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In order to better simulate the vertical ejection process of a certain type of surface-to-air missile, this article establishes a certain type of surface-to-air missile ejection device high-pressure indoor ballistic equation, low-pressure indoor ballistic equation, missile motion equation and pressure relief hole exhaust equation, and use MATLAB software to program. The pressure curves of the high and low pressure chambers with time, the gunpowder combustion curve and the missile movement curve are obtained. Comparing the data simulation results with the research results of other scholars, the results show that the algorithm can better reflect the change trend of each key parameter in the missile ejection process, and the obtained data can be used as the experimental basis for missile launch dynamics simulation.

With the development of scientific big data technology, problem-oriented analysis becomes normal case. iAnalysis V1.0, an interactive analysis cloud service system, gives a unified cloud resource management service for scientific data analysis. It can not only be used directly by end-user scientists through the service portal, but also be called by other existing data systems in the form of docker container. In this paper, there are presented the key technical methods of iAnalysis V1.0, including the interactive analysis service solution based on container technology, the interactive analysis components and storage & high-performance computing services for interactive analysis. There are also introduced the specific application services of iAnalysis V1.0 such as lightweight deployment service, management and monitoring services, external application programming interface services and so on. Some applications of iAnalysis V1.0 are also presented. The basic deployment images of iAnalysis V1.0 can be download from https://hub.docker.com/repository/docker/bioinf/ianalysis.

The vibration response of the launcher is one of the important sources of the initial disturbance of the missile launch. Based on the theory of multi-body dynamics and finite element analysis, a rigid-flexible coupling model of a vehicle-mounted missile launcher is established, and the vibration response of the launching tube is simulated. Using the method of replacing the rigid and flexible body models of the key components, the order of the main factors affecting the subsidence of the launch tube is obtained, and the conclusion that the subsidence can be reduced by increasing the rigidity of the frame and chassis is proposed.

This paper presents results of the ongoing development of a crowdsourcing platform for labelling remote sensing images. In order to perform fast spatial and temporal retrieval and read data efficiently, this paper proposes an adaptive grid design method to enable the remote sensing images being segmented into tiles, which supports parallel reading and writing, and uses Ceph cluster for storage. To guarantee crowdsourcing results' quality, a quality control mechanism combining expertsourcing and crowdsourcing is proposed. The online labelling system is designed and a prototype has been developed. Users neither have to install any software, nor download large remote sensing images. They can directly label the images by just selecting tags and drawing polygons. By providing such a platform, a large volume of training dataset for remote sensing classification can be obtained.

Hidden Markov Chains (HMCs) and, more recently, Hidden semi-Markov Chains (HsMCs) have been used by several groups of researchers to provide a model for indoor localization. A homogeneous HMC is completely determined by the state initial probability vector and the state transition probability matrix. This is also true for the HsMC provided the state duration probability is given. These parameters are often chosen heuristically but when sufficient measurement training data are available, they can be learned using the well-known Baum-Welch algorithm. Given the model parameters, approaches such as the forward-only algorithm, the forward-backwards algorithm and the Viterbi algorithm can be applied for state sequence inference under the HMC/HsMC framework. In indoor localization applications, there is often insufficient prior information to specify such parameters in advance of the application and they have to be learned from limited amounts of training data. In this paper, we endeavour to evaluate the parameter learning accuracy of the Baum-Welch algorithm using varying amounts of training data, and evaluate the influence of applying inaccurate model parameters on these typical state estimation algorithms under both the HMC and HsMC frameworks. All of the evaluations are based on received signal strength (RSS) for application to indoor localization.

In this paper, we investigate the problem of detecting and tracking small targets in sea clutter using the Bernoulli Track-Before-Detect filter. This filter is an optimal recursive Bayesian detector / estimator to determine the state of a single target and its presence in noise. Typically, clutter amplitude fluctuations are modelled using the Rayleigh distribution. This assumption, however, is not valid in the maritime domain, where sea-clutter is often spikier with a longer distribution tail. In this work, we develop the Bernoulli filter for a compound Gaussian clutter model with thermal noise and model the target with a Swerling 1 fluctuation. To demonstrate the performance improvement, we model the texture as a gamma distribution, giving an overall K plus noise distribution for the clutter plus noise. The detection and tracking improvement is then demonstrated using Monte Carlo simulation.

The detection and tracking of targets in the maritime environment can be a challenging problem. One approach to improve the tracking ability of weak targets is to use track before detect (TBD). However, different TBD algorithms have different underlying assumptions about the background clutter which are not always valid. In this paper we consider three TBD algorithms: the histogram probabilistic multi-hypothesis tracker (H-PMHT), the Bernoulli filter and the multi-Bernoulli filter. The H-PMHT inherently assumes the clutter is modelled with a distribution derived from the continuous limit of the Poisson distribution, while the two Bernoulli filters both assume Rayleigh distributed clutter. To quantify performance, we use a Monte Carlo simulation with the sea clutter generated using both Rayleigh and K-distributions. The performance is then determined using the optimal sub-pattern assignment (OSPA) distance.

A design of scientific analysis service based on container technology in data cloud is presented: resource nodes are scaled up by means of automatic registration; resource nodes can be physical hosts or virtual hosts. When the utilization rate of computing resources reaches the threshold, the management node starts resource registration, subsequently, a resource host is to be registered and the available container instances are into the pool. The health of the container instance pool is maintained inside of the host. The instance lifecycle management is performed according to the maximum usage time and maximum silent time from each instance. A user can not only do analysis jobs through the interactive component, the interactive programming component or the interactive application component, which is in a separate container instance for efficient isolation of the resources, but also use data storage clusters and the high-performance computing clusters in data cloud.

In this work, we study the case of tracking a moving sensor using the radio interferometric positioning system measurements. The system consists of four wireless network nodes and it measures the sum of signal phase differences between two receivers while the other two are transmitting sine waves at close frequencies. With the locations of three sensors known, we can estimate the fourth sensor moving trajectory from a sequence of phase measurements using a nonlinear filter. Two nonlinear filters, a natural gradient Kalman filter and a extended Kalman filter, are investigated and implemented for this moving sensor tracking problem. The performances of them are compared in a computer simulated tracking experiment.

Diverse storage formats, archive dispersal, and inconsistent naming make it difficult for researchers and the general public to find and access remote sensing data. To facilitate the use of remote sensing data, this paper provides an integrated framework for direct reading remote sensing data in a widely compatible and analysis-ready format, NumPy ndarray. The framework is composed of two main components. One is the raster data processing and storage model. All the operational gridded remote sensing data are split into tiles, and reorganized in n-dimensional array. Then the N-Dimensional data array is serialized into netCDF and stored into distributed file system. The other is the spatiotemporal filter to achieve parallel query, and it has been encapsulated into Internet-accessible application programming interfaces (APIs). The scenario of calculating NDVI of a specified spatiotemporal range given at last illustrate the efficiency and convenience of our platform provided for remote sensing data analysis.

This paper considers the joint recursive estimation of the dynamic state and the time-varying process noise covariance for a linear state space model. The conjugate prior on the process noise covariance, the inverse Wishart distribution, provides a latent variable. A variational Bayesian inference framework is then adopted to iteratively estimate the posterior density functions of the dynamic state, process noise covariance and the introduced latent variable. The performance of the algorithm is demonstrated with simulated data in a target tracking application.

The Chinese Remainder Theorem (CRT) enables phase unwrapping for measurement of a distance using multiple wrapped phases of sinusoidal transmissions, such as in radar, sonar, or wireless geolocation. In the presence of measurement noise, the existing CRT algorithm assumes that the correct number of wrapping wavelengths can be obtained after a round off operation by ignoring the received signal noise. In this paper, additional hypotheses for the output of the round off process are formulated and a modified CRT algorithm for range estimation is proposed. We show that an improved distance reconstruction rate over the existing CRT algorithm is achieved from both analytical calculations and numerical simulations.

The problem is target motion analysis (TMA) in situations where the variance (standard deviation) of additive white Gaussian measurement noise is unknown and time-varying. In particular, the paper examines a somewhat surprising result from the theoretical analysis based on the Cramer-Rao bound, which suggests that the best-achievable (second-order) error in target state estimation is unaffected by the lack of knowledge of the measurement noise variance. In order to examine this result, the paper develops three recursive Bayesian filters for TMA, which jointly estimate the target state and the measurement variance. The basis of all filters is the Cubature Kalman filter for bearings-only tracking, combined with (i) the variational Bayesian approach, (ii) the Rao-Blackwellised particle filter, and (iii) the interactive multiple-model (IMM), to deal with the unknown time-varying measurement variance. The paper presents extensive numerical simulation results and comparisons, which confirm that the lack of knowledge of the measurement noise variance is by no means a handicap for TMA.

In this paper, we investigate cooperative passive sensor trajectory planning for tracking a target where the tracking error is sensor trajectory dependent. We consider the problem under a scenario of tracking a moving target using two unmanned bearings-only sensors. The basic idea is to maximise the target information acquired from the processing measurements of the two sensors by cooperatively scheduling their future trajectories at which sensor measurements will be taken. In the literature this problem is modeled by a partially observed Markov decision process and optimal action which maximises an expected reward function is sought. Three reward functions, namely, the Expected Reward, the Determinant, and Trace of the associated Fisher Information Matrix (FIM) for the underlying problem are analysed and discussed. These rewards may only be evaluated practically through various approximations. We show that the correlation between two sensor states is weakened significantly for the Expected Reward due to linearisation and thus the closed-form Expected Reward as well as the Trace of FIM are inappropriate for this sensor trajectory scheduling problem. Finally, we present simulation results which are based on the example of a non-cooperative target chasing via two cooperative bearing-only sensors.

The Radio Frequency Identification (RFID) is a rapidly developing technology with growing applications in several fields. One of the key applications is the localisation of tagged objects using signal phase difference information via dual-frequency technology. In this application, unwrapping signal phases to acquire the range between the reader and RFID tag is the major issue that has typically been addressed in the literature using either the Chinese Remainder Theorem or Lattice Theory. In this paper, a lattice-based method robust to phase measurement noise is presented to resolve the wrapped range. The proposed algorithm is shown to be more robust and efficient than existing approaches in terms of the reconstruction probability. Simulations are presented to illustrate the performance of the proposed algorithm.

The massive data is constantly and rapidly growing in remote sensing field. How to achieve efficient storage and rapid retrieval of massive remote sensing image metadata is a difficult problem. Big Data technologies provide convenient and fast tools for the storage, retrieval and analysis of remote sensing image metadata. According to the feature of remote sensing image metadata, we first give new concepts of fat grid and thin grid and propose a grid indexing method called Spark-Fat-Thin-Grid-Index (SFTGridIndex). In SFTGridIndex, the index file and partitions files are stored in HDFS. We then design an optimized retrieval method based on SFTGrid-Index. The method can avoid the intersection computing of a large number of polygons. This research can ensure efficient storage and fast query of massive remote sensing image metadata and has good scalability.

Use of the phase of a signal to measure distance carries an inherent ambiguity. The problem is typically addressed by the use of several different frequencies and the Chinese Remainder Theorem or lattice methods, but these methods result in computational complexity issues. The difficulties are increased by the presence of noise. This paper presents a lattice-based algorithm to resolve phase ambiguity more efficiently and under more relaxed constraints than existing approaches. Simulations are presented to illustrate the performance of the proposed algorithm and compared with existing methods.

Optimal determination of a UAV using a vision-based system to match images against a database is an important problem. It can be reformulated to the problem of using multiregion scene registration to match areas of a noisy and distorted image to a geo-referenced image. Under the assumptions that the mapping between sensed and geo-referenced images preserves gradients of straight lines cross mapping points on images and registration errors are all Gaussian distributed, we derive a two-stage weighted linear least square algorithm which localises the UAV optimally. Performance of the proposed algorithm is demonstrated via Monte Carlo multiple runs along with those available in literature.

Detection of weak signals in a low SNR environment is generally difficult, particularly, when the underlying signal noise is not only not Gaussian distributed but essentially unknown. A good example of such a case is the detection of termite biting signals from noisy audio data recorded by a passive acoustic sensor. In this paper, we present a novel technique to discriminate weak signals in data from noise of a learned non-Gaussian distribution. The proposed method, proceeds via the framework of generalised likelihood ratio test, and consists of two fundamental steps. First, an entropy-based incremental variational Bayesian inference is adopted to learn the non-Gaussian distribution from data using a Gaussian mixture model. An information geometric mapping of the data is then carried out via the total Bregman divergence (tBD), where the ambient noise distribution is approximated by the tBD-based l 1 -norm center of the neighboring data points over a specified time window. Experiment results show that the proposed method yields a significantly improvement in detection probability in low SNR and a robust detection performance compared with existing detection techniques.

Nowadays, many spatial data infrastructures are providing users the ability to discover, access, publish, and share geospatial data for free. Metadata plays a critical role when searching remote sensing data entities. However, as the spatial data infrastructure usually has a large number of users, the metadata database has high access concurrency requirement. Moreover, metadata of massive remote sensing data is relatively a big table and the query conditions are complex, which includes temporal and spatial aspects. It is difficult to manage it and the metadata retrieval efficiency will not be ideal. In this paper, database cluster and table partitioning technology are adopted. Database cluster technology is used to satisfy the high concurrency requirement, and table partitioning technology is used to improve the query efficiency. Moreover, the relationship between the number of the partitions and the data volume is also studied. The results of the experiments show that all these technology greatly optimize the geospatial metadata retrieval process.

The problem of optimally selecting a subset of transmitters and receivers from a set of transmitters/receivers in a MIMO radar network which will minimize the localisation error of multiple in-region targets of interest under power constraint is studied. We show that such a problem can be formulated as an estimation variance minimization problem with a submodular variance reduction function. Based on the property of submodularity, a transmitter/receiver selection policy which minimizes the overall estimation covariance is proposed. Together, we also present an algorithm which can efficiently implement this sensor scheduling policy in polynomial time with a guaranteed accuracy compared to the optimal solution. The effectiveness and efficiency of the proposed method are demonstrated using simulated examples.

A measure of the ability of a sensor array to resolve two closely spaced point sources in angle is proposed here based on the framework of information geometry. The consideration of the geometric structure of a measurement model leads to the concept of information resolution which serves as a new metric to measure intrinsic similarities and differences between signal distributions that generate the manifold geometry. The statistical divergence between two sources is characterized in terms of the geodesic distance induced by the Fisher information metric. An analytical expression of the angular information resolution limit (AIRL) is derived using the constraints on the probability of error for a binary hypothesis test associated with the resolution of two sources. The influence of the detection error as well as the signal-to-noise ratio (SNK) on resolvability are demonstrated. The proposed AIRL can be used as a performance measure for sensor arrays in localizing remote sources and is applicable to various applications (e.g. radar, sonar, or astronomy).

Many nonlinear parameter estimation problems can be described by the class of curved exponential families. The latter are fundamental concept in the framework of Information Geometry. This paper shows that when a closed-form statistical model is available the problem can be mapped onto the corresponding statistical manifolds via fixed parameterizations and thus solved optimally through a manifold gradient method. The solution process involves a dual projection which iteratively operates under the e-connection and m-connection in the flat manifolds with the coordinate systems in which the Cramer Rao Bound is attained. An example of tracking a moving target by two bearings-only sensors with location uncertainties is presented to demonstrate the efficiency and optimality of this manifold based method as well as the associated geometrical interpretation.

The first major outbreak of HPAI H5N1 virus in wild waterbirds took place in Qinghai Lake, western China, May 2005. During fall/winter of 2005 and spring 2006, H5N1 virus spread rapidly over Asia, Europe, and Africa. One hypothesis for the global patterns and dynamics of the spread of H5N1 virus is that seasonally migratory wild waterbirds may carry the virus and spread it along migratory flyways. Since 2007, Tens of waterbirds had been marked with GPS transmitters to get the migration tracks in Qinghai Lake. Satellite remote sensing is a useful tool to help analyzing the birds' GPS tracks. In this paper, we take advantage of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images to study the birds' migration patens. How to process and use the massive remote sensing images efficiently across computer cluster is discussed detailedly. At last, land surface temperature and cloud coverage information are obtained and used to study the wild birds' migration.

Species distribution modeling is an important ecological research task that has received a great deal of interest. There are several single model packages and applications available for species distribution analysis. This paper introduces Bird-SDPS, a Prediction System for Migratory Birds' Spatial Distribution, which is an extensible system for birds' spatial distribution prediction. The Bird-SDPS uses birds' GPS tracking data and remote sensing data as input to build multiple distribution models, which are implemented by different programming languages. And the system provides online access and visualization functions. In order to store large dataset of remote sensing data, we design a hybrid storage structure based on HBase. We extensively evaluate our system using a real-world GPS dataset collected from 90 wild birds over 3 years. We show that the system can conduct birds' distribution prediction based on multiple models, and our hybrid data storage modes can outperform the traditional storage modes of files.

Optimal weapon target assignment problem involves NP-complete searching process and becomes computationally impractical as the number of weapons and targets increases. Existing approaches, therefore, only consider approximate method with heuristic searching scenarios, which are, however, no theoretical performance guarantee for the level of accuracy that the underlying algorithm may achieve. In this paper, the weapon target assignment problem is studied in the framework of combinatorial optimization theory. Following a previous work, an accelerated continuous greedy algorithm is proposed to address the underlying problem in polynomial time. The algorithm is proved to have the best guaranteed performance against optimal solution among the existing polynomial time methods.

The localization of sensor networks using positioning systems rather than a GPS device is of great interest in many real world applications. One of the systems involves a circular type localization using distance measurements, where the problem of localizing a free sensor with knowledge of three anchors in a 2D plane is formulated using the Cayley-Menger determinant with no closed-form solution. Under some mild assumptions on the free sensor distribution, we propose a linear closed-formed algorithm to approximately solve the sensor localization problem. Analysis and discussion are presented to justify the proposed method and its localization error performance is demonstrated via simulation.

With the rapid growth of data in various domains, models for data processing become more and more complicated and require higher and higher computing power of local machines. In our opinion, it is a good solution to put models into the "cloud". Integrating and categorizing these models in different domains is convenient for users. They don't have to establish similar systems for different models in different domains. This paper demonstrates how we employ the Web Processing Service in the Open Geospatial Consortium as a service engine to establish a scientific model integration platform to handle requests and responses. And a study case that integrates some geospatial models based on the platform is presented at last.

A novel array system architecture is proposed to satisfy the need of new technology revolution in this paper. Cascaded Switched Ethernet based data link is used to acquire data. The implementation of the prototype is discussed in detail and the scheme's feasibility is verified by the lake trial results. The proposed scheme is proved to be modular and scalable. Section I presents the new technology revolution requirement of underwater array system. Traditional underwater array systems are introduced in section II. In section III, a novel array system architecture is proposed. The implementation of the prototype is discussed in section IV. Finally, the lake trial results are shown in section V.

Name ambiguity has long been viewed as a challenging problem in many applications, such as scientific literature management, people search, and social network analysis. When we search a person name in these systems, many documents (e.g., papers, web pages) containing that person's name may be returned. It is hard to determine which documents are about the person we care about. Although much research has been conducted, the problem remains largely unsolved, especially with the rapid growth of the people information available on the Web. In this paper, we try to study this problem from a new perspective and propose an ADANA method for disambiguating person names via active user interactions. In ADANA, we first introduce a pairwise factor graph (PFG) model for person name disambiguation. The model is flexible and can be easily extended by incorporating various features. Based on the PFG model, we propose an active name disambiguation algorithm, aiming to improve the disambiguation performance by maximizing the utility of the user's correction. Experimental results on three different genres of data sets show that with only a few user corrections, the error rate of name disambiguation can be reduced to 3.1%. A real system has been developed based on the proposed method and is available online.

The China FLUX is an observation and research network that provides long-term and continuous measurements of the exchanges of carbon dioxide, water vapor and energy between terrestrial ecosystem and atmosphere in China. The meteorological and ecological data was collected by the sensor devices of the China FLUX in the field stations, and was transmitted to the CERN server through internet. To ensure the data integrity of collection and transmission, we deployed a Web GIS-based monitor system to check and visualize the status of the devices and the network. It is a simple distributed application. Currently, the application was installed at seven field stations. Users and administrators can view the status information of the network and the devices of these field stations in the web browser.

Species distribution analysis is becoming imperative in recent years. In this paper, we proposed the application of Maximum Entropy model to predict the species distribution from satellite tracking data and remote sensing data. We construct the 2-D geographic lattice to address the problem of huge calculating amounts resulting from the large amount GPS tracking records. The results of experiment showed the maxent model outperform the traditional classification in prediction, and the prediction results represented that the changing environment condition have directly impacts on wild species selection. We believe our method provide a useful step to understand the changing environment.

Observing system have attracted strong attention in recent years and much of real-time data is generated by those systems. Real-time data stream management is an important part of observing system. Despite significant progress has been made in sensor-based observing system, there are still some challenges to provide real-time data stream for various science application scenarios. We address some of the major challenges by proposing three-layer framework named RDSS that can provide real-time data stream for major application scenarios. Our first prototype has been developed to show that the proposed framework has good performance in sensor-based observing system.

An experiment was done to study the impact which SCC reinforced by different volume fraction has on CBF in the relative increase of splitting-tensile strength, splitting toughness and flexural toughness. The findings were that the more the fiber volume, the more increase of the splitting toughness. When the volume was 1.2‰, the splitting tensile strength of 28d and 60d reached its maximum; SCC tension increased with the increase of the volume of CBF; when the CBF was added, the flexural load-displacement curves of SCC became not so sharp, thus having an apparent impact on the toughness of SCC.

When tracking a target using phase-only signal returns, range ambiguities are a major issue. In this work, a look-up table between phase measurement space and target location space is constructed for phase measurement mapping. Solving such problems via solutions to Diophantine equations has been used to locate candidate locations. Here we show how such target location ambiguity can also be resolved over time when the underlying target is in motion and where issues of clutter are treated via a phase distribution discrimination method. That is, a probability density function of the ambiguous phase-only measurement that takes both sensor noise and target motion distributions into account is derived based on directional statistics. This approach to solving phase ambiguity under significant clutter conditions has promising results. Refereed/Peer-reviewed

Object tracking in video surveillance is often restrained by the requirement of real time processing. In this paper, the object tracking problem is approached as two independent processes. At each video frame, multi-object information is approximately represented by a set of rectangle patches as the result of object detection and the data set is converted to a set of virtual location measurements as well as the associated feature measurements with assumption that location and feature measurements are independent. In the consequent process, a feature aided integrated probabilistic data association type multi-object tracker is used to recursively update the posterior densities of the objects estimated frame by frame based on the virtual measurements obtained from the former process. The uncertainty of the converted virtual measurements largely depends on the object detection techniques used and it may be handled in the later process by the tracker. Our results demonstrated that the feature aided data association technique can resolve the uncertainty due to the imperfect of virtual measurement process. Furthermore, the required computational overhead is considerably less than those of pixel-wise based approaches.

Real-time streaming media transmission of wireless networks technology has become the research focus, mobile network based on CDMA2000 can meet the requirements of video data bandwidth of real-time transmission. According to the characteristics of mobile wireless networks, and the features of video compression coding,designe a scheme for streaming media transmission, can improve the quality of real-time streaming media transmission greatly. In this paper, choose H.264 as the video compression coding standard, according to RFC3984 protocol, using RTP(Real-time Transport Protocol) packet protocol to packet directly, combine packets or divide a packret to meet the requests of CDMA2000 wireless mobile network transmission. Through the experiment, tested under the conditions of CDMA2000 network, transmit RTP packets of different sizes, come to its effect on average speed,packet loss rate, delay and the image quality. And the conclusion is: by setting the optimal MTU(Maximum Transmission Unit) value, the packet loss rate and transmission rate of streaming media transmission over wireless networks achieve the best value.

Three different kinds of the novel enhanced genetic algorithm procedures including the hybrid genetic algorithm, interval genetic algorithm and hybrid interval genetic algorithm are respectively presented. As the results of the proven systems show, the hybrid genetic algorithm can determines the better optimum design than the traditional optimization algorithms and genetic algorithm. The interval genetic algorithm and hybrid interval genetic algorithm can avoid calculating system slope in traditional interval analysis and determines the optimum interval range of the parameters under allowable corresponding objective error boundary. It is the first time that genetic algorithm has been applied to interval optimization process.

In this paper, two types of optimality criteria for scheduling a single passive sensor in bearings-only tracking are investigated. Firstly, we examine the case of optimising sensor trajectory under a classical control frame work where the optimality criteria are based on Fisher information matrix. A first order numerical algorithm is discussed. Secondly, the sensor scheduling problem is treated as a partially observed Markov decision process where information divergence criterion is ultilised as a reward function. A grid based search strategy is adopted to find feasible sensor courses which can maximise the reward between prior and posterior densities of target state. Technically, accumulated expected reward is evaluated at each grid point to avoid using measurements. The best sensor trajectory is found by selecting those sensor locations of maximum accumulated expected reward in the time period of interest. The performance of these two techniques are compared via computer simulation in a typical single sensor bearings-only tracking scenario used in the literature. Comparison results are discussed accordingly.

The environmental science communities are actively engaged in developing the next generation of large-scale sensor based field observing systems. The challenges that these systems face are listed as below: complexity of sensor data stream processing and effects of web-based real-time data display. In this paper, we utilized the RBNB DataTurbine, an open-source streaming data middleware to improve the reliability of streaming data transportation. By using server push technology, we ensure the stability and effectiveness of web-based streaming data visualization. The results indicate that the system has enhanced the efficiency and performance of field ecological data transportation and visualization. The system is currently used for observation by ecological research scientists at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences.

In this paper, we proposed a semi-distributed transportation and management network for field ecological data collection. We developed the semi distributed transportation network to transmit the real-time video data and real-time flux data in the ChinaFlux [1] project, and utilized the SRB technique to manage the distributed ecological data among different observation sites in the ChinaFlux project. We also used the circular buffer to improve the scalability of the data transportation and management. The results showed that the semi distributed network enhanced the efficiency and performance of field ecological data transportation and management greatly.

The stipulation that measurements are obtained from only a single sensor adds additional complexity to the bearings-only tracking (BOT) problem since target states can then be observed only after a sensor platform maneuver. Because of lack of target knowledge from measurements and highly nonlinear between target state and measurements, filter initialisation is difficult and the consequent tracker instability is an inherent issue. In this paper, we discuss the use of progressive correction technique to improve the extended Kalman filter (EKF) performance for BOT problem and recall a pseudo linear least square (PLLS) filter which is derived from a batch of measurements and does not require initialisation. The performance of both filters are analysed in a benchmark scenario which was used for demonstrating the performance of a range parameterised EKF implemented in the modified polar coordinates (MPC). Simulation results demonstrated the robustness and effectiveness of these simple techniques. Index Terms—Bearings-only tracking, Pseudo linear least square estimator, Filter initialisation, Nonlinear filtering, Pro- gressive correction.

In the field ecological data collection process, lots of heterogeneous data are generated by equipments which are composed of hundreds to thousands of sensors and cameras and it is a big challenge to collect and manage these data. In this paper, we proposed a five layer framework named FEDC which can be used in the field ecological data collection and management. FEDC is a hybrid-distributed framework with some distributed properties, and it also has scalable, real-time and self-adaptive abilities. It successfully used in the ChinaFlux [1] project and the result shows that the FEDC framework has a good performance in managing and transmitting the field ecological data.

We describe a method to analyze materials on a conveyor belt using natural gamma spectra collected with a BGO (bismuth germanate) gamma ray detector, which collects emissions from potassium (K), uranium (U), and thorium (Th) in the materials. A statistical model is proposed based on a Poisson process and an approximate maximum likelihood (ML) technique via the expectation-maximization (EM) algorithm is then used to estimate the amount of each of the three elements in the material. A refinement of the statistical model is used to estimate linear drift in the detector.

Measurements based on a Radio Interferometric Positioning System (RIPS) can determine distance differences between pairs of motes in a wireless sensor network. Such measurements allow one to localise motes in the network when the locations of at least three motes are known. In the 2-dimensional case, a RIPS trilateration approach, which we describe in this paper, appears to be the most efficient way of localising all motes with respect to a set of known anchors. An important issue associated with this method is the sensitivity of the trilateration to RIPS measurement noise. In this paper we present an analytic study of the impact of RIPS measurement noise on the localisation error, the results of which can be used as a guide for choosing RIPS measurements that minimise the localisation error. A simulated example demonstrates the underlying idea, and its effectiveness.

PMHT algorithm, as proposed, promises high performance multi target tracking in clutter with (relatively) modest computational resources. However, when applied to practical target tracking situations, a number of problems need to be overcome. PMHT assumes fixed number of tracks, and furthermore it assumes that all tracks are true tracks. No track quality measure is provided within PMHT to enable false track discrimination. In this paper we add the probability of target existence to PMHT processing, and accommodate a variable number of tracks.

Efficient collaborative self-localisation is a vital component of large scale sensor network applications. This paper presents a computationally efficient algorithm for performing this task using radio interferometric positioning. In this way it is possible to localise the nodes in the network without needing additional sensors. Simulation results show performance that is similar to that obtained with a centralised approach but with significantly lower computation and transmission costs.