针对姿态多变化的飞机自动目标识别中的低识别率问题,提出了一种基于DSm T(Dezert-Smarandache theory)与隐马尔可夫模型(Hidden Markov model,HMM)的飞机多特征序列信息融合识别算法(Multiple features and sequential information fus...针对姿态多变化的飞机自动目标识别中的低识别率问题,提出了一种基于DSm T(Dezert-Smarandache theory)与隐马尔可夫模型(Hidden Markov model,HMM)的飞机多特征序列信息融合识别算法(Multiple features and sequential information fusion,MFSIF).其创新性在于将单幅图像的多特征信息融合识别和序列图像信息融合识别进行有机结合.首先,对图像进行二值化预处理,并提取目标的Hu矩和轮廓局部奇异值特征;然后,利用概率神经网络(Probabilistic neural networks,PNN)构造基本信度赋值(Basic belief assignment,BBA);接着,利用DSm T对该图像的不同特征进行融合,从而获得HMM的观察值序列;再接着,利用隐马尔可夫模型对飞机序列信息融合,计算观察值序列与各隐马尔可夫模型之间的相似度,从而实现姿态多变化的飞机目标自动识别;最后,通过仿真实验,验证了该算法在飞机姿态发生较大变化时,依然可以获得较高的正确识别率,同时在实时性方面也可以满足飞机目标识别的要求.另外,在飞机序列发生连续遮挡帧数τ≤6的情况下,也具有较高的飞机目标正确识别率.展开更多
The study on alternative combination rules in Dempster- Shafer theory （DST） when evidences are in conflict has emerged again recently as an interesting topic, especially in data/information fusion applications. The ...The study on alternative combination rules in Dempster- Shafer theory （DST） when evidences are in conflict has emerged again recently as an interesting topic, especially in data/information fusion applications. The earlier researches have mainly focused on investigating the alternative which would be appropriate for the conflicting situation, under the assumption that a conflict is identified. However, the current research shows that not only the combination rule but also the classical conflict coefficient in DST are not correct to determine the conflict degree between two pieces of evidences. Most existing methods of measuring conflict do not consider the open world situation, whose frame of discernment is incomplete. To solve this problem, a new conflict representa- tion model to determine the conflict degree between evidences is proposed in the generalized power space, which contains two parameters： the conflict distance and the conflict coefficient of inconsistent evidences. This paper argues that only when the con- flict measure value in the new representation model is high, it is safe to say the evidences are in conflict. Experiments illustrate the efficiency of the proposed conflict representation model.展开更多
文摘针对姿态多变化的飞机自动目标识别中的低识别率问题,提出了一种基于DSm T(Dezert-Smarandache theory)与隐马尔可夫模型(Hidden Markov model,HMM)的飞机多特征序列信息融合识别算法(Multiple features and sequential information fusion,MFSIF).其创新性在于将单幅图像的多特征信息融合识别和序列图像信息融合识别进行有机结合.首先,对图像进行二值化预处理,并提取目标的Hu矩和轮廓局部奇异值特征;然后,利用概率神经网络(Probabilistic neural networks,PNN)构造基本信度赋值(Basic belief assignment,BBA);接着,利用DSm T对该图像的不同特征进行融合,从而获得HMM的观察值序列;再接着,利用隐马尔可夫模型对飞机序列信息融合,计算观察值序列与各隐马尔可夫模型之间的相似度,从而实现姿态多变化的飞机目标自动识别;最后,通过仿真实验,验证了该算法在飞机姿态发生较大变化时,依然可以获得较高的正确识别率,同时在实时性方面也可以满足飞机目标识别的要求.另外,在飞机序列发生连续遮挡帧数τ≤6的情况下,也具有较高的飞机目标正确识别率.
基金supported by the National Natural Science Foundation of China (60572161 60874105)+4 种基金the Excellent Ph.D. Paper Author Foundation of China (200443)the Postdoctoral Science Foundation of China (20070421094)the Program for New Century Excellent Talents in University (NCET-08-0345)the Shanghai Rising-Star Program(09QA1402900)the Ministry of Education Key Lab of Intelligent Computing & Signal Processing (2009ICIP03)
文摘The study on alternative combination rules in Dempster- Shafer theory （DST） when evidences are in conflict has emerged again recently as an interesting topic, especially in data/information fusion applications. The earlier researches have mainly focused on investigating the alternative which would be appropriate for the conflicting situation, under the assumption that a conflict is identified. However, the current research shows that not only the combination rule but also the classical conflict coefficient in DST are not correct to determine the conflict degree between two pieces of evidences. Most existing methods of measuring conflict do not consider the open world situation, whose frame of discernment is incomplete. To solve this problem, a new conflict representa- tion model to determine the conflict degree between evidences is proposed in the generalized power space, which contains two parameters： the conflict distance and the conflict coefficient of inconsistent evidences. This paper argues that only when the con- flict measure value in the new representation model is high, it is safe to say the evidences are in conflict. Experiments illustrate the efficiency of the proposed conflict representation model.
