Sensor registration using neural networks

One of the major problems in multiple sensor surveillance systems is inadequate sensor registration. We propose a new approach to sensor registration based on layered neural networks. The nonparametric nature of this approach enables many different kinds of sensor biases to be solved. As part of the implementation we develop some modifications to the common network training algorithm to tackle the inherent randomness in all components of the training set     

Railway track inspection using INS-BINS-N

The results of an integrated strap-down inertial system development and tests are presented. The system includes laser gyros and accelerometers and is intended for measuring railway track angular parameters. To raise the accuracy of angles and distance measurements the inertial system is integrated with a GPS receiver and an odometer. Test results showed that BINS-N accuracy is better than the accuracy of an issued system     

Space-time registration of radar and ESM using unscented Kalman filter

Space and time alignments are the prerequisites for the successful fusion of multiple sensors. A space-time registration model is proposed to estimate the system biases and to perform time synchronization together for mobile radar and electronic support measure (ESM) systems. A space-time registration model for radar and ESM is first developed, and an unscented Kalman filter (UKF) is proposed to estimate the space-time biases and target states simultaneously. The posterior Cramer-Rao bounds (PCRBs) are derived for the proposed UKF registration algorithm for ESM detection probability less than or equal to one. Theoretical analyses are performed to evaluate the accuracy and robustness of the proposed method. Computer simulations show that the UKF registration algorithm is indeed effective and robust for different radar and ESM tracking scenarios.     

Vehicular remote tolling services using EGNOS

Future Global Navigation Satellite Systems (GNSS) will significantly improve the performance of current navigation systems, providing new and enhanced capabilities. This will enable the implementation of innovative and advanced services and applications closer to the user's needs. In this framework, the road sector is one of the major potential markets for GNSS applications and, therefore, it is very promising for future EGNOS and GALILEO related applications. Satellite navigation receivers are now commonly installed in cars as a key tool for proving new services to people on the move such as electronic charging, real-time traffic information, emergency calls, route guidance, fleet management, or advance driving assistance systems. Specifically, a very important commercial opportunity is represented by the tolling-related applications. This paper describes the Vehicular Remote Tolling (VeRT) Project sponsored by Galileo Joint Undertaking (GJU) and performed in the frame of the first GJU Call under the EC VI Framework Programme. The overall design of the service provision architecture is also presented in this paper, together with the main topics related to the following activities focused on the service prototype demonstrator development.     

EM-ML algorithm for track initialization using possibly noninformative data

Initializing and maintaining a track for a low observable (LO) (low SNR, low target detection probability and high false alarm rate) target can be very challenging because of the low information content of measurements. In addition, in some scenarios, target-originated measurements might not be present in many consecutive scans because of mispointing, target maneuvers, or erroneous preprocessing. That is, one might have a set of noninformative scans that could result in poor track initialization and maintenance. In this paper an algorithm based on the expectation-maximization (EM) algorithm combined with maximum likelihood (ML) estimation is presented for tracking slowly maneuvering targets in heavy clutter and possibly noninformative scans. The adaptive sliding-window EM-ML approach, which operates in batch mode, tries to reject or weight down noninformative scans using the Q-function in the M-step of the EM algorithm. It is shown that target features in the form of, for example, amplitude information (AI), can also be used to improve the estimates. In addition, performance bounds based on the supplemented EM (SEM) technique are also presented. The effectiveness of new algorithm is first demonstrated on a 78-frame long wave infrared (LWIR) data sequence consisting of an Fl Mirage fighter jet in heavy clutter. Previously, this scenario has been used as a benchmark for evaluating the performance of other track initialization algorithms. The new EM-ML estimator confirms the track by frame 20 while the ML-PDA (maximum likelihood estimator combined with probabilistic data association) algorithm, the IMM-MHT (interacting multiple model estimator combined with multiple hypothesis tracking) and the EVIM-PDA estimator previously required 28, 38, and 39 frames, respectively. The benefits of the new algorithm in terms of accuracy, early detection, and computational load are illustrated using simulated scenarios as well.     

无人机对雷达组网航迹欺骗综述

未来战争对体系作战的需求不仅仅是靠规模"以量取胜",更要有战术上的智谋"以活胜僵"。通过干扰敌方对我方的意图推断,甚至设计出让敌方相信的假意图,能够在双方的博弈中塑造态势,取得先手,从顶层设计和战术策略上掌握主动权。运用无人机对敌方组网雷达进行航迹欺骗就是一种有效的对抗手段,能够在实现对敌探测的同时,消耗敌方雷达组网的计算资源,干扰敌方雷达及幕后指挥员对我方作战意图的推断,同时提高己方态势感知能力和生存力。本文首先介绍了航迹欺骗干扰的概念,并以时间为序,分类梳理了国内外各研究团队在航迹欺骗领域取得的技术成果;然后针对航迹欺骗干扰的特点,详细分析了虚假航迹设计和生成、由不确定性误差导致的估计问题、延时转发策略和同源检验准则、以及动力学约束问题等4个方面的关键问题和技术难点;最后展望了航迹欺骗的应用前景和下一步研究方向。     

强化档案管理 服务学校发展

档案是人们社会活动形成的文、形、声历史真迹材料 ,既记录了人们物质生产发展的过程和人们精神生活的进步状况 ,又是一种重要的信息资源。它具有广泛的社会作用 ,随着信息时代的到来 ,人们对信息的价值日益重视。因此 ,档案的价值也越来越被社会重视。我校从 195 6年建校至今 ,历经四十余载 ,形成和集存了丰富的档案资料 ,这些档案资料是学校的一面镜子 ,一笔财富 ,不仅给人们提供查考和研究之便 ,而且也在学校的建设发展中发挥着重要作用。但我校档案管理目前还处于传统模式 ,现代科学技术手段还是空白 ,随着信息时代和知识经济的来临 ,随…     

JTIDS relative navigation and data registration

The Joint Tactical Information Distribution System (JTIDS), an integrated communication, navigation, and identification system, provides a solution to the critical data registration problem facing the joint US military services today, namely, the establishment, in real-time, of accurately correlated positions and tracks for all friendly, unknown, and hostile targets in an operational area, thus providing the total situation awareness required for tactical and C² operations. The fundamental relationships of JTIDS navigation and the error analysis for target registration and target hand-off in both geodetic and relative grid coordinates are presented. Simulation results are provided for two scenarios to demonstrate the level of improvement that JTIDS navigation can have on situation awareness, target acquisition, and weapon delivery. Specifically, it is shown that accurate data registration can be achieved by as few as two JTIDS members, with or without accurate knowledge of geodetic position     

Architectures and algorithms for track association and fusion

Target tracking using multiple sensors can provide better performance than using a single sensor. One approach to multiple target tracking with multiple sensors is to first perform single sensor tracking and then fuse the tracks from the different sensors. Two processing architectures for track fusion are presented: sensor to sensor track fusion, and sensor to system track fusion. Technical issues related to the statistical correlation between track estimation errors are discussed. Approaches for associating the tracks and combining the track state estimates of associated tracks that account for this correlation are described and compared by both theoretical analysis and Monte Carlo simulations     

Track association and track fusion with nondeterministic targetdynamics

Representative track fusion algorithms and track association metrics are quantitatively compared using a simple linear-Gaussian-Poisson model, under various degrees of nondeterministicity of the target dynamics, i.e., process noises, and of the initial condition uncertainty. Track fusion algorithms are compared using an analytical method, while track association metrics are evaluated by Monte Carlo simulations     

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.     

Clutter map information for data association and track initialization

This paper considers the problem of forming and maintaining tracks when measurements have both uncertain origin and are corrupted by additive sensor noise. The spatial clutter measurement density is assumed nonhomogeneous and known. The PPDA-MAP algorithm provides a set of recursive formulae for data association and probability of target existence, thus enabling automatic track initiation, track maintenance, and track termination. New values for initial probability of target existence for IPDA-type algorithm are also derived. Simulation results compare the performance of IPDA-MAP with IPDA, IMM-PDA, IMM-PDA-MAP, EB-PDA and EB-PDA-MAP in a heavy and nonuniform clutter situation.     

Continuous-time ECV and ECA track filters with colored measurementnoise

Analytical solutions are presented for two continuous-time track filters with correlation in both the measurement errors and the target maneuvers. The measurement error model is a first-order Markov process, characterized by a correlation time (γ) and a position error variance (r). The target motion is described by one of two dynamic models: the exponentially correlated velocity (ECV) model, and the exponentially correlated acceleration (ECA) model. For both models, the steady-state velocity variance exhibits resonant behavior. The analytical solutions include as special cases several previously published results     

Finite data performance of MUSIC and minimum norm methods

In the direction of arrival (DOA) estimation problem, we encounter both finite data and insufficient knowledge of array characterization. It is therefore important to study how subspace-based methods perform in such conditions. We analyze the finite data performance of the multiple signal classification (MUSIC) and minimum norm (min. norm) methods in the presence of sensor gain and phase errors, and derive expressions for the mean square error (MSE) in the DOA estimates. These expressions are first derived assuming an arbitrary array and then simplified for the special case of an uniform linear array with isotropic sensors. When they are further simplified for the case of finite data only and sensor errors only, they reduce to the recent results given previously (1989, 1991). Computer simulations are used to verify the closeness between the predicted and simulated values of the MSE     

Training, career development and registration for safety criticalsoftware systems specialists

A career development program for information systems practitioners currently being used widely by employers in the UK and now becoming available in North America is described. The program, called the Professional Development Scheme (PDS), was developed by the British Computer Society to address the lack of structure and quality control generally present in the way computing professionals were being trained. The performance standards underpinning the program (The British Computer Society Industry Structure Model) have been thoroughly updated and now include material specific to the development, maintenance, and management of software for safety-critical applications. The use of the program for this purpose and potential developments in the field of training and registration for safety-critical software specialists are discussed