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Currently micro/nano satellites have become the protagonists in most formation flying missions, and there is an urgent demand to propose a reliable approach for quick fuel estimation of multiple-impulsive scheme on-board to achieve formation reconfiguration problems. This paper presents an optimal control approach based on multiple impulses for satellite formation in-plane reconfiguration issue in near circular orbit. Based on initial small deviation in the cylindrical coordinates system, a relative orbit motion expression is investigated, and a time-varying propagate system without perturbation is presented in this paper, which is suitable to calculate the solution of relative orbital maneuver by multiple impulses. The formation reconfiguration problem of this relative orbit motion is considered, and orbital motion equations with initial deviations are presented for relative orbital transfer calculation. Through different combinations of method from normal four-impulsive solution, the optimal three-impulsive method for relative orbital transfer in plane is obtained by analysis solution based on graphical and numerical way. In the end of paper, the effectivity and optimality of method is validated, and fuel consumption is analyzed through simulations of assumptive different formation reconfiguration missions.

Deoxygenation has frequently appeared in coastal ecosystems over the past century due to the joint influence of increasing anthropogenically induced nutrient inputs and global warming. The semi-enclosed Bohai Sea is a typical system that is prone to deoxygenation, with regular hypoxia events consistently recorded in recent decades. Based on in-situ observation data collected in large-scale voyage surveys in the Bohai Sea during 2008–2017, the seasonal variability in dissolved oxygen (DO) and its controlling mechanisms were studied. The results indicated that in spring and autumn, the DO distributions exhibited similar spatial patterns in the surface and bottom layers, while in summer, its spatial distribution was characterized by large-scale oxygen-poor zones distributed off the Qinhuangdao Coast and the central southern Bohai Sea in the bottom layer. The controlling mechanisms of the DO distribution varied from season to season. Spring and autumn DO distributions were dominated by the seawater temperature. Under the combined effects of stratification and decomposition, the summer bottom DO exhibited dual-core distribution. On the one hand, stratification could greatly impede vertical mixing, resulting in reduced bottom DO replenishment. On the other hand, the increased bottom organic matter intensified the decomposition processes, inducing massive DO consumption and elevated dissolved inorganic nitrogen concentrations. In addition, the stronger stratification might be the reason for the more severe deoxygenation in the southern oxygen-poor zones in summer. Our study provides guidance for an in-depth understanding of the DO seasonality in the Bohai Sea and the mechanisms that modulate it and for the improvement of hypoxia forecasts in ocean models.

Micro/nano satellites have become the protagonists in most formation flying missions currently, there is an urgent demand to find an approach to quickly calculate multiple-impulsive scheme on board for relative orbital transfer, while considering the optimality of total impulsive consumption. This paper presents an optimal control approach based on multiple impulses for in-plane satellite formation reconfiguration in near circular orbit. A new type of relative orbit motion equation is established based on the initial small deviation in the cylindrical coordinates system in this paper, which is suitable to calculate the solution of relative orbital maneuver by means of multiple impulses. The formation reconfiguration problem is investigated on account of the relative orbit motion, and the orbital motion equations with initial deviations are presented. The formation reconfiguration problem in orbital plane is discussed and analyzed selectively. An optimal three-impulsive approach for relative orbital transfer in plane is obtained by analysis solution based on graphical and numerical way, which is achieved by the combination method from four-impulsive solution. And the method is proved to be effectivity and optimality.

This paper aims to examine the characteristics of steady burning of inclined polymethyl methacrylate (PMMA) slabs under different ambient pressures. A sequence of experiments concerning steady burning intensity and combustion efficiency of the sample were conducted under both normal pressure (Hefei: altitude 30 m, 100 kPa) and reduced pressure (Lhasa: altitude 3650 m, 64 kPa). An in situ calorimeter based on oxygen consumption method was employed to measure the heat release rate of materials. A semi-theoretical model based on convective heat feedback from the flame was proposed and fitted well with laminar-dominated flame over the inclined PMMA surface. The critical maximum side length of sample for laminar-dominated flame in Hefei is lower than Lhasa. The trend of combustion efficiency with increasing inclination angle shows a great difference under different pressure environments.

A distributed detection problem where sensors are deployed to observe a common source of interest is studied. For centralized processing, decision is made by utilizing all the data collected at the sensors, which takes more resources of transmission and computation. For independent processing, it takes less resource at the cost of some performance loss. Motivated by the recently proposed cloud radio access network (C-RAN) and cloud radar, this paper proposes the cloud processing, where each sensor directly compresses (quantizes) its data and the central processor makes decision through all the compressed data. To model the quantization effects, the additive quantization noise model (AQNM) is adopted. Then, the performances of the generalized likelihood ratio test (GLRT), independent GLRT (IGLRT) and cloud GLRT (CGLRT) through deflection coefficients are analyzed. We especially focus on the performance comparison of cloud processing and independent processing, which depends on the number of sensors M, the variances of the additive quantization noise $\sigma _{\text{q}}^2$ and the additive noise σ2. Numerical results are conducted to verify the analysis.

Estimating the azimuth and elevation angles of targets is referred to as two-dimensional direction of arrival (2D-DOA) estimation problem, which is of vital importance in array signal processing and multiple input multiple output (MIMO) millimeter wave communication fields. Inspired by the Newtonized orthogonal matching pursuit (NOMP) for line spectrum estimation (LSE), this paper proposes the two-dimensional multi-snapshot NOMP (2D-MNOMP) to deal with the 2D-DOA estimation. Specifically, two-dimensional fast Fourier transform (FFT) is utilized to significantly reduce the computation complexity, and a Newton refinement step and feedback strategy are proposed to improve performance of DOA estimation. Based on the generalized likelihood ratio test (GLRT), the stopping criterion is established. The near-optimal performance of 2D-MNOMP is also demonstrated by comparing against other methods and the Cramer-Rao bound (CRB), both in terms of simulations and real data.

Sparse Bayesian learning (SBL) has been a popular method for sparse signal recovery under the standard linear model (SLM). Since SBL involves a matrix inversion in each iteration, the computation complexity is usually very high when applied to problems with large data set. Consequently, an inversefree sparse Bayesian learning (IF-SBL) algorithm has been proposed to achieve lower reconstruction errors than other state-of-the-art fast sparse recovery methods in low signal-to-noise ratio (SNR) scenarios. In practice, many problems can be formulated as a generalized linear model (GLM) where measurements are obtained in a nonlinear way such as image classification and estimation from quantized data. This work develops inverse-free generalized sparse Bayesian learning (IF-Gr-SBL), which can be viewed as performing iterations between two modules, where one module performs the standard IF-SBL algorithm, the other module performs the minimum mean squared error (MMSE) estimation. Finally, numerical experiments show the effectiveness

Fires become one of the common challenges faced by smart cities. As one of the most efficient ways in the safety science field, risk assessment could determine the risk in a quantitative or qualitative way and recognize the threat. And Bayesian Belief Networks (BBNs) has gained a reputation for being powerful techniques for modeling complex systems where the variables are highly interlinked and have been widely used for quantitative risk assessment in different fields in recent years. This work is aimed at further exploring the application of Bayesian Belief Networks for smart city fire risk assessment using history statistics and sensor data. The dynamic urban fire risk assessment method, Bayesian Belief Networks (BBNs), is described. Besides, fire risk associated factors are identified, thus a BBN model is constructed. Then a case study is presented to expound the calculation model. Both the results and discussion are given.

the task of road extraction from remote sensing imagery faces many challenges, traditional methods require complex extracting processes with relatively low precision. Deep learning methods such as convolutional neural network, VggNet, AlexNet, GoogleNet can obtain higher accuracy of road extraction, but requires lots of computing resources, training time and unsatisfactory real-time performance. Based on the reasons mentioned above, this paper proposes an optimized residual network with block-soft clustering (ORNBSC) for road extraction from remote sensing imagery. The block-soft clustering module aims at extracting essential features from satellite images and reducing the dimensionality, therefore accelerating the extraction speed. Meanwhile, the residual neural network module to improve the accuracy of extraction. Groups of experiments using Massachusetts roads dataset demonstrate that the ORNBSC model achieves better performance than traditional methods on precision of road extraction from remote sensing imagery.

Fires are one of the common challenges faced by smart cities. As the foundation of smart fire-fighting, urban fire risk assessment could help well understand the fire situation faced by a smart city, thus effective decision-making support for government and fire department could be provided. This work is aimed at further exploring the construction of urban fire risk assessment index system for smart cities. Urban fire history statistics analysis is performed. Besides, fire risk assessment indicators are discussed and selected based on theoretical analysis. Furthermore, urban fire risk assessment index system is built by the Analytic Hierarchy Process (AHP), which is of great significance to promote the application of smart fire-fighting in smart city construction.

There exist two main defects in traditional monitoring systems: rigid monitoring intensities and fixed task roles, which induce low monitoring efficiencies and large power consumptions. To alleviate these two problems, this paper proposes a cooperative block-variant monitoring mechanism for Internet of Things. This mechanism divides the whole monitoring area into several blocks with different monitoring intensities according to the spatial distribution of monitoring terminals based on spectral clustering. The monitoring intensities, including monitoring densities and frequencies, are decided by the status of monitoring objects in different blocks and the values of pre-setting thresholds. By adjusting the monitoring densities and frequencies in real time, this method makes the monitoring focus on the most critical blocks, which improves the monitoring efficiencies and reduces the overall consumption of system. In addition, adequate switching of the task roles of nodes balances their workloads, and therefore extends the overall life of the monitoring systems. A large number of experiments have been carried out, and the results show that this collaborative monitoring mechanism achieves good performance.

Both observations and numerical simulations have proven the existence of a plume bulge at the trumpet-like Pearl River Estuary (PRE). The formation dynamic of the PRE plume bulge during the southeasterly wind period is investigated with numerical model simulations based on the Regional Ocean Modelling System (ROMS). The results indicate that the formation and evolution of the PRE plume bulge is strongly modulated by the tidal cycle. The bulge is sustained by the strong jet flow along the longitudinal Lantau Channel, which is induced by the ebb current, and transports freshwater offshore. Its size is significantly modulated by spring-neap cycle, being large during spring tide and very small during neap tide. Besides, the existence of abundant freshwater in the estuary is a precondition for the formation of bulge. The ebb jet flow could be strengthened by lateral buoyancy forcing, which is induced by the accumulated freshwater in the estuary, thus guarantees the offshore extension of bulge. The input momentum flux by the river runoff, however, makes no significant contribution. The work indicates a new mechanism of plume bulge formation at a wide estuary and evaluates the impact of different forcings.

Unlike single geospatial objects extraction, the task of road extraction faces many challenges, including its narrowness, sparsity, diversity, and class imbalance. In order to solve the above problems, this paper proposes a modified convolution neural network with transfer learning (MCNNTL)for road extraction from remote sensing imagery. The techniques of data augmentation, transfer learning, data preprocessing, and backpropagation algorithm are used in order to get better performance. The Massachusetts roads dataset is chosen as the dataset to carry out the experiment of road extraction, and the result shows that this model outperforms traditional methods of road extraction from remote sensing imagery in precision, recall rate and composite accuracy.

To explore the possibility of using hyperspectral methods for handling conventional color images(CIs) or panchromatic images (PIs), some researchers propose to simulate HSI by using PI, based on stacking local pixels. Since the simulated data possesses the similar characteristics as HSI, vertex component analysis is then adopted to factorize the simulated data. Since the method only exploits the pixel value, it may face some problems if the gray value of targets varies. To avoid the problem, an improved approaches is proposed to reduce the influence of gray value. Instead of directly stacking the original pixels, the proposed method first extracts five different features of original images, then stack the different fundamental features and thus forming feature data. Such data provides more information than original RGB image, besides, it could provide more robust information than original methods since the application of image features. In the experiments, two different methods will be used to detect clouds, and results show the efficacy of proposed methods.

Realizing a reliable and robust localization based on mobile nodes plays a critical role in increasing pervasive sensing environments and location-based services (LBS). Although the Global Positioning System (GPS) has been widely used in outdoor environments, indoor robust positioning is still a challenging problem because of the unavailability of GPS and complex indoor environments where non-line-of-sight (NLOS) occurs due to reflection and diffraction. To solve the problem, an accurate and robust integration localization scheme based on Kalman filter is proposed in this paper. In the scheme, we merge the two heterogeneous but complementary positioning technologies on the mobile node equipped with both inertial sensors and the chirp-spread-spectrum ranging hardware. In order to NLOS identification and decrease NLOS error, a novel sight-state estimation method based on the Markov model is proposed. Besides, experiments have been carried out in real indoor NLOS environment to evaluate performance of proposed system. Experimental results indicate a remarkable performance improvement by using the proposed integrated system.

Wireless sensor networks (WSNs) have become one of the most promising and interesting areas over the past few years. But the properties of constrained resources make WSNs vulnerable to different types of intrusions such as Denial of Service (DoS) attacks which result in a large number of compromised nodes. For success application of ubiquitous WSN it is important to maintain the basic security. However, there is not an effective intrusion detection and self-recovery system to eliminate the harm of DoS attacks. To solve the problem, an agent-based intrusion detection and self-recovery system for WSNs is proposed, which adopts the distributed architecture to monitor intrusion activities and realize abnormal events processing in local nodes. Finally, the system is analyzed and verified. The simulation results indicate that the compromised nodes can self-recover effectively and network total energy consumption also is reduced effectively.

Chirp Spread Spectrum (CSS) defined in IEEE 802.15.4a, known to be resistant to signal interference and multipath fading, can perform precise ranging and it is a promising technology for localization systems. This paper is designed to investigate the performance and characteristic of CSS ranging in LOS and NLOS surroundings. On the basis of the experimental analysis, we propose a voting-averaging filter to deal with the problem of unpredictable abnormal ranging points and measurement noise. Secondly, we further propose a uniform model of CSS ranging, which can apply to LOS and NLOS range measurement, to improve the performance of distance measurement.

Providing a seamless, ubiquitous and reliable positioning system based on smartphones plays a critical role in increasing pervasive sensing environments and location-based services (LBS). Although the Global Positioning System (GPS) has been widely used in outdoor environments, indoor/outdoor seamless positioning is still a challenge because of the unavailability of GPS in indoor environment. To solve this problem, an advanced integration of Wi-Fi, MARG (magnetic, angular rate, and gravity sensors) and GPS is presented in this paper. In order to merge there heterogenous but complementary technologies on smarphones, an adaptive integration structure based on Kalman Filter (KF) and Particle Filter (PF) is proposed. Besides, experiments have been carried out in real indoor/outdoor environment to evaluate performance of proposed system. Experimental results indicate a remarkable performance improvement by using the proposed integrated system.

Location fingerprinting for outdoor areas using existing wireless local area network infrastructure is growing rapidly in importance and gains commercial interests in location-based services. The most challenge in outdoor fingerprint positioning is the fluctuation of received signal strength (RSS). In the paper, we propose a micro-cell-based radio map construction method targeted to deal with the unstable RSS and build a metropolitan-scale radio map efficiently by using spatial diversity instead of temporal diversity and an average filter which are made use of by the conventional methods of building radio map. The paper also presents a self-adaptive update algorithm using RSSs which are collected by users of Wireless-LAN (WLAN) location system and the estimated user's location to update the radio map, which further improves the positioning accuracy. Experimental results in outdoor WLAN environments demonstrate that our methods not only improve the efficiency of constructing the radio map but also the positioning accuracy.

In WLAN-based fingerprint positioning systems, the data preprocessing before applying the matching algorithm is important for improving the accuracy. In this paper, an effective preprocessing scheme is proposed to improve selection methods of reference point (RP) and access point (AP). The spatial filter is used to select RPs, and APs are decided in the principle of minimal redundancy. The experiment is carried out on campus, whose results show that the proposed scheme could improve the accuracy of both the Kernel-based and KNN matching algorithm, especially the former, by about 25%. Moreover, the denser is the AP distribution, the better the proposed scheme performs.

In recent years, the demand of ubiquitous computing and location-based services (LBS) has risen. The most challenge in LBS is the location of mobile terminals. Existing location techniques have poor performance in urban environments due to severe multipath and NLOS propagation. Fingerprint localization based on wireless-LAN (WLAN) has been preferred for those types of environments. Although location fingerprinting has been researched in some previous works, there are only few studies that investigate the performance of outdoor positioning system according to physical parameters and the underlying environment. In this paper, we present an experimental analysis for outdoor fingerprinting system, implemented over the WLAN. On the basis of this experimental analysis, we demonstrate that it is indeed feasible to perform outdoor locating with reasonable accuracy using 802.11-based positioning. Secondly, we further work out certain crucial control parameters, which we can adopt to different cases and scenario, to achieve better positioning accuracy.

Reliable and real-time location information is important for location-based services (LBS) of the mobile terminals. Existing location techniques have poor performance in urban environments due to severe multipath and NLOS propagation. Fingerprint localization based on wireless-LAN (WLAN) has been preferred for those types of environments. However, the most challenge in fingerprint positioning is the fluctuation of the received signal strength (RSS) and the online computational burden or latency for a metropolitan-scale radio map. To alleviate this problem, we propose a real-time algorithm including two key techniques: (1) The micro-cell-based radio map construction method exploiting spatial diversity is used to tackle the fluctuation of RSS and enhance positioning accuracy. (2) The RSS-based clustering of the radio map is presented to reduce the computational burden and improve the performance of the locating system. The practical implementation of positioning mobile terminals at different speeds in urban environments is described and conducted. Experimental results show that our algorithms can significantly improve the performance of WLAN-based positioning system.

Location tracking for vehicle plays a key role in increasing road safety and transportation efficiency. In this paper, a Wi-Fi based real-time tracking system which can work in both indoor and outdoor environments is presented. The proposed system estimates the location of vehicle using the Received Signal Strength (RSS) fingerprints of transmitted Wi-Fi Access Points (APs) and Particle Filter (PF). A simple, effective approach based on Inverse Distance Weighting (IDW) is designed to build fingerprints database effectively. Outdoor experiments have been carried out to investigate the usefulness of IDW and PF. Experiment results show that a tracking system based on Wi-Fi signals is able to give a position estimate of moving vehicle in real time, and that it can be significantly enhanced by PF compared with by Kalman Filter (KF).

Recently, fingerprint positioning systems for urban areas using the existing wireless-LAN (WLAN) play important role in ubiquitous applications. The most challenge in fingerprint positioning is how to reduce the number of training samples and high time consumption with no significant degradation in location accuracy. To deal with this problem, we propose a micro-cell-based radio map construction method based on spatial correlation of collected signal samples and spatial diversity. We present how to apply these properties to improve positioning accuracy and reduce time consumption of constructing a metropolitan-scale radio map. To further enhance the positioning accuracy and reduce latency for real-time positioning, we propose an unequal weighting fast matching algorithm to exploit temporal-spatial diversity and spatial continuity of the objects' movement trajectory. The practical implementation of positioning mobile device at different speeds in urban environments is described and conducted. Experimental results show that our algorithms can significantly improve the performance of WLAN-based fingerprint positioning system.

In this paper, a novel algorithm called Receding Horizon Kalman Particle Filter (RHKPF) has been proposed and is applied to our improved fingerprint-based WLAN vehicle positioning system. The RHKPF is a particle filter that the optimal importance density is approximated by incorporating the most current measurement through a Receding Horizon Kalman Filter (RHKF), for that the RHKF is believed to be robust against temporary modeling uncertainties since it utilizes only finite measurements on the most recent horizon. In this paper, the RHKPF and the Kalman Particle Filter (KPF) are both applied to the WLAN-based vehicle positioning system with temporary measurement modeling uncertainty. Through simulations we find that, although the KPF has the property of robustness compared with the RHKPF when there is temporary modeling uncertainty, whereas the RHKPF has the property of fast convergence after temporary modeling uncertainty disappears compared with the KPF. So we propose a scheme called KPF-RHKPF that both of the RHKPF and the KPF are used to estimate the position of the vehicle, that is, when there is a modeling uncertainty, the estimation results of the KPF are used as the estimation of the vehicle, and when the modeling uncertainty disappears, the estimation results of the RHKPF is used as the vehicle estimation. Simulation results show us the robustness and the fast convergence properties of the KPF-RHKPF.

Reliable and accurate vehicle position information is important for autonomous systems. The increased popularity of wireless networks has enabled the development of positioning techniques that rely on WLAN signal strength. Fingerprint architecture is one of the most viable solutions for Received Signal Strength (RSS)-based positioning. The most challenging aspect of the fingerprint based method is to formulate a distance calculation that can measure similarity between the observed RSS and the known RSS fingerprints. In this paper we proposed an improved fingerprint-based algorithm that incorporates the spatial diversity and the road constraints for vehicle positioning. We present how to apply the properties of the spatial diversity and the road constraints to enhance the robustness and the accuracy of the fingerprint algorithm. To further improve the accuracy of the fingerprint algorithm, a Dead Reckoning (DR) system, which has been widely used for vehicle navigation, has been integrated with the WLAN-based positioning system. We have conducted extensive field tests and simulations for the proposed positioning algorithm, and key outcomes are given out. It has been demonstrated by the results that our algorithm could significantly improve the performance of WLAN-based vehicle positioning system.