Quantized innovations Kalman filter: stability and modification with scaling quantization

The stability of quantized innovations Kalman filtering (QIKF) is analyzed. In the analysis, the correlation between quantization errors and measurement noises is considered. By taking the quantization errors as a random perturbation in the observation system, the QIKF for the original system is equivalent to a Kalman-like filtering for the equivalent state-observation system. Thus, the estimate error covariance matrix of QIKF can be more exactly analyzed. The boundedness of the estimate error covariance matrix of QIKF is obtained under some weak conditions. The design of the number of quantized levels is discussed to guarantee the stability of QIKF. To overcome the instability and divergence of QIKF when the number of quantization levels is small, we propose a Kalman filter using scaling quantized innovations. Numerical simulations show the validity of the theorems and algorithms.

Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder

为解决深度学习网络中隐藏层节点数难以确定的问题,文中提出一种改进的KL(Kullback-Leibler)散度稀疏自动编码机,并将该方法应用到战斗损伤评估中。该方法能够自动筛选出对数据重建贡献大的隐层特征,舍弃贡献小的隐层特征,从而优化网络结构。在网络预测精度不受影响的前提下,该方法自动筛选隐层特征,提升了计算速度。基于UCI(University of California,Irvine)数据集和BDA(battle damage assessment)战争破坏数据的实验表明,该方法优于其他数据驱动的方法。改进的KL稀疏自动编码机回归网络在保证预测精度的前提下,能提升网络的训练和预测速度,并自动筛选隐层有效特征,优化隐层节点数,优化网络结构。