Study of a New Improved PSO-BP Neural Network Algorithm

In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based on adaptive particle swarm optimization( PSO) algorithm. This algorithm adjusted the inertia weight coefficients and learning factors adaptively and therefore could be used to optimize the weights in the BP network. After establishing the improved PSO-BP( IPSO-BP) model,it was applied to solve fault diagnosis of rolling bearing. Wavelet denoising was selected to reduce the noise of the original vibration signals,and based on these vibration signals a wide set of features were used as the inputs in the neural network models. We demonstrate the effectiveness of the proposed approach by comparing with the traditional BP,PSO-BP and linear PSO-BP( LPSO-BP) algorithms. The experimental results show that IPSO-BP network outperforms other algorithms with faster convergence speed,lower errors,higher diagnostic accuracy and learning ability.

Mutual-information based weighted fusion for target tracking in underwater wireless sensor networks

Underwater wireless sensor networks(UWSNs)can provide a promising solution to underwater target tracking.Due to limited energy and bandwidth resources,only a small number of nodes are selected to track a target at each interval.Because all measurements are fused together to provide information in a fusion center,fusion weights of all selected nodes may affect the performance of target tracking.As far as we know,almost all existing tracking schemes neglect this problem.We study a weighted fusion scheme for target tracking in UWSNs.First,because the mutual information(MI)between a node’s measurement and the target state can quantify target information provided by the node,it is calculated to determine proper fusion weights.Second,we design a novel multi-sensor weighted particle filter(MSWPF)using fusion weights determined by MI.Third,we present a local node selection scheme based on posterior Cramer-Rao lower bound(PCRLB)to improve tracking efficiency.Finally,simulation results are presented to verify the performance improvement of our scheme with proper fusion weights.

基于RTS竞争机制的声通信网络slotted-FAMA协议研究（英文）

目的:针对水声信道特点,设计适用于水下环境的高效节能的水声通信网络MAC层协议。创新点:在传统slotted-FAMA协议基础上,引入RTS竞争机制,克服多RTS尝试问题,大幅度提高网络吞吐量并降低网络能耗。方法:首先,讨论水声通信网络slotted-FAMA协议存在的多RTS尝试问题。指出该问题严重制约了网络性能。然后,提出克服该问题的方法,提出新的MAC协议,RC-SFAMA(图3)。该协议在RTS/CTS握手环节中引入RTS竞争机制。当多RTS尝试问题发生时,各节点利用RTS数据包中的C-number进行RTS竞争来争夺信道使用权。竞争获胜的节点可以继续进行有效数据的传输而失败节点则推迟其数据传输以避免冲突产生。通过竞争机制,当多RTS尝试问题发生时,仍有有效数据得以传输并不会造成冲突,从而克服了这一问题,实现网络吞吐量大幅度提高以及网络能耗的降低。结论:针对slotted-FAMA存在多RTS尝试问题,提出解决办法,提高网络吞吐量并降低网络能耗。

An efficient measurement-driven sequential Monte Carlo multi-Bernoulli filter for multi-target filtering

We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets.Since most clutter measurements do not participate in the update step, the computing time is reduced significantly.Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.

A novel approach of noise statistics estimate using H_∞ filter in target tracking

Noise statistics are essential for estimation performance. In practical situations, however, a priori information of noise statistics is often imperfect. Previous work on noise statistics identification in linear systems still requires initial prior knowledge of the noise. A novel approach is presented in this paper to solve this paradox.First, we apply the H_∞ filter to obtain the system state estimates without the common assumptions about the noise in conventional adaptive filters. Then by applying state estimates obtained from the H_∞ filter, better estimates of the noise mean and covariance can be achieved, which can improve the performance of estimation. The proposed approach makes the best use of the system knowledge without a priori information with modest computation cost,which makes it possible to be applied online. Finally, numerical examples are presented to show the efficiency of this approach.

一类时变时滞非线性系统的H_∞参考跟踪控制设计（英文）

目的:针对一类具有时变时滞的非线性系统,设计状态控制器实现闭环系统输出对参考轨迹的H∞跟踪。创新点:利用统一模型表示所研究的时变时滞非线性系统,修正参考模型使其更具一般性,引入缩放系数提高系统的跟踪精度。方法:采用一个由线性时滞动态系统和有界静态非线性项组成的统一模型来描述所要研究的时变时滞非线性系统。完成系统建模后,首先给出参考轨迹跟踪的定义,以修正后的跟踪模型为参考轨迹,采用状态反馈法实现闭环控制(图1)。并对闭环系统的H∞性能进行分析,基于此分析结果推导得到以线性矩阵不等式形式表示的跟踪控制器参数所满足的条件。该条件可以确保闭环系统H∞意义下跟踪给定的参考信号。在控制器设计中引入缩放系数以提高跟踪精度。图2和图4分别给出跟踪控制器对正弦信号和方波信号的H∞跟踪;图3和图5则分别表示在正弦参考信号和方波参考信号输入下,缩放系数的变大对提高跟踪效果的具体影响。结论:针对一类具有时变时滞的非线性系统,以修正后的跟踪模型为参考轨迹,设计状态反馈控制器实现闭环系统输出对参考轨迹的H∞跟踪,并可通过增大缩放系数提高跟踪效果。

智能家居环境中基于移动机器人的失物搜索（英文）

智能家居可提供补充信息以帮助家居服务机器人。本文利用智能家居环境中机器人所获取的历史轨迹数据,提出一种机器人失物搜索(MIF)系统。首先,提出一种多传感器融合方法定位和跟踪用户。其次,利用人物历史轨迹所包含的知识,提出一种路径规划方法生成机器人搜索路径。再次,应用一种基于卷积神经网络的实时物体检测单元检测目标丢失物品。基于搭建的智能家居测试平台进行MIF系统相关实验。最后,实验结果验证了所提方案的准确性和有效性。

Energy-efficient localization and target tracking via underwater mobile sensor networks

Underwater mobile sensor networks(UMSNs) with free-floating sensors are more suitable for understanding the immense underwater environment. Target tracking, whose performance depends on sensor localization accuracy, is one of the broad applications of UMSNs. However, in UMSNs, sensors move with environmental forces,so their positions change continuously, which poses a challenge on the accuracy of sensor localization and target tracking. We propose a high-accuracy localization with mobility prediction(HLMP) algorithm to acquire relatively accurate sensor location estimates. The HLMP algorithm exploits sensor mobility characteristics and the multistep Levinson-Durbin algorithm to predict future positions. Furthermore, we present a simultaneous localization and target tracking(SLAT) algorithm to update sensor locations based on measurements during the process of target tracking. Simulation results demonstrate that the HLMP algorithm can improve localization accuracy significantly with low energy consumption and that the SLAT algorithm can further decrease the sensor localization error. In addition, results prove that a better localization accuracy will synchronously improve the target tracking performance.