Comparison of two measurement fusion methods forKalman-filter-based multisensor data fusion

Currently there exist two commonly used measurement fusion methods for Kalman-filter-based multisensor data fusion. The first (Method I) simply merges the multisensor data through the observation vector of the Kalman filter, whereas the second (Method II) combines the multisensor data based on a minimum-mean-square-error criterion. This paper, based on an analysis of the fused state estimate covariances of the two measurement fusion methods, shows that the two measurement fusion methods are functionally equivalent if the sensors used for data fusion, with different and independent noise characteristics, have identical measurement matrices. Also presented are simulation results on state estimation using the two measurement fusion methods, followed by the analysis of the computational advantages of each method     

New track correlation algorithms in a multisensor data fusion system

In order to resolve the problem of track-to-track association in a distributed multisensor situation, this paper presents independent and dependent sequential track correlation algorithms based on Singer's and Bar-Shalom's algorithms. Based on sequential track correlation algorithm, the restricted and attenuation memory track correlation algorithms and sequential classic assignment rules are proposed. In this paper, these algorithms are described in detail. Then, the track correlation mass and multivalency processing methods are discussed as well. Finally, simulations are designed to compare the correlation performance of these algorithms with that of Singer's and Bar-Shalom's algorithms. The simulation results show that the performance of these algorithms proposed here is much better than that of the classical methods under the environments of dense targets, interfering, noise, track cross, and so on. Under the above situations, their correct correlation ratio is improved about 69 percent over the classical methods     

An efficient algorithm for multisensor track fusion

An efficient algorithm for track-to-track fusion by incorporating cross-covariance between tracks created by dissimilar sensors is described. An analytical solution of this problem is complicated if cross-correlation between sensors tracking the same target is taken into account. An explicit solution of the cross-covariance matrix at steady state is derived in terms of an integral. It is shown that solution of this integral involves inversion of a matrix whose elements are functions of parameters of individual trackers. Structure of this matrix is analyzed. An efficient analytical solution for inversion of this matrix is obtained. For fusion of similar sensors, it is shown that this matrix is reduced to the Routh-Hurwitz matrix which arises in the study of steady state stability of linear systems. Numerical results showing the amount of reduction of fused track covariance by taking into account the effects of cross-correlation between candidate tracks for fusion is also presented     

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     

Optimum multisensor fusion of correlated local decisions

A distributed detection system consisting of a number of local detectors and a fusion center is considered. Each detector makes a decision for the underlying binary hypothesis testing problem based on its own observation and transmits its decision to the fusion center where the global decision is derived. The local decision rules are assumed to be given, but the local decisions are correlated. The correlation is generally characterized by a finite number of conditional probabilities. The optimum decision fusion rule in the Neyman-Pearson sense is derived and analyzed. The performance of the distributed detection system versus the degree of correlation between the local decisions is analyzed for a correlation structure that can be indexed by a single parameter. System performance as well as the performance advantage of using a larger number of local detectors degrade as the degree of correlation between local decisions increases     

Covariance control for multisensor systems

As the profusion of different sensors improves the capabilities of tracking platforms, tracking objectives can move from simply trying to achieve the most with a limited sensor suite to developing the ability to achieve more specific tracking goals, such as reducing the uncertainty in a target estimate enough to accurately fire a weapon at a target or to ensure that a mobile robot does not collide with an obstacle. Multisensor manager systems that balance tracking performance with system resources have traditionally been ill-suited for achieving such specific control objectives. This work extends the methods developed in single-sensor management schemes to a multisensor application using an approach known as covariance control, which selects sensor combinations based on the difference between the desired covariance matrix and that of the predicted covariance of each target.     

Optical fiber data bus modulation techniques for military avionics

In this paper, we propose several modulation techniques for use in military avionics optical fiber data buses, namely, Extended Manchester II Bi-Phase Coding with Beginning-Stopping Flags, Partial Trilevel Manchester II Bi-Phase Coding, Four-ary Pulse Width Modulation, Two-ary Pulse Width Modulation with Amplitude-Distinguished Sync Field, Two-ary Message Pulse Width Modulation and Two-ary Sync Pulse Amplitude Modulation, respectively. Compared with an existing modulation scheme of MIL-STD-1773 avionics data buses, the proposed techniques can be effectively used to overcome the difficulty in recognizing the correct operation states of an active transmitter at the output of optical receivers. The feasibility of proposed modulation schemes are discussed, and their performance is also evaluated     

Component recognition of ISAR targets via multimodal feature fusion

Inverse Synthetic Aperture Radar(ISAR) images of complex targets have a low Signalto-Noise Ratio(SNR) and contain fuzzy edges and large differences in scattering intensity, which limits the recognition performance of ISAR systems. Also, data scarcity poses a greater challenge to the accurate recognition of components. To address the issues of component recognition in complex ISAR targets, this paper adopts semantic segmentation and proposes a few-shot semantic segmentation framework fusing multimodal features. The scarcity of available data is mitigated by using a two-branch scattering feature encoding structure. Then, the high-resolution features are obtained by fusing the ISAR image texture features and scattering quantization information of complexvalued echoes, thereby achieving significantly higher structural adaptability. Meanwhile, the scattering trait enhancement module and the statistical quantification module are designed. The edge texture is enhanced based on the scatter quantization property, which alleviates the segmentation challenge of edge blurring under low SNR conditions. The coupling of query/support samples is enhanced through four-dimensional convolution. Additionally, to overcome fusion challenges caused by information differences, multimodal feature fusion is guided by equilibrium comprehension loss. In this way, the performance potential of the fusion framework is fully unleashed, and the decision risk is effectively reduced. Experiments demonstrate the great advantages of the proposed framework in multimodal feature fusion, and it still exhibits great component segmentation capability under low SNR/edge blurring conditions.     

Optimal m-ary data fusion with distributed sensors

An optimal data fusion rule is derived for an m-ary detection problem. Each detector determines a local decision using a local decision rule and transmits the local decision to the fusion center. Considering the reliability of local detectors, local decisions are combined to produce the final decision. In this study, based upon the maximum posterior probability concept, optimal decision rules for m-ary detection problems are proposed for the local detector and the data fusion center     

编队信息融合软件测试

介绍了依据软件测试技术理论,结合编队信息融合软件的具体情况,对其进行自动化测试系统设计,尤其是新开发了一套规范的而向对象的想定描述语言和可以独立于测试系统的测试用例,最后对测试的实施作了介绍.     

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     

数据融合技术在空气动力学研究中的应用

气动力数据通常通过风洞试验、数值计算和飞行试验三种途径获得,三者各有优缺点,为了得到更加完善准确的气动数据,可采用数据融合技术对不同来源的数据进行深加工和利用。针对数据融合技术在空气动力学中的应用进行讨论和探索,首先介绍了数据融合技术在空气动力学中的应用背景、发展现状及数据融合的基本思想,此后综合提出了建立气动数据融合准则的基本思路和两种具有应用价值的融合算法:基于不确定度的数据融合方法和基于气动力建模的数据融合方法,并给出了部分研究应用结果。最后,文章对气动数据融合技术在气动领域的应用前景进行了展望。研究发现,采用数据融合技术后,可以进一步提高气动数据的完整性和准确性,但由于气动数据融合工作不仅需要进行算法研究,同时还需要工作人员的大量经验,融合结果包含较多的人为因素,因此具体采用何种融合方法要根据具体情况而定。     

SAR data focusing using seismic migration techniques

The focusing of synthetic-aperture-radar (SAR) data using migration techniques quite similar to those used in geophysics is treated. The algorithm presented works in the ω-k_x domain. Because time delays can be easily accommodated with phase shifts that increase linearly with ω, range migration poses no problem. The algorithm is described in plane geometry first, where range migration and phase history can be exactly matched. The effects of the sphericity of the Earth, of the Earth's rotation, and of the satellite trajectory curvature are taken into account, showing that the theoretically achievable spatial resolution is well within the requirements of present day and near future SAR missions. Terrestrial swaths as wide as 100 km can be focused simultaneously with no serious degradation. The algorithm has been tested with synthetic data, with Seasat-A data, and with airplane data (NASA-AIR). The experimental results fully support the theoretical analysis     

Multi-sensor optimal data fusion for INS/GPS/SAR integrated navigation system

INS/GPS/SAR integrated navigation system represents the trend of next generation navigation systems with the high performance of independence, high precision and reliability. This paper presents a new multi-sensor data fusion methodology for INS/GPS/SAR integrated navigation systems. This methodology combines local decentralized fusion with global optimal fusion to enhance the accuracy and reliability of integrated navigation systems. A decentralized estimation fusion method is established for individual integrations of GPS and SAR into INS to obtain the local optimal state estimations in a parallel manner. A global optimal estimation fusion theory is studied to fuse the local optimal estimations for generating the global optimal state estimation of INS/GPS/SAR integrated navigation systems. The global data fusion features a method of variance upper finiteness and a method of variance upper bound to ensure that the global optimal state estimation can be achieved under a general condition. Experimental results demonstrate that INS/GPS/SAR integrated navigation systems achieved by using the proposed methodology have a better performance than INS/GPS integrated systems.     

Automatic target recognition using enhanced resolution SAR data

Using advanced technology, a new automatic target recognition (ATR) system has been developed that provides significantly improved target recognition performance compared with ATR systems that use conventional synthetic aperture radar (SAR) image-processing techniques. This significant improvement in target recognition performance is achieved by using a new superresolution image-processing technique that enhances SAR image resolution (and image quality) prior to performing target recognition. A computationally efficient two-level implementation of a template-based classifier is used to perform target recognition. The improvement in target recognition performance achieved using superresolution image processing in this new ATR system is quantified