及其故障修复方法,内容包括高精度惯导快速自对准技术、大失准角故障处理、磁力计辅助MEMS IMU抗差自适应姿态融合、神经网络辅助GNSS/INS组合导航系统故障识别与修复和惯导辅助BDS三频周跳探测与修复技术等,主要研究成果如下:     

一种车辆GPS/DR/DMAP组合导航系统

车辆导航系统常采用GPS/DR组合导航方式,在实际使用中若采用联邦滤波器,由于设计结构复杂,各种参数选取对滤波器的性能影响较大,选取不当反而引起导航精度的降低。本文设计了一种GPS/DR滤波器,这种滤波器当GPS接收机在失锁时仍能够为用户导航,并且该滤波器结构简单,设计简易。本文采用了DMAP,纠正了车辆相对于道路的偏差。实测导航实验验证了该滤波器能够满足导航精度要求,适用于城市中车辆导航应用。     

多星座GNSS/INS组合导航理论与方法研究EI北大核心CSCD

随着我国BDS的持续发展,GPS不断完善,GLONASS加紧现代化进程,多系统的联合使用对卫星定位的精度和可靠性带来极大改善。但在复杂的动态环境下,卫星信号频繁受到遮挡,甚至失锁,无法保证定位的有效性。INS不受外界环境的影响,并且可以获得短时、高精度的导航参数。GNSS/INS组合导航技术能够获得连续、可靠和高精度的导航参数信息,已经被广泛应用于军事与民用领域。     

LAND USE SCANNER: An integrated GIS based model for long term projections of land use in urban and rural areas

This paper describes the structure of the LAND USE SCANNER model, a GIS based model developed to generate spatial forecasts for various types of land use for a large number of grids. The model basically allocates land according to bid prices for various types of land use. The possibility of government intervention in land use is taken into account among others by adding aggregate constraints. The model includes all relevant land use types such as residential, industrial, agricultural, natural areas and water. The model is driven by sectoral models providing forecasts of aggregate land use in various land use categories. An application of the first version of the model is given for the Netherlands with some 200,000 grid cells. Further developments and refinements of the model are planned for the near future. Received: 18 February 1998/Accepted: 29 September 1998     

An intelligent and autonomous MEMS IMU/GPS integration scheme for low cost land navigation applications

An intelligent scheme to integrate inertial navigation system/global positioning system (GPS) is proposed using a constructive neural network (CNN) to overcome the limitations of current schemes, namely Kalman filtering (KF). The proposed CNN technique does not require prior knowledge or empirical trials to implement the proposed architecture since it is able to construct its architecture “on the fly,” based on the complexity of the vehicle dynamic variations. The proposed scheme is implemented and tested using Micro-electro-mechanical systems inertial measurement unit data collected in a land-vehicle environment. The performance of the proposed scheme is then compared with the multi-layer feed-forward neural networks (MFNN) and KF- based schemes in terms of positioning accuracy during GPS signal outages. The results are then analyzed and discussed in terms of positioning accuracy and learning time. The preliminary results presented in this article indicate that the positioning accuracy were improved by more than 55% when the MFNN and CNN-based schemes were implemented. In addition, the proposed CNN was able to construct the topology by itself autonomously on the fly and achieve similar prediction performance with less hidden neurons compared to MFNN-based schemes.     

The change of land use/cover and characteristics of landscape pattern in arid areas oasis: An application in Jinghe,Xinjiang

This paper uses 3S technology in macroscopic. Combining the integrated technology of ecological quantity analytical method with GIS technology through ArcGIS and Fragstats, the authors study the images of 1972, 1990, 2001, and 2005 and obtained land use data in Jinghe County. Then, the change of land use/cover and landscape pattern had been analyzed in the Jinghe County of Xinjiang. The conclusions were as follows: (1) The trend of LUCC is that the area of oasis expands slowly in nearly 33 years between 1972 to 2005 in Jinghe County. (2) The water area is mainly influenced by Ebinur Lake, so the area expands a little in this period. (3) The area of salinization-land expands at first and reduces later. The area of sand land decreases and the other land class increases, while the probability of transfer is always high. (4) Landscape change is also obvious throughout the decades. Overall, landscape density increases, the largest path index decreases at first and expends later, the weight area index decreases, and the shape of landscape becomes regulated. The nearest distances, the degrees of reunite, and outspread decreases. It shows that the connection of the main path in 1972 is better than 2005, wherein the patch becomes more complex. From the changes of Shannon’s Diversity Index and Shannon’s Evenness Index, we know that the diversity of landscape and the Interspersion Juxtaposition Index increase. The degree of diversity landscape and fragmentation increase also shows that the land uses become more complex. All in all, it is essential to intensify the spatial relationships among landscape elements and to maintain the continuity of landscape ecological process and pattern in the course of area expansion.     

An integrated remote sensing and GIS approach for monitoring areas affected by selective logging: A case study in northern Mato Grosso,Brazilian Amazon

Forest cover disturbances due to processes such as logging and forest fires are a widespread issue especially in the tropics, and have heavily affected forest biomass and functioning in the Brazilian Amazon in the past decades. Satellite remote sensing has played a key role for assessing logging activities in this region; however, there are still remaining challenges regarding the quantification and monitoring of these processes affecting forested lands. In this study, we propose a new method for monitoring areas affected by selective logging in one of the hotspots of Mato Grosso state in the Brazilian Amazon, based on a combination of object-based and pixel-based classification approaches applied on remote sensing data. Logging intensity and changes over time are assessed within grid cells of 300 m × 300 m spatial resolution. Our method encompassed three main steps: (1) mapping forest/non-forest areas through an object-based classification approach applied to a temporal series of Landsat images during the period 2000–2015, (2) mapping yearly logging activities from soil fraction images on the same Landsat data series, and (3) integrating information from previous steps within a regular grid-cell of 300 m × 300 m in order to monitor disturbance intensities over this 15-years period. The overall accuracy of the baseline forest/non-forest mask (year 2000) and of the undisturbed vs disturbed forest (for selected years) were 93% and 84% respectively. Our results indicate that annual forest disturbance rates, mainly due to logging activities, were higher than annual deforestation rates during the whole period of study. The deforested areas correspond to circa 25% of the areas affected by forest disturbances. Deforestation rates were highest from 2001 to 2005 and then decreased considerably after 2006. In contrast, the annual forest disturbance rates show high temporal variability with a slow decrease over the 15-year period, resulting in a significant increase of the ratio between disturbed and deforested areas. Although the majority of the areas, which have been affected by selective logging during the period 2000–2014, were not deforested by 2015, more than 70% of the deforested areas in 2015 had been at least once identified as disturbed forest during that period.     

Land resource inventories for integrated land use planning with special reference to the catchment of Pohru river in Jammu and Kashmir state

A Soil and land Use Map of Pohru catchment was prepared through the adoption of a systematic Air Photo-interpretation procedure including selective field studies in such a manner that the ten delineated mapping units were co-extensive with major Land Resource Areas. Defined in terms of physiography, soil profile, soil erosion and present land use each of the ten units has unique characteristics requiring differential management, which was arrived at through joint field studies by Pedologists who prepared the Map, Foresters, Soil Conservationists, Agronomists. a Horticulturist and an Agricultural Chemist. Stemming from this integrated effort a Land Use Plan for the catchment was drawn up with its major objective as the conservation of Land and Soil Resources leading to a reduction in sediment yields that are now so high as to clog the Jhelum river at the polnt where Pohru river joins it. In respect of four Land Resource areas, the plan prescribes radical readjustments of Land Use such as conversion of Forest Department owned Himalayan Southern slopes to grass lands, change of cropping pattern in Karewa side slopes and conversion of the northern and southern Himalayan footslopes now under cultivation into orchards. The areal extents of the different Land Resource Areas as also the cost of implementing the conservation recommendations have been furnished for every one of the twentyeight watersheds falling under the eight subcatchments of the Pohru catchment. The total cost of implementing the conservation recommendations approximates to about fifty five million rupees.     

Assessing the IRNSS L5-signal in combination with GPS,Galileo, and QZSS L5/E5a-signals for positioning and navigation

The Indian Regional Navigation Satellite System (IRNSS), which is being developed for positioning services in and around India, is the latest addition to the global family of satellite-based navigation systems. As IRNSS only shares the L5-frequency with GPS, the European Galileo, and the Japanese Quasi-Zenith Satellite System (QZSS), it has at least at present a limited interoperability with the existing systems. Noting that the L5-frequency capability is under development even for GPS, this contribution assesses the interoperability of the IRNSS L5-signal with the GPS, Galileo, and QZSS L5/E5a-signals for positioning and navigation using real data collected in Perth, Australia. First, the noise characteristic of the IRNSS L5-signal and its comparison with that of the GPS, Galileo, and QZSS L1/E1- and L5/E5a-signals is presented. Then, the L5-observables of combined systems (formed from IRNSS, GPS, Galileo, and QZSS) are assessed for real-time kinematic positioning using the standard LAMBDA method and for instantaneous attitude determination using the constrained LAMBDA method. The results show that the IRNSS L5-signal has comparable noise characteristics as that of the other L5/E5a-signals. For single-frequency carrier phase-based positioning and navigation, the results show better ambiguity resolution performance of L5/E5a-only processing than that of L1/E1-only processing.     

Development of an image based integrated method for determining and mapping aerosol optical thickness (AOT) over urban areas using the darkest pixel atmospheric correction method,RT equation and GIS: A case study of the Limassol area in Cyprus

This paper presents the development of an image-based integrated method for determining and mapping aerosol optical thickness (AOT). Using the radiative transfer (RT) equation, a methodology was developed to create a Geographical Information System (GIS) model that can visually display the AOT distribution over urban areas. In this paper, the model was applied to eleven Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) satellite images of Limassol, Cyprus during 2010 and 2011 to determine the AOT levels in Limassol Cyprus during satellite overpass. The study is innovative and unique in that the RT equation, satellite images, the darkest pixel (DP) method of atmospheric correction and GIS were integrated to derive AOT from satellite images and display the AOT distribution over an urban area without the input of any meteorological or atmospheric parameters. The accuracy of the algorithm was verified through statistical analysis by the strong agreement between the AOT values derived using the algorithm and the in situ AOT values from the ground-based sensors.     

Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region

In this study, we test the potential of two different classification algorithms, namely the spectral angle mapper (SAM) and object-based classifier for mapping the land use/cover characteristics using a Hyperion imagery. We chose a study region that represents a typical Mediterranean setting in terms of landscape structure, composition and heterogeneous land cover classes. Accuracy assessment of the land cover classes was performed based on the error matrix statistics. Validation points were derived from visual interpretation of multispectral high resolution QuickBird-2 satellite imagery. Results from both the classifiers yielded more than 70% classification accuracy. However, the object-based classification clearly outperformed the SAM by 7.91% overall accuracy (OA) and a relatively high kappa coefficient. Similar results were observed in the classification of the individual classes. Our results highlight the potential of hyperspectral remote sensing data as well as object-based classification approach for mapping heterogeneous land use/cover in a typical Mediterranean setting.