基于节点通信度的信息加权一致性滤波

协同目标跟踪是无人机集群等多传感器网络的典型应用。在分布式传感器网络目标跟踪过程中,目标状态估计的一致性直接影响到跟踪有效性。针对目标跟踪过程中网络节点之间一致性迭代次数受限的问题,提出了一种基于节点通信度的信息加权一致性滤波算法,设计了用节点通信度来充分衡量传感器节点在网络中的通信拓扑状况,并构建了非对称一致性权值的选取机制,可在复杂拓扑结构网络中实现快速一致性跟踪。典型目标跟踪场景仿真验证表明,所提算法相比经典的信息加权一致性滤波算法,目标跟踪的不一致程度降低了20%以上,有效提升了分布式跟踪的一致性速度。     

基于MPI的主从式并行MCMC

马尔可夫链蒙特卡洛(MCMC)是贝叶斯网络(BN)的一种近似推理算法,为保证其时间性能,在生成马尔可夫链的组成序列时,通常只使用较少的样本,其推理精度较低.以Gibbs抽样为基本框架,提出一种BN的并行MCMC推理方法PMCMC,在生成马尔可夫链的组成序列时,通过增加对BN中每个节点的抽样频率来提高推理精度,并在消息传递接口MPJ的支持下,利用主从式并行机制来实现其推理过程,保证了推理的时间性能.在3个不同BN上的推理实验结果表明,PMCMC在提高推理精度的同时有效保证了推理的时间生能.     

基于KLD采样的自适应UPF非线性状态估计方法

针对标准UPF算法存在的计算量大、实时性差的问题,设计了一种利用KLD采样在线实时改变粒子个数的自适应UPF算法.该算法的核心思想是利用KLD采样原理,根据预测粒子在状态空间中的分布情况来在线实时的确定下一次滤波迭代所需的粒子个数,减少对滤波算法没有帮助的粒子,仅保留保证滤波估计精度所需的最少粒子个数,从而有效减小算法的运算量,提高算法的实时处理能力.最后,将自适应UPF算法与粒子滤波、标准UPF算法进行了仿真比较,仿真结果表明在保持高精度估计能力的同时,自适应UPF算法比标准UPF算法具有更好的实时性,是解决非线性非高斯系统状态估计问题的一种有效方法.     

基于多关联向量机的动态软测量建模

为了估计工业过程中的质量参数,考虑到实际过程中的动态特性,提出一种基于多关联向量机的动态软测量建模方法.对于动态过渡周期内的二次变量数据,根据不同采样时刻划分为多个计算子空间,在每一子空间内采用关联向量机来建立不同时刻的二次变量对主导变量的影响模型.各个子模型的输出采用一个综合关联向量机进行连接,从而建立了基于过渡周期内不同二次变量采样数据的主导变量动态软测量模型.通过实例仿真,结果验证了所提出方法的有效性.     

改进的光辐射强度估算

光子图是一种高效的全局光照算法,能够模拟物理真实感光照效果.然而,经典的光子图算法的光辐射强度估算不够准确,尤其在物体的细节区域光通量计算有很大误差,细节部分的绘制质量很差.分析了光辐射强度估计的过程,找到了产生较大误差的原因,进而提出了一种改进的光辐射强度估算方法.对于搜索范围内的每个光子,求出它在绘制点的切平面上的投影,判断投影点与绘制点之间的距离是否小于搜索半径,进而决定是否使用该光子计算光通量.改进后的算法虽然增加了一些计算时间,但是显著地提高了光辐射强度估计的准确性.理论分析和场景测试证明改进后的算法明显改善了物体细节部分的绘制效果.     

反演海底参数的不确定性对声传播损失预报的影响

浅海混响反演海底声学参数的结果通常被直接用于计算(预报)声传播损失(TL),而很少考虑参数反演结果的不确定性对TL预报的影响.首次采用后验预报概率方法分析浅海混响垂直相关反演海底参数结果的不确定性对TL预报的影响.一次海上实验数据处理的结果表明:3个不同深度、1～5km传播距离内,500～800Hz频段相干TL在相干极大值附近其后验预报概率分布较为分散,而在其他区域较为集中,相应的最大90%置信区间(CI)超过20dB,整体距离上的平均90%CI也超过6dB;相同条件下非相干TL的后验预报概率均很好地收敛于最优值,相应的最大90%CI不超过5dB,距离平均90%CI小于3dB.     

提高图像块运动估计搜索速度的改进算法及其性能

运动估计是视频压缩中帧间预测编码的关键技术之一.由于运动估计具有较大的运算量,因此对压缩性能有重要影响.在研究分析了影响视频编码性能的传统搜索算法基础上,提出了一种基于六边形搜索算法的改进算法.该算法初始步骤使用十字形搜索模式,然后采用六边形搜索模式中的小钻石搜索模式,进行快速块运动估计.并进行了计算机仿真实验证明了改进算法在压缩处理运算量及信号质量方面的优越性.结果表明,改进算法比新三步搜索算法和传统六边形搜索算法有着更快的搜索速度和更小的失真.     

Adaptive compensating method for Doppler frequency shift using LMS and phase estimation

The novel compensating method directly demodulates the signals without the carrier recovery processes, in which the carrier with original modulation frequency is used as the local coherent carrier. In this way, the phase offsets due to frequency shift are linear. Based on this premise, the  compensation processes are: firstly, the phase offsets between the base band neighbor-symbols after clock recovery are unbiasedly estimated among the reference symbols; then, the receiving signals symbols are adjusted by the phase estimation value; finally, the phase offsets after adjusting are compensated by the least mean squares (LMS) algorithm. In order to express the compensation processes and ability clearly, the quadrature phase shift keying (QPSK) modulation signals are regarded as examples for Matlab simulation. BER simulations are carried out using the Monte-Carlo method. The learning curves are obtained to study the algorithm’s convergence ability. The constellation figures are also simulated to observe the compensation results directly.     

Single link tracking scheme based on UWB localization

To investigate the low-complex and high-precise tracking method, a novel single link tracking scheme based on UWB localization is proposed. Two antenna arrays are settled at the fixed station (FS) to receive the UWB signal from mobile terminal (MT), one FS is enough for the proposed scheme to track the MT. The UWB multipath detection algorithm for time difference of arrival (TDOA) estimation is presented and TDOA is thus adopted to estimate angle of arrival (AOA), avoiding the synchronization and complicated beamforming for AOA. The impacts of localization errors, concluding multipath and non-line-of-sight (NLOS) errors are simulated for the proposed track scheme. It is demonstrated that the simulation curve can match the real target moving, and the feasibility of the proposed scheme is proved.     

Testing serial correlation in semiparametric varying coefficient partially linear errors-in-variables model

The authors propose a V _{N, p} test statistic for testing finite-order serial correlation in a semiparametric varying coefficient partially linear errors-in-variables model. The test statistic is shown to have asymptotic normal distribution under the null hypothesis of no serial correlation. Some Monte Carlo experiments are conducted to examine the finite sample performance of the proposed V _{N, p} test statistic. Simulation results confirm that the proposed test performs satisfactorily in estimated size and power. This research is supported by the National Natural Science Foundation of China under Grant Nos. 10871217 and 40574003; the Science and Technology Project of Chongqing Education Committee under Grant No. KJ080609; the Doctor's Start-up Research Fund under Grant No. 08-52204; and the Youth Science Research Fund of Chongqing Technology and Business University under Grant No. 0852008.