Smart Sensor Web: Web-based exploitation of sensor fusion forvisualization of the tactical battlefield

Smart Sensor Web (SSW) is a recent DUSD (S&T) initiative inspired by extraordinary technological advances in sensors and microelectronics and by the emergence of the Internet as a real time communication tool. The overall vision for SSW is an intelligent, Web-centric distribution and fusion of sensor information that provides greatly enhanced situational awareness, on demand, to warfighters at lower echelons. Emphasis is on multi-sensor fusion of large arrays of local sensors, joined with other assets, to provide real-time imagery, weather, targeting information, mission planning, and simulations for military operations on land, sea, and air. This paper gives an overview of this new initiative, highlights some of the technology challenges in sensor/information Fusion, and presents a program approach for near-term demonstrations and long-term solutions, involving the DoD, National Labs, commercial industry, and academia     

Fuel cells for tactical battlefield power

Basic fuel cell concepts are presented. The various types of fuel cells used by the US Army are described. The technological problems encountered are discussed     

Performance limits of track-to-track fusion versus centralized estimation: theory and application [sensor fusion]

Track-to-track fusion is an important part in distributed multisensor-multitarget tracking. The centralized and distributed tracking configurations were studied in (H.Chen et al., Proc. of SPIE Conf. on Signal and Data Processing of Small Targets, vol. 4048, 2000) using simulated air-to-air scenarios, and in (K.C. Chang, et al, IEEE Transact. on Aerospace and Electronic Systems, vol. 33, no. 4, pp. 1271-1276, 1997) with analytical results based on /spl alpha/-/spl beta/ filters. The current work generalizes the results in the latter to the cases with more than 2 sensors. As the number of sensors increases, the performance of the distributed tracker is shown to degrade compared with the centralized estimation even when the optimal track-to-track fusion is used. An approximate track-to-track fusion is presented and compared with the optimal track-to-track fusion with performance curves for various numbers of sensors. These performance curves can be used in designing a fusion system where certain trade-offs need to be considered. Finally, these results are compared with simulation results for a realistic air-to-air encounter scenario.     

MMWR/FLIR/ATR sensor fusion: proof of concept

To improve the relocatable target capabilities of strategic aircraft, a sensor fusion concept using a millimeter-wave radar (MMWR) and a forward-looking infrared (FLIR) system providing inputs to an auto target recognizer (ATR) has been developed. To prove this concept, a cooperative research effort is being conducted by a group of industry leaders in bomber avionics, MMWR, and ATR technologies. The author discusses the concept and the plan developed to test, evaluate, and demonstrate the expected performance     

Distributed sensor data fusion with binary decision trees

A distributed sensor object recognition scheme that uses object features collected by several sensors is presented. Recognition is performed by a binary decision tree generated from a training set. The scheme does not assume the availability of any probability density functions, thus it is practical for nonparametric object recognition. Simulations have been performed for Gaussian feature objects, and some of the results are presented     

Joint sensor registration and track-to-track fusion for distributed trackers

Sensor registration deals with the correction of registration errors and is an inherent problem in all multisensor tracking systems. Traditionally, it is viewed as a least squares or a maximum likelihood problem independent of the fusion problem. We formulate it as a Bayesian estimation problem where sensor registration and track-to-track fusion are treated as joint problems and provide solutions in cases 1) when sensor outputs (i.e., raw data) are available, and 2) when tracker outputs (i.e., tracks) are available. The solution to the latter problem is of particular significance in practical systems as band limited communication links render the transmission of raw data impractical and most of the practical fusion systems have to depend on tracker outputs rather than sensor outputs for fusion. We then show that, under linear Gaussian assumptions, the Bayesian approach leads to a registration solution based on equivalent measurements generated by geographically separated radar trackers. In addition, we show that equivalent measurements are a very effective way of handling sensor registration problem in clutter. Simulation results show that the proposed algorithm adequately estimates the biases, and the resulting central-level trucks are free of registration errors.     

Smart card information and operations using biometrics

Nowadays many systems need a portable media to store some sensible data, such as smart cards. The information can be protected by the user with his Personal Identification Number (PIN), or through biometrics. Unfortunately, there is not a smart card today that can verify the biometric template inside it, performing this task in the terminal. The author has developed the algorithms and data structures needed to solve this problem. Therefore, he has created a smart card with user biometric authentication, based on an Open Platform smart card (in this case, a JavaCard). To achieve these results, different biometric techniques have been studied: speaker verification, hand geometry and iris recognition. Experimental results are given to show the viability of the prototype developed     

社交网络媒体:航空业的新战场

<正>先让我们看一则新闻:麦当劳近年来将主要的市场转向年轻人群体。在2009夏季促销调查中麦当劳发现,年轻人尤其是大学生目前的主要业余时间都用在了网络交流上,而人人网     

Efficient multisensor fusion using multidimensional dataassociation

We present the development of a multisensor fusion algorithm using multidimensional data association for multitarget tracking. The work is motivated by a large scale surveillance problem, where observations from multiple asynchronous sensors with time-varying sampling intervals (electronically scanned array (ESA) radars) are used for centralized fusion. The combination of multisensor fusion with multidimensional assignment is done so as to maximize the “time-depth” in addition to “sensor-width” for the number S of lists handled by the assignment algorithm. The standard procedure, which associates measurements from the most recently arrived S-1 frames to established tracks, can have, in the case of S sensors, a time-depth of zero. A new technique, which guarantees maximum effectiveness for an S-dimensional data association (S⩾3), i.e., maximum time-depth (S-1) for each sensor without sacrificing the fusion across sensors, is presented. Using a sliding window technique (of length S), the estimates are updated after each frame of measurements. The algorithm provides a systematic approach to automatic track formation, maintenance, and termination for multitarget tracking using multisensor fusion with multidimensional assignment for data association. Estimation results are presented for simulated data for a large scale air-to-ground target tracking problem     

Distributed decision fusion using empirical estimation

The problem of optimal data fusion in multiple detection systems is studied in the case where training examples are available, but no a priori information is available about the probability distributions of errors committed by the individual detectors. Earlier solutions to this problem require some knowledge of the error distributions of the detectors, for example, either in a parametric form or in a closed analytical form. Here we show that, given a sufficiently large training sample, an optimal fusion rule can be implemented with an arbitrary level of confidence. We first consider the classical cases of Bayesian rule and Neyman-Pearson test for a system of independent detectors. Then we show a general result that any test function with a suitable Lipschitz property can be implemented with arbitrary precision, based on a training sample whose size is a function of the Lipschitz constant, number of parameters, and empirical measures. The general case subsumes the cases of nonindependent and correlated detectors.     

A new SINS/GPS sensor fusion scheme for UAV localization problem using nonlinear SVSF with covariance derivation and an adaptive boundary layer

The state estimation strategy using the smooth variable structure filter(SVSF) is based on the variable structure and sliding mode concepts. As presented in its standard form with a fixed boundary layer limit, the value of the boundary layer width is not precisely known at each step and may be selected based on a priori knowledge. The boundary layer width reflects the level of uncertainty in the model parameters and disturbance characteristics, where large values of the boundary layer width lead to robustness without optimality and small values of the boundary layer width provide optimality with poor robustness. As a solution and to overcome these limitations, an adaptive smoothing boundary layer is required to achieve greater robustness and suitable accuracy.This adapted value of the boundary layer width is obtained by minimizing the trace of the a posteriori covariance matrix. In this paper, the proposed new approach will be considered as another alternative to the extended Kalman filters(EKF), nonlinear H1 and standard SVSF-based data fusion techniques for the autonomous airborne navigation and self-localization problem. This alternative is based on strapdown inertial navigation system(SINS) and GPS data using the nonlinear SVSF with a covariance derivation and adaptive boundary layer width.Furthermore, the full mathematical model of the SINS/GPS navigation system considering the unmanned aerial vehicle(UAV) position, velocity and Euler angle as well as gyro and accelerometer biases will be used in this paper to estimate the airborne position and velocity with better accuracy.     

Multiple target angle tracking using sensor array outputs

The use of the output of an array of sensors to track multiple independently moving targets is reported. The output of each sensor in the array is the sum of signals received from each of the targets. The results of direction-of-arrival estimation by eigenvalue analysis are extended to derive a recursive procedure based on a matrix quadratic equation. The solution of this matrix quadratic equation is used to provide updated target positions. A linear approximation method for estimating the solution of the matrix equation is presented. The algorithm is demonstrated by the simulated tracking of two targets. The main advantage of the algorithm is that a closed-form solution for updating the target angle estimates has been obtained. Also, its application is straightforward, and the data association problem due to uncertainty in the origin of the measurements is avoided. However, it requires the inversion of an N×N as well as other linear operations, so that the computational burden becomes substantial as N becomes very large     

Detection of satellite attitude sensor faults using the UKF

A novel fault detection (FD) method for nonlinear systems using the residuals generated by the unscented Kalman filter (UKF) is proposed. The errors of the UKF are derived and sufficient conditions for the convergence of the UKF are presented. As the local approach is a powerful statistical technique for detecting changes in the mean of a Gaussian process, it is used to devise a hypothesis test to detect faults from residuals obtained from the UKF. Further, it is demonstrated that the selection of a sample number is important in improving the performance of the local approach. To illustrate the implementation and performance of the proposed technique, it is applied to detect sensor faults in the measurement of satellite attitude.     

Flow visualization study of an airship model using a water towing tank

The work presented investigates the aerodynamic drag and flow physics of an airship configuration. The investigation is carried out experimentally in a water towing tank. The purpose of this project is to improve the understanding of the aerodynamic characteristics of an airship through quantitative and qualitative approaches. Qualitative measurements of integral drag force and moment are measured in the presence of a crosswind. Flow visualization using colored and fluorescent dyes has been applied to improve knowledge of the aerodynamics of airship configurations with an emphasis on the boundary layer separation, transition and hull–appendage interference effects.     

图像融合技术检测低可观测目标评述

阐明了利用图像信息空间覆盖范围大的特点,运用图像融合的手段来对低可观测目标进行检测.通过综合处理来自多个传感器图像包含的检测对象信息、环境特性信息、运动信息、空间信息、时间信息,获得的融合信息包含任何单一传感器无法提供的信息,进而提高检测性能.扼要介绍了像素层图像融合、特征层图像融合以及符号层图像融合的基本概念.列举了图像融合技术一些应用实例,阐述了用图像融合技术检测低可观测目标的优点.着重强调了检测低可观测目标需要进一步研究的问题.     

Data fusion of multiradar system by using genetic algorithm

Detection system with distributed sensors and data fusion. are increasingly being used by surveillance systems. There has been a great deal of theoretical study on decentralized detection networks composed of identical or non-identical sensors. To solve the resulting nonlinear system, exhaustive search and some crude approximations are adopted. However, those methods often cause either the system to be insensitive to some parameters or the suboptimal results. In this paper, a novel flexible genetic algorithm is investigated to obtain the optimal results on constant false alarm rate data fusion. Using this approach, all system parameters are directly coded in decimal chromosomes and they can be optimized simultaneously. The simulation results show that adopting the proposed approach, one can achieve better performances than the reported methods and results     

Detection and diagnosis of sensor and actuator failures using IMMestimator

An approach to detection and diagnosis of multiple failures in a dynamic system is proposed. It is based on the interacting multiple-model (IMM) estimation algorithm, which is one of the most cost-effective adaptive estimation techniques for systems involving structural as well as parametric changes. The proposed approach provides an integrated framework for fault detection, diagnosis, and state estimation. It is able to detect and isolate multiple faults substantially more quickly and more reliably than many existing approaches. Its superiority is illustrated in two aircraft examples for single and double faults of both sensors and actuators, in the forms of “total”, “partial”, and simultaneous failures. Both deterministic and random fault scenarios are designed and used for testing and comparing the performance fairly. Some new performance indices are presented. The robustness of the proposed approach to the design of model transition probabilities, fault modeling errors, and the uncertainties of noise statistics are also evaluated     

Reconfigurable NDI controller using inertial sensor failuredetection & isolation

A modified derivation of nonlinear dynamic inversion provides the theoretical underpinnings for a reconfigurable control law for aircraft that have suffered combinations of actuator failures, missing effector surfaces, and aerodynamic changes. The approach makes use of acceleration feedback to extract information pertaining to any aerodynamic change and thus does not require a complete aerodynamic model of the aircraft. The control law does require feedback of effector positions to accommodate actuator dynamics. Both accelerometer and rate gyro failure detection and isolation (FDI) systems are implemented, allowing up to three independent failures for each FDI system as long as they are in different axes. Nonlinear simulation results show that the FDI systems improve the robustness to accelerometer/rate gyro uncertainties. An advanced tailless aircraft model is used to demonstrate the concepts. The simulation includes accelerometer and rate gyro noise and bias, failures due to accelerometers, rate gyros, and actuators, and modeled missing surfaces that cause airplane aerodynamic changes     

DDQNC-P: A framework for civil aircraft tactical synergetic trajectory planning under adverse weather conditions

Adverse weather during aircraft operation generates more complex scenarios for tactical trajectory planning, which requires superior real-time performance and conflict-free reliability of solving methods. Multi-aircraft real-time 4D trajectory planning under adverse weather is an essential problem in Air Traffic Control (ATC) and it is challenging for the existing methods to be applied effectively. A framework of Double Deep Q-value Network under the Critic guidance with heuristic Pairing (DDQNC-P) is proposed to solve this problem. An Agent for two aircraft synergetic trajectory planning is trained by the Deep Reinforcement Learning (DRL) model of DDQNC,which completes two aircraft 4D trajectory planning tasks preliminarily under dynamic weather conditions. Then a heuristic pairing algorithm is designed to convert the multi-aircraft synergetic trajectory planning into multi-time pairwise synergetic trajectory planning, making the multiaircraft trajectory planning problem processable for the trained Agent. This framework compresses the input dimensions of the DRL model while improving its generalization ability significantly. Substantial simulations with various aircraft numbers, weather conditions, and airspace structures were conducted for performance verification and comparison. The success rate of conflict-free trajectory resolution reached 96.56%with an average calculation time of 0.41 s for 350 4D trajectory points per aircraft, finally confirming its applicability to make real-time decision-making support for controllers in real-world ATC systems.     

Angular velocity determination of spinning solar sails using only a sun sensor

The direction of the sun is the easiest and most reliable observation vector for a solar sail running in deep space exploration. This paper presents a new method using only raw measurements of the sun direction vector to estimate angular velocity for a spinning solar sail. In cases with a con-stant spin angular velocity, the estimation equation is formed based on the kinematic model for the apparent motion of the sun direction vector;the least-squares solution is then easily calculated. A performance criterion is defined and used to analyze estimation accuracy. In cases with a variable spin angular velocity, the estimation equation is developed based on the kinematic model for the apparent motion of the sun direction vector and the attitude dynamics equation. Simulation results show that the proposed method can quickly yield high-precision angular velocity estimates that are insensitive to certain measurement noises and modeling errors.