Spatio-temporal pattern recognition using hidden Markov models

A spatio-temporal method for identifying objects contained in an image sequence is presented. The Hidden Markov Model (HMM) technique is used as the classification algorithm, making classification decisions based on a spatio-temporal sequence of observed object features. A five class problem is considered. Classification accuracies of 100% and 99.7%, are obtained for sequences of images generated over two separate regions of viewing positions. HMMs trained on image sequences of the objects moving in opposite directions showed a 98.1% successful classification rate by class and direction of movement. The HMM technique proved robust to image corruption with additive correlated noise and had a higher accuracy than a single-look nearest neighbor method. A real image sequence of one of the objects used was successfully recognized with the HMMs trained on synthetic data. This study shows the temporal changes that observed feature vectors undergo due to object motion hold information that can yield superior classification accuracy when compared with single-frame techniques     

一种基于证据距离的多分类器差异性度量

 多分类器系统因其能够显著提升分类精度而引发了广泛关注。多分类器系统中各子分类器间的差异性是提升融合分类精度的先决条件。提出了一种基于证据距离的分类器系统差异性度量,同时基于该度量提出一种多分类器系统构造方法。综合了既有差异性度量、所提新差异性度量以及在训练样本集上的分类性能等多个指标,实现了多分类器系统的有效构造。实验结果表明,所提差异性度量及多分类器系统构造方法是合理的,能有效提升融合分类精度。     

证据冲突下自适应融合目标识别算法

 利用证据理论对空中目标识别系统的观测信息融合时,Dempster规则对低冲突信息的融合结果较为理想,但无法对高冲突信息有效融合。Dubois &; Prade(DP)规则及证据折扣法可对高冲突信息进行合理融合。为使不同融合方法发挥各自优势,提出一种自适应融合算法。首先将矛盾因子和证据距离两者结合以更全面地表示证据冲突程度,当冲突较小时,选用Dempster规则,反之,根据冲突的具体情况选择使用DP规则或证据折扣法。通过目标识别实验对多种算法进行了对比,表明本文算法既能对高冲突证据进行合理融合,又能使融合结果快速收敛,可以有效地提高识别速度及正确率。     

无人车基于激光雷达/视觉的目标体积自动测量方法

近些年，基于激光雷达和视觉的目标感知在无人系统中得到了广泛应用。目标的体积测量在很多应用场景可以发挥极其重要的作用，然而对识别感知目标的体积测量，目前尚无大量研究。首次提出了一种基于激光雷达/视觉的无人车目标体积自动测量方法，实现了无人车与目标体积测量功能的结合。通过在LeGO-LOAM算法中加入点云畸变补偿，相较于原始LeGO-LOAM算法，无人车在高速情况下的构图精度得到提升；通过将激光雷达与视觉进行深度融合，实现了目标的自动识别与全局定位；通过基于平面拟合的地面分割与欧式聚类，实现了目标点云轮廓的实时获取；通过设计一种基于切片法的不规则物体体积测量方法，实现了无人车在运动情况下对目标体积的自动估计。最终，分别通过Gazebo仿真和实际试验验证了算法的有效性。试验结果表明，所提算法在无人车运动的情况下对静态目标物的实时体积测量精度优于3%，具有较好的工程应用价值。     

De-combination of belief function based on optimization

In the theory of belief functions, the evidence combination is a kind of decision-level information fusion. Given two or more Basic Belief Assignments(BBAs) originated from different information sources, the combination rule is used to combine them to expect a better decision result. When only a combined BBA is given and original BBAs are discarded, if one wants to analyze the difference between the information sources, evidence de-combination is needed to determine the original BBAs. Evidence d...     

Softmax分类器深度学习图像分类方法应用综述

基于深度学习的人工智能图像分类方法研究是当前计算机视觉领域的研究热点。面向深度学习中的Softmax图像分类方法，首先回顾了图像分类技术的发展历程，接着介绍了图像识别技术中的分类器，并解释了Softmax回归函数的分类实现原理。基于Softmax回归分类器的应用，详细阐述了多种图像分类技术，具体包括浅层神经网络、深度置信网络、深度自编码器和卷积神经网络。同时，对比介绍了各种级联模型的具体结构、训练方法、实际应用、分类效果以及优缺点。最后，从Softmax回归分类器、深度学习网络模型和高维数据分类三个方面对基于Softmax回归分类器的深度学习模型在图像分类方面的发展与应用前景进行了展望。     

基于空间多特征综合推理的航迹航路关联

针对航迹分类问题,研究了基于空间多特征的综合推理在航路判读中的应用。首先根据空管系统对航路以及飞机飞行的要求,对航迹航路相关问题进行建模。然后根据已知的传感器系统输出的目标特性(位置,航向)与已知的多个航路信息分别进行相关度计算,构造基本信任函数,通过对其融合,得到目标单特征识别结果。其中,通过合理地引入复合类,实现了对目标类别的广义信任分类。建立了多特征折扣融合算法,对多特征基本信任函数进行折扣后再融合,得到目标多特征识别结果。仿真结果以及空管实际数据测试表明:该算法不仅可以实现航迹分类,同时可以有效地降低分类的错误率。     

Target classification approach based on the belief function theory

A theoretical framework is presented for target classification based on the belief theory on the continuous space. The proposed approach is applicable when class-conditioned densities of feature/attribute measurements are known only partially, as subjective models of a potential "betting" behaviour. Prior class probabilities may also be unknown. Numerical examples are provided to illustrate how the proposed approach is more cautious in decision making and produces very different results from those obtained using the Bayesian classifier.     

Support vector machines for SAR automatic target recognition

Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc, are receiving more and more attention in the literature. A real application of SVMs for synthetic aperture radar automatic target recognition (SAR/ATR) is presented and the result is compared with conventional classifiers. The SVMs are tested for classification both in closed and open sets (recognition). Experimental results showed that SVMs outperform conventional classifiers in target classification. Moreover, SVMs with the Gaussian kernels are able to form a local “bounded” decision region around each class that presents better rejection to confusers     

A novel approximation of basic probability assignment based on rank-level fusion

Belief functions theory is an important tool in the field of information fusion. However, when the cardinality of the frame of discernment becomes large, the high computational cost of evidence combination will become the bottleneck of belief functions theory in real applications. The basic probability assignment (BPA) approximations, which can reduce the complexity of the BPAs, are always used to reduce the computational cost of evidence combination. In this paper, both the cardinalities and the mass assignment values of focal elements are used as the criteria of reduction. The two criteria are jointly used by using rank-level fusion. Some experiments and related analyses are provided to illustrate and justify the proposed new BPA approximation approach.     

一种基于实数粗集空间的自组织映射方法及在模式识别上的应用

提出一种实数粗糙集,避免了经典粗糙集必须经过离散化处理的环节;并且用实数粗糙集的下、上近似集的精确概念划分自组织映射的输出结果,使得修改后的映射结果中各类样本点之间有明显的间隔,易于进行分类识别.最后通过对某型歼击机的舵面故障的模式识别仿真验证了其方法的正确性和有效性.     

一种基于实数粗集空闻的自组织映射方法及在模式识别上的应用

提出一种实数粗糙集，避免了经典粗糙集必须经过离散化处理的环节；并且用实数粗糙集的下、上近似集的精确概念划分自组织映射的输出结果，使得修改后的映射结果中各类样本点之间有明显的间隔，易于进行分类识别。最后通过对某型歼击机的舵面故障的模式识别仿真验证了其方法的正确性和有效性。     

Tracking with classification-aided multiframe data association

In most conventional tracking systems, only the target kinematic information from, for example, a radar or sonar or an electro-optical sensor, is used in measurement-to-track association. Target class information, which is typically used in postprocessing, can also be used to improve data association to give better tracking accuracy. The use of target class information in data association can improve discrimination by yielding purer tracks and preserving their continuity. In this paper, we present the simultaneous use of target classification information and target kinematic information for target tracking. The approach presented integrates target class information into the data association process using the 2-D (one track list and one measurement list) as well as multiframe (one track list and multiple measurement lists) assignments. The multiframe association likelihood is developed to include the classification results based on the "confusion matrix" that specifies the accuracy of the target classifier. The objective is to improve association results using class information when the kinematic likelihoods are similar for different targets, i.e., there is ambiguity in using kinematic information alone. Performance comparisons with and without the use of class information in data association are presented on a ground target tracking problem. Simulation results quantify the benefits of classification-aided data association for improved target tracking, especially in the presence of association uncertainty in the kinematic measurements. Also, the benefit of 5-D (or multiframe) association versus 2-D association is investigated for different quality classifiers. The main contribution of this paper is the development of the methodology to incorporate exactly the classification information into multidimensional (multiframe) association.     

多测速系统应答信标数据实时分类算法

为了提高目标轨迹解算的稳定性,针对多测速系统在应答模式下的测量数据分类问题,提出了一种新的实时分类算法。首先分析了现有方法的缺陷;其次设计了一种能够将应答数据、信标数据与异常数据进行分类的新算法。该算法的关键是选择适当的分类参考,采用目标理论轨迹与已经分类的历史测量数据等2种分类参考相结合的方法,以适应不同的应用情形;最后,利用2类主站配置模式下的典型实测数据对新算法进行验证,结果表明新算法能够将3类数据正确分类。在此基础上,解算出的实时轨迹平稳且连续,测量数据的利用率明显提高。因此新分类算法的性能优于现有方法,对改善其他测量系统中的实时数据分类效果也具有借鉴意义。     

运动单阵元被动合成阵列波达方向估计

 提出了一种运动单阵元被动合成阵列波达方向(DOA)估计算法。该算法基于被动合成阵列(PSA)的概念,结合空间谱估计的思想构建了运动单阵元被动合成阵列模型,通过多次不同速度合成阵列过程实现对信号DOA的无模糊估计。通过对单次匀速合成阵列过程进行分析得到,在假设信号频率已知条件下,合成阵列算法能够达到与同孔径实阵列多重信号分类（MUSIC）算法相当的DOA估计性能。仿真验证了被动合成阵列与同孔径实阵列的渐近等效性及算法的有效性。     

Hybrid discriminative/class-specific classifiers for narrowband signals

The class-specific (CS) method of signal classification operates by computing low-dimensional feature sets defined for each signal class of interest. By computing separate feature sets tailored to each class, i.e., CS features, the CS method avoids estimating probability distributions in a high-dimension feature space common to all classes. Building a CS classifier amounts to designing feature extraction modules for each class of interest. In this paper we present the design of three CS modules used to form a CS classifier for narrowband signals of finite duration. A general module for narrowband signals based on a narrowband tracker is described. The only assumptions this module makes regarding the time evolution of the signal spectrum are: (1) one or more narrowband lines are present, and (2) the lines wandered either not at all, e.g., CW signal, or with a purpose, e.g., swept FM signal. The other two modules are suited for specific classes of waveforms and assume some a priori knowledge of the signal is available from training data. For in situ training, the tracker-based module can be used to detect as yet unobserved waveforms and classify them into general categories, for example short CW, long CW, fast FM, slow FM, etc. Waveform-specific class-models can then be designed using these waveforms for training. Classification results are presented comparing the performance of a probabilistic conventional classifier with that of a CS classifier built from general modules and a CS classifier built from waveform-specific modules. Results are also presented for hybrid discriminative/generative versions of the classifiers to illustrate the performance gains attainable in using a hybrid over a generative classifier alone.     

A New Probabilistic Transformation in Generalized Power Space

The mapping from the belief to the probability domain is a controversial issue, whose original purpose is to make (hard) decision, but for contrariwise to erroneous widespread idea/claim, this is not the only interest for using such mappings nowadays. Actually the probabilistic transformations of belief mass assignments are very useful in modern multitarget multisensor tracking systems where one deals with soft decisions, especially when precise belief structures are not always available due to the existence of uncertainty in human being's subjective judgments. Therefore, a new probabilistic transformation of interval-valued belief structure is put forward in the generalized power space, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the new transformation works and we compare it to the main existing transformations proposed in the literature so far. Results are provided to illustrate the rationality and efficiency of this new proposed method making the decision problem simpler.     

An online ensemble semi-supervised classification framework for air combat target maneuver recognition

Online target maneuver recognition is an important prerequisite for air combat situation recognition and maneuver decision-making. Conventional target maneuver recognition methods adopt mainly supervised learning methods and assume that many sample labels are available. However, in real-world applications, manual sample labeling is often time-consuming and laborious. In addition, airborne sensors collecting target maneuver trajectory information in data streams often cannot process information in real time. To solve these problems, in this paper, an air combat target maneuver recognition model based on an online ensemble semi-supervised classification framework based on online learning, ensemble learning, semi-supervised learning, and Tri-training algorithm, abbreviated as Online Ensemble Semi-supervised Classification Framework (OESCF), is proposed. The framework is divided into four parts: basic classifier offline training stage, online recognition model initialization stage, target maneuver online recognition stage, and online model update stage. Firstly, based on the improved Tri-training algorithm and the fusion decision filtering strategy combined with disagreement, basic classifiers are trained offline by making full use of labeled and unlabeled sample data. Secondly, the dynamic density clustering algorithm of the target maneuver is performed, statistical information of each cluster is calculated, and a set of micro-clusters is obtained to initialize the online recognition model. Thirdly, the ensemble K-Nearest Neighbor (KNN)-based learning method is used to recognize the incoming target maneuver trajectory instances. Finally, to further improve the accuracy and adaptability of the model under the condition of high dynamic air combat, the parameters of the model are updated online using error-driven representation learning, exponential decay function and basic classifier obtained in the offline training stage. The experimental results on several University of California Irvine (UCI) datasets and real air combat target maneuver trajectory data validate the effectiveness of the proposed method in comparison with other semi-supervised models and supervised models, and the results show that the proposed model achieves higher classification accuracy.     

Radar target identification using the bispectrum: a comparativestudy

Radar target identification is performed using time-domain bispectral features. The classification performance is compared with the performance of other classifiers that use either the impulse response or frequency domain response of the unknown target. The classification algorithms developed here are based on the spectral or the bispectral energy of the received backscatter signal. Classification results are obtained using simulated radar returns derived from measured scattering data from real radar targets. The performance of classifiers in the presence of additive Gaussian (colored or white), exponential noise, and Weibull noise are considered, along with cases where the azimuth position of the target is unknown. Finally, the effect on classification performance of responses horn extraneous point scatterers is investigated     

Aero-engine fault diagnosis applying new fast support vector algorithm

A new fast learning algorithm was presented to solve the large-scale support vector machine ( SVM ) training problem of aero-engine fault diagnosis.The relative boundary vectors ( RBVs ) instead of all the original training samples were used for the training of the binary SVM fault classifiers.This pruning strategy decreased the number of final training sample significantly and can keep classification accuracy almost invariable.Accordingly , the training time was shortened to 1 / 20compared with basic SVM classifier.Meanwhile , owing to the reduction of support vector number , the classification time was also reduced.When sample aliasing existed , the aliasing sample points which were not of the same class were eliminated before the relative boundary vectors were computed.Besides , the samples near the relative boundary vectors were selected for SVM training in order to prevent the loss of some key sample points resulted from aliasing.This can improve classification accuracy effectively.A simulation example to classify 5classes of combination fault of aero-engine gas path components was finished and the total fault classification accuracy reached 96.1%.Simulation results show that this fast learning algorithm is effective , reliable and easy to be implemented for engineering application.