自适应强跟踪卡尔曼滤波器载波环设计

针对自适应Q_k/R_k卡尔曼滤波算法在大频偏或运动场景切换时存在收敛速度慢、易失锁问题,提出了一种自适应强跟踪滤波算法,该算法从自适应Q_k/R_k卡尔曼滤波算法出发,引入强跟踪技术。在稳态场景下,采用自适应Q_k/R_k算法保证跟踪环路有较好的跟踪精度;当场景变化时,自适应强跟踪算法保证跟踪环路有更稳健的动态性能。仿真结果表明,所提出的设计方法能够有效提高载波跟踪环路的跟踪精度和鲁棒性。     

空中目标敌我识别空域关联函数算法

空中目标敌我识别需要根据多信源、多因子综合判断目标属性.空域关联函数的计算是其中的一个关键问题,其实质是用数学方法判断平面内给定的一个点是否位于给定的单连通区域内部.解决此问题常用的方法有几何法、网格法和射线法等.针对此问题,基于复变函数理论,提出了环路积分算法,并推导出了针对任意多边形的环路积分算法的计算公式.     

选取环路切换策略的高动态载波跟踪算法研究

针对基于环路切换的传统GPS跟踪环路,会因为载体运动背景的复杂性而出现环路切换频繁,造成环路抖动和跟踪效果差的问题,提出了基于环路切换策略的高动态载波跟踪算法。该算法首先利用频率、相位判决器选择带有切换策略的子环路工作,然后利用切换策略模块去控制子环路对信号进行跟踪。仿真结果表明:载体在信号强度为45 d B·Hz,以5 g和10 g加速度分时间段做匀加速运动的场景下,基于环路切换策略的高动态载波跟踪算法能使得环路的切换次数和抖动现象明显减少,跟踪效果更佳。     

基于扩展卡尔曼滤波的环路跟踪算法仿真分析

针对接收机定位动态适应性需求,分析了采用扩展卡尔曼滤波的接收机环路跟踪算法,给出了基于扩展卡尔曼滤波的接收机环路跟踪算法的数学模型,基于采集卫星信号模拟器输出的射频信号与程序生成的接收机中频信号,从动态适应性和跟踪灵敏度两个方面,仿真分析了基于标准卡尔曼滤波和基于扩展卡尔曼滤波的环路跟踪算法性能。仿真结果表明,基于扩展卡尔曼滤波的环路跟踪算法在动态环境中具有更好的跟踪性能,且能够改善跟踪灵敏度1 dB,这表明其对复杂应用场景具有更好的适应性。该分析结果能够为算法工程化提供参考和借鉴。     

高速并行Gardner算法设计与实现

随着空间探测任务逐步增加、空间信道频谱资源日趋紧张,传统Gardner定时同步算法已经无法满足高速数传系统高通量、高可靠性的需求。为了提高Gardner定时同步算法的吞吐率并增大可纠正误差范围,提出一种高速并行Gardner算法。为了保证插值精度同时减少乘法器消耗,设计了一种并行分段抛物线插值滤波器;为了便于并行流水线设计和最佳采样点选取,构建了计数模块和定时缓存调整模块;为了提高等价吞吐率,重构了流水线并行环路滤波器结构和并行数控振荡器结构。结果表明,该算法等价吞吐率可达1 739.13 Msps,数字信号处理器资源消耗可减少44%,可纠正2×10^-3的定时误差。     

基于副载波参考波形方法的GNSS双载波环多径抑制技术

为了实现对高阶BOC信号的无模糊和抗多径接收,本文将码相关参考波形的闸波设计思路应用于GNSS双载波环路接收方法的副载波锁相环,通过在副载波锁相环中引入设计的闸波参与信号的相干积分过程,使双载波环法具备抗多径性能,同时该方法并不需要额外引入相关器。本文对该设计方法的理论和具体实现进行了阐述和分析,从副载波多径误差包络和跟踪精度两方面对改进的双载波环路方法性能进行了评估。仿真结果显示,采用的算法与双载波环路法相比可以降低BOC(1, 1)信号81.1%的副载波多径误差包络面积,以及BOC(14, 2)信号75.1%的副载波多径误差包络面积。但是,改进的双载波环路法将带来-6dB的相干积分后载噪比损失,降低跟踪精度。因此,在闸波设计参数设计上,需要谨慎选择以平衡算法的多径抑制和跟踪精度性能。综合来看,该方法适用于解决非弱信号条件下及多径环境下的高阶BOC信号接收问题。     

基于能量度量的星载AIS信号自适应码元同步抽取算法

基带信号码元同步抽取是目前星载AIS(Automatic Identification System)信号处理中的一个难点问题,针对过零检测和迟门早门等现有算法存在的不足,提出了基于能量度量的基带信号自适应码元同步抽取算法。将基带信号根据采样率和码速率的比值分割成相同的段的组合,之后分别计算每个段内相应样点的能量度量,最后选择使能量度量最大时的样点集作为最优同步抽取样点,该样点集具有最大信噪比。仿真结果验证了该算法误码率优于过零检测和迟门早门算法。     

采用副载波参考波形方法的GNSS双载波环多径抑制技术

为了实现对高阶二进制偏移载波(BOC)信号的无模糊和抗多径接收,将码相关参考波形的闸波设计思路应用于GNSS双载波环路接收方法的副载波锁相环。在副载波锁相环中引入设计的闸波参与信号的相干积分过程,使双载波环法具备抗多径性能且不需要额外引入相关器。对该设计方法的理论和具体实现进行阐述和分析,从副载波多径误差包络和跟踪精度两方面对改进的双载波环路方法性能进行评估。仿真结果显示,采用的算法与双载波环路法相比,可以降低BOC(1,1)信号81.1%的副载波多径误差包络面积以及BOC(14,2)信号75.1%的副载波多径误差包络面积。但是,改进的双载波环路法会带来-6 dB的相干积分后载噪比损失,降低跟踪精度。因此,在闸波参数设计上,需要谨慎选择以平衡算法的多径抑制和跟踪精度性能。综合来看,该方法适用于解决非弱信号条件下及多径环境下的高阶BOC信号接收问题。     

利用实时小波降噪的抗电离层闪烁载波跟踪

电离层闪烁影响下的导航信号载波跟踪呈现非平稳、非线性和非高斯特性,这使得传统基于鉴别器和Kalman滤波的载波跟踪改进算法并不能有效减弱电离层闪烁的影响。提出了基于实时小波降噪的抗电离层闪烁载波跟踪算法,其能够更好地刻画闪烁信号的非平稳特性。通过对跟踪环路同相与正交支路的积分结果进行小波降噪,有效滤除了电离层闪烁引入的非高斯噪声,减弱了闪烁对环路鉴别器的不良影响,提高了跟踪环路的精度与稳健性。实验验证了相关分析和小波降噪跟踪算法的有效性。     

基于可变增益的码跟踪环路优化设计

针对在突发直接序列扩频(DSSS)信号跟踪中,信号持续时间短,而传统延迟锁定环(DLL)存在入锁时间长的问题,提出一种可变增益码跟踪环路设计方法。介绍了DLL环路基本原理和环路结构,对环路优化进行了理论分析,最后采用可变增益设计方法,自适应的调整环路增益,达到锁定时间和跟踪精度的平衡。通过仿真分析,在相同跟踪精度上,与传统DLL环路相比,锁定时间大大缩短。     

武警部队开进路线问题的蚂蚁算法研究

部队开进路线问题是类似于TSP的NP问题。本文将蚂蚁算法应用于部队开进路线问题中，在综合考量每条路径的通行条件、隐蔽性、迂回道路数量基础上，通过层次分析法求出每条路径的权值，进而运用蚂蚁算法原理对问题设计算法。算法用Delphi实现，通过对实际部队开进路线问题的测试，得到较好结果。相对于传统的经验选择路径方法，此方法更少依赖选择主体的主观性，从而更具客观性与可重复性。     

UCAV空面多目标攻击三维轨迹规划技术

研究了单架无人作战飞机(UCAV)攻击多个地面目标的三维轨迹规划问题。首先,将问题形式化为一类特殊的旅行商问题(TSP),即带动力学约束的邻域访问TSP问题(DCTSPN)。其次,针对规划空间维度过高、搜索代价过大的问题,提出了一种基于概率路标图(PRM)的方法。该方法借鉴了基于采样的运动规划方法的思想,并结合多种组合优化技术,将原本连续状态空间中的轨迹规划问题转化为离散拓扑图上的路由问题。求解过程分为离线预处理和在线查询两个阶段。离线阶段采用Halton拟随机采样算法及Noon-Bean转换方法,将原问题转化为经典的非对称旅行商问题(ATSP);在线阶段根据战场态势的实时变化,快速更新路标图,然后采用LKH算法在线求解问题的近似最优解。为了保证生成的飞行轨迹满足平台的运动学/动力学约束,算法基于Gauss伪谱法构建了局部轨迹规划器。最后,以攻击时间最短为优化指标对算法进行了仿真实验。结果表明,本文提出的方法能够以较高的精度和在线收敛速度生成真实可行的、较优的多目标攻击轨迹。     

基于支持向量机和遗传算法的末敏弹系统效能参数优化设计

末敏弹是一种先进的新型弹药,由于其结构复杂,影响因素多,所以对其进行全面的系统优化设计比较困难。文中探讨了基于支持向量机的末敏弹命中概率预报模型的建模方法,并采用遗传算法对系统效能参数进行了优化,获得了影响系统效能的几个主要因素的合理搭配,为末敏弹的系统效能研究提供了依据。     

Clustered coverage orienteering problem of unmanned surface vehicles for water sampling

This study investigates a clustered coverage orienteering problem (CCOP), which is a generalization of the classical orienteering problem. The problem is widely motivated by the emerging unmanned techniques (eg, unmanned surface vehicles and drones) applied to environmental monitoring. Specifically, the unmanned surface vehicles (USVs) are used to monitor reservoir water quality by collecting samples. In the CCOP, the water sampling sites (ie, the nodes) are grouped into clusters, and a minimum number of nodes must be visited in each cluster. With each node representing a certain coverage area of the water, the objective of the CCOP is to monitor as much as possible the total coverage area in one tour of the USV, considering that overlapping areas provide no additional information. An integer programming model is first formulated through a linearization procedure that captures the overlapping feature. A two-stage exact algorithm is proposed to obtain an optimal solution to the problem. The efficiency and effectiveness of the two-stage exact algorithm are demonstrated through experiments on randomly generated instances. The algorithm can effectively solve instances with up to 60 sampling sites.     

Heuristic solution to a discrete collection model

A discrete time Collection Model is formulated, involving the completion of a touring objective on a network with stochastic node states. Heuristic touring strategies are constructed, there being as yet inadequate analytic results for its optimal solution. Effectiveness of the heuristics is assessed by comparing expected tour times under the heuristics with expected tour times given perfect information. A branch and bound algorithm is presented for computing the perfect information tour times.     

Transformations of node‐balanced routing problems

This article describes a polynomial transformation for a class of unit‐demand vehicle routing problems, named node‐balanced routing problems (BRP), where the number of nodes on each route is restricted to be in an interval such that the workload across the routes is balanced. The transformation is general in that it can be applied to single or multiple depot, homogeneous or heterogeneous fleet BRPs, and any combination thereof. At the heart of the procedure lies transforming the BRP into a generalized traveling salesman problem (TSP), which can then be transformed into a TSP. The transformed graph exhibits special properties which can be exploited to significantly reduce the number of arcs, and used to construct a formulation for the resulting TSP that amounts to no more than that of a constrained assignment problem. Computational results on a number of instances are presented. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 370–387, 2015     

Aggregating courier deliveries

We consider the problem of efficiently scheduling deliveries by an uncapacitated courier from a central location under online arrivals. We consider both adversary‐controlled and Poisson arrival processes. In the adversarial setting we provide a randomized (3βΔ/2δ ? 1) ‐competitive algorithm, where β is the approximation ratio of the traveling salesman problem, δ is the minimum distance between the central location and any customer, and Δ is the length of the optimal traveling salesman tour overall customer locations and the central location. We provide instances showing that this analysis is tight. We also prove a 1 + 0.271Δ/δ lower‐bound on the competitive ratio of any algorithm in this setting. In the Poisson setting, we relax our assumption of deterministic travel times by assuming that travel times are distributed with a mean equal to the excursion length. We prove that optimal policies in this setting follow a threshold structure and describe this structure. For the half‐line metric space we bound the performance of the randomized algorithm in the Poisson setting, and show through numerical experiments that the performance of the algorithm is often much better than this bound.     

An integer programming model for the weekly tour scheduling problem

We study a workforce planning and scheduling problem in which weekly tours of agents must be designed. Our motivation for this study comes from a call center application where agents serve customers in response to incoming phone calls. Similar to many other applications in the services industry, the demand for service in call centers varies significantly within a day and among days of the week. In our model, a weekly tour of an agent consists of five daily shifts and two days off, where daily shifts within a tour may be different from each other. The starting times of any two consecutive shifts, however, may not differ by more than a specified bound. Furthermore, a tour must also satisfy constraints regarding the days off, for example, it may be required that one of the days off is on a weekend day. The objective is to determine a collection of weekly tours that satisfy the demand for agents' services, while minimizing the total labor cost of the workforce. We describe an integer programming model where a weekly tour is obtained by combining seven daily shift scheduling models and days‐off constraints in a network flow framework. The model is flexible and can accommodate different daily models with varying levels of detail. It readily handles different days‐off rules and constraints regarding start time differentials in consecutive days. Computational results are also presented. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 607–624, 2001.     

One-facility location with rectilinear tour distances

This article concerns the location of a facility among n points where the points are serviced by “tours” taken from the facility. Tours include m points at a time and each group of m points may become active (may need a tour) with some known probability. Distances are assumed to be rectilinear. For m ≤ 3, it is proved that the objective function is separable in each dimension and an exact solution method is given that involves finding the median of numbers appropriately generated from the problem data. It is shown that the objective function becomes multimodal when some tours pass through four or more points. A bounded heuristic procedure is suggested for this latter case. This heuristic involves solving an auxiliary three-point tour location problem.     

A finite descent theory for linear programming,piecewise linear convex minimization,and the linear complementarity problem

A descent algorithm simultaneously capable of solving linear programming, piecewise linear convex minimization, and the linear complementarity problem is developed. Conditions are given under which a solution can be found in a finite number of iterations using the geometry of the problem. A computer algorithm is developed and test problems are solved by both this method and Lemke's algorithm. Current results indicate a decrease in the number of cells visited but an increase in the total number of pivots needed to solve the problem.