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     

Data fusion and tracking using HMMs in a distributed sensor network

This work deals with the problem of multiple target tracking, from the measurements made on a field of passive sonars activated by an active sonar (multistatic network). The difficulties encountered then are of two kinds: each sensor alone does not provide full observability of a target, and multiple, possibly maneuvering targets moving in a cluttered environment must be dealt with. The algorithm presented here is based on a discrete Markovian modelization of the targets evolution in time. It starts with a fusion of the detections obtained at each measurement time. Tracking and target motion analysis (TMA) are next achieved thanks to dynamic programming (DP). This approach leads to multiple and maneuvering target tracking, with few assumptions; for instance, the use of deterministic target state models are avoided. Simulation results are presented and discussed.     

A multi-rate sensor fusion approach using information filters for estimating aero-engine performance degradation

Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter(KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However, it is hard to achieve state estimation using various kinds of sensor measurements at the same sampling rate due to a complex network and physical characteristic differences between sensors, especially in an advanced multisensor architecture. For this purpose, a multi-rate sensor fusion using the information filtering approach is proposed based on the square-root cubature rule, which is called Multi-rate Squareroot Cubature Information Filter(MSCIF) to track engine performance degradation. Soft measurement synchronization of the MSCIF is designed to provide a sensor fusion condition for multiple sampling rates of measurement, and a fault sensor is isolated by maximum likelihood validation before state estimation. The contribution of this paper is to supply a novel multi-rate informationfilter approach for sensor fault tolerant health estimation of an aero-engine in a multi-sensor system. Tests are conducted for aero-engine performance degradation estimation with multiple sampling rates of sensor measurement on both digital simulation and semi-physical experiment.Experimental results illustrate the superiority of the proposed algorithm in terms of degradation estimation accuracy and robustness to sensor failure in a multi-sensor system.     

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     

Decision fusion rules in multi-hop wireless sensor networks

The decision fusion problem for a wireless sensor network (WSN) operating in a fading environment is considered. In particular, we develop channel-aware decision fusion rules for resource-constrained WSNs where binary decisions from local sensors may need to be relayed through multi-hop transmission in order to reach a fusion center. Each relay node employs a binary relay scheme whereby the relay output is inferred from the channel impaired observation received from its source node. This estimated binary decision is subsequently transmitted to the next node until it reaches the fusion center. Under a flat fading channel model, we derive the optimum fusion rules at the fusion center for two cases. In the first case, we assume that the fusion center has knowledge of the fading channel gains at all hops. In the second case, we assume a Rayleigh fading model, and derive fusion rules utilizing only the fading channel statistics. We show that likelihood ratio (LR) based optimum decision fusion statistics for both cases reduce to respective simple linear test statistics in the low channel signal-to-noise ratio (SNR) regime. These suboptimum detectors are easy to implement and require little a priori information. Performance evaluation, including a study of the robustness of the fusion statistics with respect to unknown system parameters, is conducted through simulations.     

A Boolean algebra approach to multiple sensor voting fusion

A technique for integrating multiple-sensor data using a voting fusion process that combines the individual sensor outputs is described. An important attribute of the method is the automatic confirmation of the target by the fusion processor without the need to explicitly determine which sensors and what level of sensor participation are involved. A three-sensor system, with multiple confidence levels in each sensor, is discussed to illustrate the approach. Boolean algebra is used to derive closed-form expressions for the multiple sensor-system detection probability and false-alarm probability. Procedures for relating confidence levels to detection and false alarm probabilities are described through an example. The hardware implementation for the sensor system fusion algorithm is discussed     

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.     

基于喘振信息数据融合的发动机温度传感器

针对热电偶（阻）型温度传感器呈现较强的惯性，提出一种基于矩阵奇异分解的传感器数据融合方法，将喘振信息与温度传感器输出信号相融合，分别消除了发动机进口温度T1约1．2％和涡轮后燃气温度L约0．8％的静态误差；同时，还提出了利用喘振信息消除温度传感器动态误差的技术，补偿了热电偶（阻）的惯性延迟。实验和仿真结果表明，该数据融合方法和动态补偿技术不仅算法简单有效，而且温度补偿准确，提高了发动机电子调节器工作的可靠件．     

Adaptive data fusion and sensor management for military applications

The military typically operates in demanding, dynamic, semi-structured and large-scale environments. This reality makes it difficult to detect, track, recognize/classify, and response to all entities within the volume of interest, thus increasing the risk of late (or non-) response to the ones that pose actual threat. A key challenge facing the military operators, in these contexts, is the focus of attention and effort, that is, how to make the most effective use of the available but scarce sensing and processing resources to gather the most relevant information from the environment and fuse it in the most efficient way. Adaptive Data Fusion and Sensor Management can aid this information gathering and fusion processes by automatically allocating, controlling, and coordinating the sensing and the processing resources to meet mission requirements. This paper presents results of a project initiated by Defence R&D Canada – Valcartier that aims at defining, developing, and demonstrating adaptive data fusion and sensor management concepts for distributed military surveillance operations.     

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.     

基于SPSO-SVR的融合航空发动机传感器故障诊断

针对航空发动机常见的传感器故障问题, 提出了一种利用改进的粒子群算法训练支持向量回归机, 并利用融合机制将其应用于传感器故障诊断.论述了用一簇支持向量回归机(SVR)预测器对传感器进行实时检测, 通过逻辑判断机制隔离故障传感器, 并且依据剩余的无故障传感器信息实现信号重构.以某型航空发动机传感器在其整个工作范围内受到的冲击、偏置和漂移故障为例, 验证了基于自协调粒子群优化支持向量回归机(SPSO-SVR)算法的融合诊断机制对传感器单一故障和多重故障具有较高的精度和计算效率.      

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.     

Bomb damage assessment using a dispensed sensor projectile

The problem of obtaining images of a target site after munition impact is approached by releasing a small projectile equipped with a camera from a dropped munition. A ballute is deployed from the sensor projectile shortly after release from the munition. This type of system is capable of viewing munition impact and subsequent target effects over a wide variety of conditions and offers the possibility of real-time battle damage assessment (BDA). However, fundamental limits exist on the duration that the camera is able to view the target after impact for a particular required separation distance between the sensor projectile and the target at impact and the field of regard (FOR) of the camera. Munition release altitude and velocity significantly affect these fundamental limits. Optimal performance is attained under high altitude and low speed munition drop conditions. Basic characteristics of the camera projectile also significantly influence system performance. Maximum target view time is attained with a low weight high drag configuration., To reduce the maximum acceleration experienced by the sensor projectile, a small delay time between the release of the small sensor projectile from the munition and inflation of ballute is required.     

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