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1.
针对多节点合作接收系统中的盲均衡问题,提出了一种基于一致优化的分布式粒子滤波盲均衡改进算法。该算法采用多个接收机组成无融合中心的分布式网络,使用分布式粒子滤波合作估计共同的发送符号序列,克服了单接收节点受信道影响大、误码率高的问题。为了保证粒子滤波中每个节点产生共同的粒子集和粒子权重,采用基于交替方向乘子法的一致优化算法获得联合似然函数,并与最大一致协议相结合,从而使每个节点获得相同的最佳重要性函数和粒子权重。理论分析与仿真结果表明,该算法只需经过有限次的一致迭代就可以达到集中式合作盲均衡的性能。全分布式的多节点合作获得了空间分集增益,降低了系统误码率。 相似文献
2.
为降低水下无线传感器网络目标跟踪算法能耗并提高定位精度,提出基于能量有效的分布式粒子滤波跟踪算法(EEPF算法)。EEPF算法通过能量有效的最优分布式动态成簇机制和启发式能量有效的调度算法来平衡水下节点间的能耗,延长网络生存期,并在预测、滤波、重采样阶段对粒子滤波算法进行改进,在保障期望目标跟踪精度的同时降低了运算能耗。仿真结果表明,EEPF算法是一种轻量级的能量有效的目标跟踪算法,该算法能耗低,网络存活时间长,且跟踪精度较传统粒子滤波算法有了较大提高。 相似文献
3.
针对无线传感器网络中节点通信能力及能量有限的情况,该文提出基于动态分簇路由优化和分布式粒子滤波的传感器网络目标跟踪方法。该方法以动态分簇的方式将监测区域内随机部署的传感器节点划分为若干个簇,并对簇内成员节点与簇首节点之间、簇首节点与基站之间的通信路由进行优化,确保网络能耗的均衡分布,在此基础上,被激活的簇内成员节点并行地执行分布式粒子滤波算法实现目标跟踪。仿真结果表明,该方法能有效地降低传感器网络中节点的总能耗,能在实现跟踪的同时保证目标跟踪的精度。 相似文献
4.
重要性密度函数的选择对粒子滤波至关重要,围绕重要性密度函数的选择,已提出许多改进粒子滤波算法,典型的有扩展卡尔曼粒子滤波(EPF),不敏卡尔曼粒子滤波(UPF)、辅助粒子滤波(APF)及正则化粒子滤波(RPF).详细讨论了4种改进粒子滤波算法的基本思想、性能特.占及主要步骤.通过对一典型标量非线性系统的滤波实验,对4种改进算法的性能进行了仿真比较,实验结果表明,4种改进算法都从不同程度上改善了粒子滤波器的性能,其中,UPF的性能最优.最后,分析了各算法的改进原因. 相似文献
5.
该文针对多源-多中继放大转发协作通信网络,以最小化系统总功率为目标,在保证系统满足一定中断概率的前提下,提出了一种分布式功率分配与中继选择算法.算法由源节点自主选择为其转发信息的中继节点,并引入定时器,通过竞争方式避免了分布式所导致的中继选择冲突.中继收到来自源节点的信号后,只需根据转发门限自主判断是否进行转发,从而完成传输.仿真结果表明该分布式算法能够有效降低传输所需要的总发射功率.并且与集中式控制所获得的最优中继选择与功率分配算法相比性能相近,但所提分布式算法显著降低了系统的控制开销. 相似文献
6.
针对非线性非高斯的分布式多传感器状态估计问题,提出了一种基于无迹卡尔曼滤波与粒子滤波相结合的融合跟踪算法。通过对量测方程的非线性分析,利用改进的粒子滤波算法(UPF)计算局部传感器的状态估计值,再应用分布式加权融合准则得到全局状态估值. 相似文献
7.
为提高分布式雷达系统的目标检测与跟踪能力,研究了基于粒子滤波的检测前跟踪算法。针对传统粒子滤波中粗化方法盲目性的问题,提出了一种适用于分布式雷达目标检测与跟踪的多簇聚类粒子滤波算法。该算法在粗化的基础上,首先采用改进的K Means方法对粒子聚类以形成多个粒子簇,引导各簇内粒子沿着该簇中心向该簇最大联合似然粒子方向偏移,使粒子向高联合似然区域集中。该算法能够在缓解粒子滤波样本贫化问题的同时减少传统粗化的盲目性,提高了系统从接收数据中提取目标信息的能力。对分布式雷达目标检测与跟踪的仿真结果表明,多簇聚类粒子滤波算法比传统的粗化策略粒子滤波算法具有更好的检测能力和更高的跟踪精度。 相似文献
8.
为了解决分布式最小均方算法在输入信号相关性较高时收敛速度较慢、分布式仿射投影算法计算复杂度较高等问题,本文提出了两种分布式子带自适应滤波算法,即递增式和扩散式子带自适应滤波算法.分布式子带自适应滤波算法将节点信号进行子带分割来降低信号的相关性,从而加快收敛速度.由于用于子带分割的滤波器组中包含了抽取单元,所以分布式子带自适应滤波算法和对应的分布式最小均方算法的计算复杂度相近.仿真结果表明,与分布式最小均方算法相比,分布式子带自适应滤波算法具有更好的收敛性能. 相似文献
9.
重要性函数的选择是粒子滤波算法的核心,本文提出一种基于扩展H∞滤波(EHF)产生重要性函数的扩展H∞粒子滤波(EHPF)算法,由于EHF滤波算法鲁棒性强、滤波精度高,且该滤波算法考虑了最新的观测数据,因此由其产生的重要性函数更接近于系统状态的真实后验概率分布.理论分析和仿真结果表明扩展H∞粒子滤波算法的滤波性能明显优于标准粒子滤波算法,扩展卡尔曼滤波算法和扩展卡尔曼粒子滤波算法,与不敏粒子滤波算法滤波精度相当,但计算复杂度要低于不敏粒子滤波算法,是一种有效的粒子滤波算法. 相似文献
10.
针对粒子滤波算法中的权值退化问题,提出了一种遗传算法辅助下的粒子滤波算法。利用遗传算法在选择继承性上的优势,将遗传算法中的选择、交叉和变异操作引入到粒子滤波算法中。在此基础上,将改进的粒子滤波算法与建立的全球定位系统(GPS)非线性动态状态空间模型结合应用于GPS定位数据处理问题,通过采集实测GPS数据将改进粒子滤波算法与基本粒子滤波算法做了比较。结果表明,遗传算法辅助下的粒子滤波可以增加有效粒子数目,有效解决粒子退化问题,可提高GPS定位数据处理精确度。 相似文献
11.
水下无线传感网络(UWSN)执行目标跟踪时,因为各个传感器节点测量值对目标状态估计的贡献不一样以及节点能量有限,所以探索一种好的节点融合权重方法和节点规划机制能够获得更好的跟踪性能。针对上述问题,该文提出一种基于Grubbs准则和互信息熵加权融合的分布式粒子滤波(PF)目标跟踪算法(GMIEW)。首先利用Grubbs准则对传感器节点所获得的信息进行分析检验,去除干扰信息和错误信息。其次,在粒子滤波的重要性权值计算的过程中,引入动态加权因子,采用传感器节点的测量值与目标状态之间的互信息熵,来反映传感器节点提供的目标信息量,从而获得各个节点相应的加权因子。最后,采用3维场景下的簇-树型网络拓扑结构,跟踪监测区域内的目标。实验结果显示,该算法可有效提高水下传感器网络测量数据对目标跟踪预测的准确度,降低跟踪误差。 相似文献
12.
Target tracking is one of the main applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of the sensor nodes. A framework and analysis for collaborative tracking via particle filter are presented in this paper Collaborative tracking is implemented through sensor selection, and results of tracking are propagated among sensor nodes. In order to save communication resources, a new Gaussian sum particle filter, called Gaussian sum quasi particle filter, to perform the target tracking is presented, in which only mean and covariance of mixands need to be communicated. Based on the Gaussian sum quasi particle filter, a sensor selection criterion is proposed, which is computationally much simpler than other sensor selection criterions. Simulation results show that the proposed method works well for target tracking. 相似文献
13.
In this work, an efficient direction of arrival (DOA) tracking method for coherently distributed sources is proposed. The central DOA and angle spread are estimated by the proposed method with a particle filter at each snapshot. The spectrum calculated by the distributed source parameter estimator (DSPE) is used as a pseudo-likelihood function for particle updating, which enables the particle filter to process sensor signals directly without estimating the source amplitude. The proposed method is compared with the total least square estimating signal parameter via rotational invariance technique and the fast implementation of a power iteration subspace updating (FAPI-TLS-ESPRIT). The proposed method is verified by Monte Carlo simulations, and simulation results show that the proposed method can achieve an excellent DOA tracking performance and outperforms the FAPI-TLS-ESPRIT method. In addition, the proposed method can simultaneously estimate the central DOA and angle spread. 相似文献
14.
Target tracking is one of the most important applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of sensor nodes. A new robust and energy-efficient collaborative target tracking framework is proposed in this article. After a target is detected, only one active cluster is responsible for the tracking task at each time step. The tracking algorithm is distributed by passing the sensing and computation operations from one cluster to another. An event-driven cluster reforming scheme is also proposed for balancing energy consumption among nodes. Observations from three cluster members are chosen and a new class of particle filter termed cost-reference particle filter (CRPF) is introduced to estimate the target motion at the cluster head. This CRPF method is quite robust for wireless sensor network tracking applications because it drops the strong assumptions of knowing the probability distributions of the system process and observation noises. In simulation experiments, the performance of the proposed collaborative target tracking algorithm is evaluated by the metrics of tracking precision and network energy consumption. 相似文献
15.
This paper investigated the problem of distributed estimation for a class of discrete-time nonlinear systems with unknown inputs in a sensor network. A modification scheme to the derivative-free versions of nonlinear robust two-stage Kalman filter (DNRTSKF) is first introduced based on recently developed cubature Kalman filter (CKF) technique. Afterward, a novel information filter is proposed by expressing the recursion in terms of the information matrix based upon DNRTSKF. In the end, distributed DNRTSKF is developed by applying a new information consensus filter to diffuse local statistics over the entire sensor network. In the implementation procedure, each sensor node only fuses the local observation instead of the global information and updates its local information state and matrix from its neighbors’ estimates using Average-Consensus Algorithm. Simulation results illustrate that the proposed distributed filter reveals the performance comparable to centralized DNRTSKF and better than distributed CKF. 相似文献
16.
针对目标检测概率较低导致单个传感器无法对目标进行有效检测并跟踪的问题,本文提出了多传感器箱粒子概率假设密度(multi-sensor box particle probability hypothesis density filter,MS-BOX-PHD)滤波器.MS-BOX-PHD滤波器首先将多个传感器的量测转换、融合成为一个量测集合,并利用箱粒子概率假设密度(box particle probability hypothesis density filter,BOX-PHD)滤波器对多个目标的状态进行预测和更新.数值实验表明,相较于单传感器箱粒子概率假设密度(Single-BOX-PHD)滤波器,MS-BOX-PHD滤波器在目标检测概率较低时,能够有效地对多目标的状态和数目进行估计;相较于区间量测下多传感器标准PHD粒子(multi-sensor standard probability hypothesis density particle filter with interval measurement,IM-PHD-PF)滤波器,在达到相同的跟踪性能时,计算效率提升了38.57%. 相似文献
17.
A new distributed node localization algorithm named mobile beacons-improved particle filter (MB-IPF) was proposed. In the algorithm, the mobile nodes equipped with globe position system (GPS) move around in the wireless sensor network (WSN) field based on the Gauss-Markov mobility model, and periodically broadcast the beacon messages. Each unknown node estimates its location in a fully distributed mode based on the received mobile beacons. The localization algorithm is based on the IPF and several refinements, including the proposed weighted centroid algorithm, the residual resampling algorithm, and the markov chain monte carlo (MCMC) method etc., which were also introduced for performance improvement. The simulation results show that our proposed algorithm is efficient for most applications. 相似文献
18.
In this letter, we propose a moving‐target tracking algorithm based on a particle filter that uses the time difference of arrival (TDOA)/frequency difference of arrival (FDOA) measurements acquired by distributed sensors. It is shown that the performance of the proposed algorithm, based on the particle filter, outperforms the one based on the extended Kalman filter. The use of both the TDOA and FDOA measurements is shown to be effective in the moving‐target tracking. It is proven that the particle filter deals with the nonlinear nature of the moving‐target tracking problem successfully. 相似文献
19.
针对实际应用条件下传感器节点的观测数据与目标动态参数间呈现为非线性关系的特性,提出了一种基于粒子群优化和M-H抽样粒子滤波的传感器网络目标跟踪方法。该方法采用分布式结构,在动态网络拓扑结构下,由粒子群优化和M-H抽样技术实现滤波中的重抽样过程,抑制粒子退化现象,并通过粒子间共享历史信息,降低单个粒子历史状态间的相关性使各粒子能快速收敛至最优分布,从而实现高精度的目标跟踪效果。仿真结果表明,相比现有的基于信息粒子滤波和并行粒子滤波技术的传感器网络目标跟踪方法,所提出的方法能降低网络总能耗,同时保证目标跟踪的精度。 相似文献
20.
利用分布式传感器网络进行目标跟踪,能够有效增加传感器的覆盖范围,提高对运动目标的检测和跟踪能力,但如何充分利用相邻传感器之间的信息进行有效的融合,仍然是一个难点问题。本文在多伯努利滤波(Multi-Bernoulli,MB)框架下,提出了一种改进的分布式融合跟踪算法用于目标数未知且变化的多目标跟踪。提出算法包含三种精度提升策略,即特征级融合反馈、决策级融合输出及交互反馈;其中,决策级融合输出策略可以提取更加准确的估计状态,特征级融合反馈策略可以降低错误融合结果对后续滤波过程的不良影响,交互反馈策略可以避免单传感器因漏检而导致的滤波失败。实验结果表明,提出算法的跟踪精度明显要优于传统的基于广义协方差交集(Generalized Covariance Intersection,GCI)的分布式融合算法以及粒子多伯努利跟踪算法,具有较好的跟踪性能。 相似文献
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