Multipath effects on vector tracking algorithm for GNSS signal

The vector tracking algorithm uses a single extended Kalman filter （EKF） to predict the time-delays and the Doppler deviations of the GNSS signal, while also estimating the user＇s position, velocity, and clock state. In this paper, the effects of multipath on the tracking performance of the vector delay / frequency lock loop （VDFLL） is studied for better application in the multipath environment. The error expressions of the measurements are given in theory. The tracking error caused by multipath is reduced by VDFLL, which is proved by the tracking error of VDFLL through a new iterating method. The theoretical analysis is verified by the Monte Carlo simulation.     

Cooperative distributed target tracking algorithm in mobile wireless sensor networks

The paper proposes a cooperative distributed target tracking algorithm in mobile wireless sensor networks.There are two main components in the algorithm:distributed sensor-target assignment and sensor motion control.In the key idea of the sensor-target assignment,sensors are considered as autonomous agents and the defined objective function of each sensor concentrates on two fundamental factors:the tracking accuracy and the tracking cost.Compared with the centralized algorithm and the noncooperative distrib...     

Constrained nonlinear MPC for accelerated tracking piece-wise references and its applications to thermal systems

In this paper, we propose a model predictive control (MPC) strategy for accelerated offset-free tracking piece-wise constant reference signals of nonlinear systems subject to state and control constraints. Some special contractive constraints on tracking errors and terminal constraints are embedded into the tracking nonlinear MPC formulation. Then, recursive feasibility and closed-loop convergence of the tracking MPC are guaranteed in the presence of piece-wise references and constraints by deriving some sufficient conditions. Moreover, the local optimality of the tracking MPC is achieved for unreachable output reference signals. By comparing to traditional tracking MPC, the simulation experiment of a thermal system is used to demonstrate the acceleration ability and the effectiveness of the tracking MPC scheme proposed here.     

Design and performance analysis of tracking controller for uncertain nonlinear composite system using neural networks

Based on high order dynamic neural network, this paper presents the tracking problem for uncertain nonlinear composite system, which contains external disturbance, whose nonlinearities are assumed to be unknown. A smooth controller is designed to guarantee a uniform ultimate boundedness property for the tracking error and all other signals in the dosed loop. Certain measures are utilized to test its performance. No a priori knowledge of an upper bound on the “optimal” weight and modeling error is required; the weights of neural networks are updated on-line. Numerical simulations performed on a simple example illustrate and clarify the approach.     

HierTrack: an energy-efficient cluster-based target tracking system forwireless sensor networks

Target tracking is a typical and important application of wireless sensor networks(WSNs).Existing target tracking protocols focus mainly on energy efficiency,and little effort has been put into network management and real-time data routing,which are also very important issues for target tracking.In this paper,we propose a scalable cluster-based target tracking framework,namely the hierarchical prediction strategy(HPS),for energyefficient and real-time target tracking in large-scale WSNs.HPS organizes sensor nodes into clusters by using suitable clustering protocols which are beneficial for network management and data routing.As a target moves in the network,cluster heads predict the target trajectory using Kalman filter and selectively activate the next round of sensors in advance to keep on tracking the target.The estimated locations of the target are routed to the base station via the backbone composed of the cluster heads.A soft handoff algorithm is proposed in HPS to guarantee smooth tracking of the target when the target moves from one cluster to another.Under the framework of HPS,we design and implement an energy-efficient target tracking system,HierTrack,which consists of 36 sensor motes,a sink node,and a base station.Both simulation and experimental results show the efficiency of our system.     

A multi-cue mean-shift target tracking approach based on fuzzified region dynamic image fusion

Traditional target tracking algorithms based on single sensor images are unstable and have low accuracy. Based on regional target detection and fuzzy region rules, a fuzzy region-based multi-sensor image fusion approach is proposed in this paper. The similarity measure weight is adapted to this dynamic image fusion algorithm, while the tracking method uses the proposed multi-cue mean-shift tracking algorithm. Three experimental results using real world image sequences are evaluated using the steady state square root mean error. The fusion and tracking experiments indicate that the proposed approach is effective and efficient when aiming at a target moving from one area to a different area, which meets the robustness and real-time requirements.     

Learning-based tracking of complex non-rigid motion

This paper describes a novel method for tracking complex non-rigid motions by learning the intrinsic object structure. The approach builds on and extends the studies on non-linear dimensionality reduction for object representation, object dynamics modeling and particle filter style tracking. First, the dimensionality reduction and density estimation algorithm is derived for unsupervised learning of object intrinsic representation, and the obtained non-rigid part of object state reduces even to 2-3 dimensions. Secondly the dynamical model is derived and trained based on this intrinsic representation. Thirdly the learned intrinsic object structure is integrated into a particle filter style tracker. It is shown that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle filter style tracker more robust and reliable.Extensive experiments are done on the tracking of challenging non-rigid motions such as fish twisting with selfocclusion, large inter-frame lip motion and facial expressions with global head rotation. Quantitative results are given to make comparisons between the newly proposed tracker and the existing tracker. The proposed method also has the potential to solve other type of tracking problems.     

Design and performance analysis of tracking controller for uncertain nonlinear composite system using neural networks

Based on high order dyna mi c neural network, paper presents the tracking problem for uncertain nonlinear composite system,which contain s external disturbance,whose nonlinear ities are assumed to be unknown.A smooth contr oller is designed to guarantee a uniform ultimate boundedness property for the tracking error and a ll other signals in the closed loop.Certain meas ures are utilized to test its performance.No a priori knowledge of an upper bound on the “ optimal" weight and modeling error is required;the weights of neural networks are updated on-line.N umerical simulations performed on a simple example i llustrate and clarify the approach.     

A tracking loop based on tightly coupled range and velocity filter equations

A joint range-velocity closed tracking loop,which is based on tightly coupled range and velocity filter is proposed.When the measured velocity value is adopted in the range tracking loop to modify the velocity and acceleration equations from traditional α-β-γ filter,the tracking loop based on tightly coupled range and velocity filter can not only track the range and the velocity simultaneously,but also improve the range tracking accuracy.The experimental results show that the tracking errors about range thermal noise in the proposed loop is lower than those in the traditional loop over 2.2 dB,when filter parameters satisfy least mean-square error criterion.Moreover,with the increase of the filter parameter,the tracking performance of our schemes are improved accordingly.     

一种模型参考鲁棒控制的跟踪性能改善

This paper considers the tracking performance problem of a model reference robust control (MRRC) for plants with relative degree greater than one. A new algorithm is proposed based on the earlier research. It is shown that by applying a special transformation to the tracking system, the L1 bound of the tracking error can be achieved even when the high frequency gain is unknown, and both the tracking performance and the control effort can be improved significantly. Furthermore, the strictly positive real (SPR) condition, which is an essential assumption of the earlier design, can be removed.     

一种单频GPS软件接收机的设计实现

GPS软件接收机相对于硬件接收机更灵活,在一些特殊应用背景下易于实现更复杂的算法,从而提升了系统的性能.有着不可比拟的优势.文章致力于研究一种基于PC的单频GPS软件接收机.GPS信号下变频和对中频信号的采样模数转换、数据传输由射频前端、数据采集板实现,采样后的数字中频信号在PC中完成捕获、跟踪、导航信息解算,并给出最终定位结果.对数据采集硬件和信号处理算法的具体实现作了详细介绍,并给出了系统测试实采数据的处理结果,结果表明系统工作正常,静态定位精度可以满足要求.     

基于跟踪微分器的基波测量方法研究

电网基波参数特别是基波频率在谐波分析与治理中具有重要的地位,也是电能质量、电能计量以及电力系统控制等领域的重要技术指标。在非同步采样情况下,针对基于频域法测量基波频率存在频谱泄露现象和测算精度不高的问题,提出了基于跟踪微分器(Tracking Differentiator,TD)的基波参数时域测量方法。该方法的主要思想是将检测到的基波信号通过TD来获取基波跟踪信号及其微分跟踪信号,再通过这两个信号的最大幅值即可获取基波的幅值、频率和初相位参数。仿真结果表明,本文方法只需要60%额定工频周期窗口的样本数据即可获得较高精度的基波参数测算值,是一种有效的基波参数测量方法。     

无人机测控数据链路的机载扩频接收机实现

通过具体实例介绍了基于无人机测控数据链路的机载扩频接收机的主要功能和实现原理。测控数据链路采用直接序列扩频技术,具备较强的抗干扰、抗截获和保密能力。系统采用一次变频构架,引入相关信号能量控制链路增益保持A/D采样精度,所有的数字信号处理都基于软件无线电,从而使产品具有软件无线电的诸多优点。重点阐述利用软件无线电,在低信噪比情况下,捕获与跟踪在本扩频接收机的实现。     

GPS中频信号的建模与仿真

中频信号是软件接收机研制重要输入数据,通常可以通过两种方法获得,一种为通过位捕获器对实际GPS信号进行采集获取,另一种为通过对信号进行数学建模后利用软件生成.相对而言,软件方法更加灵活.通过对GPS L1 C/A码信号的研究,推导和简化了GPS中频信号的建模所需的各种数学模型包括信号生成模型、电离层模型、信号传播时间模型等,并在研究的基础上进行了仿真实现,通过频谱对信号的可信度进行了分析.     

GPS软件接收机捕获跟踪算法研究与仿真

本文对GPSL1软件接收机的捕获跟踪算法进行了研究。GPS信号的捕获分别采用时域滑动相关捕获方法和频域快速捕获算法，通过分析比较认为频域快速捕获算法计算量大为减少；在GPS信号跟踪中，采用非相干延迟锁定环（DLL），锁相环（PLL）实现对GPS信号的跟踪。     

GPS软件接收机信号的快速捕获与跟踪

根据GPS软件接收机对数据块进行信号处理的工作方式,研究基于快速傅里叶变换的循环相关法伪码快速捕获与跟踪技术。基于实测GPS中频数据,采用循环相关法在频域计算输入信号与本地信号的相关值,由相位关系得到精细载频。仿真结果表明,该技术能减少运算量,缩短捕获时间。得到的载频能满足跟踪环对频率分辨率的要求。对跟踪结果进行子帧匹配和奇偶校验后,可以获得导航电文信     

强等离子鞘套下航天返回舱相控阵雷达探测问题研究

为了有效应对强等离子鞘套环境下相控阵雷达对再入航天器的探测跟踪任务,设计采用多种波形的航天器目标多通道临空复制跟踪策略。针对等离子鞘套的特性和对雷达探测跟踪的不利影响,分别从信号带宽、检测门限和信号波形等方面进行了适应性改进。采用大的跟踪带宽、慢门限凝聚检测送点方式提升常规线性调频信号检测能力;采用宽、窄带交替跟踪方式提升目标距离分辨与检测能力;采用相位编码波形屏蔽鞘套的影响,稳定跟踪目标;采用正负LFM信号检测产生相关强点迹,以保持目标跟踪;复制跟踪策略在实际任务中进行了应用验证,取得了较好的效果。     

基于载波频率辅助相位的GPS信号跟踪算法

针对传统全球定位系统(GPS)接收机在高动态环境下跟踪性能不理想,提出一种基于载波频率辅助相位的GPS信号跟踪算法。利用锁频环(FLL)辅助锁相环(PLL)的方式代替传统单一跟踪环路,通过卡尔曼(Kalman)滤波器对接收机各跟踪通道中频信号进行综合处理。根据多条跟踪通道的伪距和伪距率残差对系统状态参量进行综合估计,并搭建Kalman滤波器的状态方程和量测方程,给出了跟踪环路反馈量,与传统标量跟踪模式下的跟踪性能进行了对比。仿真结果表明,基于载波频率辅助相位的GPS信号跟踪算法进入稳态时间减小了100 ms,位置误差精度提高了5 m,速度误差精度提高了近3 m/s,在接收机用户快速运动的环境下,能够很好地处理高动态信号。     

一种基于自适应陷波器的科氏流量计频率解算新方法

为提高科氏流量计信号的初始收敛速度和频率跟踪精度,提出了一种频率解算的新方法:首先采用基于burg算法实现的格型IIR自适应陷波器对信号滤波,并短时间跟踪信号频率,其接近收敛时基于简化梯度算法实现的格型自适应陷波器开始并行工作,待简化梯度算法实现的格型自适应陷波器收敛后,前者停止工作,简化梯度算法实现的格型自适应陷波器持续工作直至结束。仿真及实验结果表明,本方法可以获得更快的收敛速度、更高的频率跟踪精度。