组网雷达的改进粒子滤波算法

针对现代战争中有源雷达容易受到干扰和反辐射导弹的摧毁,以及无源雷达隐蔽性高,只能测量方位角度,测量精度小等特点,提出利用集中式有源雷达系统与无源雷达系统协同组网对目标进行跟踪。但是在实际环境中,噪声属性以及有源雷达,无源雷达接收信号的特点决定了组网雷达需要应用非线性滤波技术对信号进行处理。传统的非线性技术包括卡尔曼滤波、扩展卡尔曼滤波(EKF)或无迹卡尔曼滤波(UKF)等方法。非线性近似过程带来的误差相对较大,而且均要求观测噪声和过程噪声为独立或相关的高斯白噪声。而粒子滤波避免了传统非线性滤波方法的缺陷,但是存在粒子退化,于是用EKF和UKF在每一时刻更新粒子,用更新的粒子及其协方差构造重要性函数,然后重采样。仿真实验表明这两种改进粒子滤波方法有很好的效果。     

误差配准在两种纯距离定位算法的应用比较

组网雷达系统中,由于观测信息量的增加,对目标存在多种定位算法。很多情形下,误差配准公式是基于某一定位算法推导而来,误差配准的结果也相应的用来提高此定位算法的定位精度。定位算法的复杂程度不同导致基于此算法推导误差配准公式难度不一致,不同定位算法的定位精度也不尽相同。因此,对两种多距离定位算法的定位精度、误差配准推导难易程度进行了理论分析和仿真计算,给出了定位精度的解析表达式和仿真结果。利用表达式简单的定位算法推导基于最小二乘的误差配准公式,并将误差配准结果反馈给定位精度高的定位算法,以最大程度提高误差配准结果的应用效果,减轻计算复杂度,提高信息的利用度。     

基于优先级队列的多约束无线链路资源调度算法

针对海战场环境中指控数据以先入先出(FIFO)的方式排队发往数据链,当数据链中存在丢失率高和不可达节点时,会导致无线链路带宽资源综合利用率不高的问题,结合仿真模型分析了原因,提出了基于优先级队列的多约束无线链路资源调度算法。通过将该算法与FIFO、优先级队列算法(Priority Queue)进行仿真对比分析,结果表明该算法在存在排队报文时增加了丢失率较低链路上的发送速率,减少了目的不可达报文的发送量,最终降低了重要报文的延迟,提高了数据链的利用率。     

分布式干扰下雷达组网探测效能分析

针对雷达和干扰机两种重要组网体系的战术对抗应用,采用等效噪声功率谱密度表征干扰特性,推导出多干扰源下的雷达探测距离方程;并提出了区域网格融合算法,实现了分布式干扰下雷达网不规则探测区域绘制;对于评估雷达网作战效能具有重要工程应用价值。     

Robust cubature Kalman filter method for the nonlinear alignment of SINS

Nonlinear initial alignment is a significant research topic for strapdown inertial navigation system(SINS).Cubature Kalman filter(CKF)is a popular tool for nonlinear initial alignment.Standard CKF assumes that the statics of the observation noise are pre-given before the filtering process.Therefore,any unpredicted outliers in observation noise will decrease the stability of the filter.In view of this problem,improved CKF method with robustness is proposed.Multiple fading factors are introduced to rescale the obser-vation noise covariance.Then the update stage of the filter can be autonomously tuned,and if there are outliers exist in the observations,the update should be less weighted.Under the Gaussian assumption of KF,the Mahalanobis distance of the innovation vector is supposed to be Chi-square distributed.Therefore a judging index based on Chi-square test is designed to detect the noise outliers,determining whether the fading tune are required.The proposed method is applied in the nonlinear alignment of SINS,and vehicle experiment proves the effective of the proposed method.     

A cooperative interference resource allocation method based on improved firefly algorithm

To deal with the radio frequency threat posed by modern complex radar networks to aircraft, we researched the unmanned aerial vehicle (UAV) formations radar countermeasures, aiming at the solution of radar jamming resource allocation under system countermeasures. A jamming resource allocation method based on an improved firefly algorithm (FA) is proposed. Firstly, the comprehensive factors affecting the level of threat and interference efficiency of radiation source are quantified by a fuzzy comprehensive evaluation. Besides, the interference efficiency matrix and the objective function of the allocation model are determined to establish the interference resource allocation model. Finally, A mutation operator and an adaptive heuristic are integtated into the FA algorithm, which searches an interference resource allocation scheme. The simulation results show that the improved FA algorithm can compensate for the deficiencies of the FA algorithm. The improved FA algorithm provides a more sci-entific and reasonable decision-making plan for aircraft mission allocation and can effectively deal with the battlefield threats of the enemy radar network. Moreover, in terms of convergence accuracy and speed as well as algorithm stability, the improved FA algorithm is superior to the simulated annealing algorithm (SA), the niche genetic algorithm (NGA), the improved discrete cuckoo algorithm (IDCS), the mutant firefly algorithm (MFA), the cuckoo search and fireflies algorithm (CSFA), and the best neighbor firefly algorithm (BNFA).     

Appointment scheduling at a multidisciplinary outpatient clinic using stochastic programming

The purpose of this paper is to investigate the problem of constructing an appointment template for scheduling patients at a specific type of multidisciplinary outpatient clinic called an integrated practice unit (IPU). The focus is on developing and solving a stochastic optimization model for a back pain IPU in the face of random arrivals, an uncertain patient mix, and variable service times. The deterministic version of the problem is modeled as a mixed integer program with the objective of minimizing a weighted combination of clinic closing time (duration) and total patient waiting time (length of stay). A two‐stage stochastic program is then derived to account for the randomness and the sequential nature of the decisions. Although it was not possible to solve the two‐stage problem for even a limited number of scenarios, the wait‐and‐see (WS) problem was sufficiently tractable to provide a lower bound on the stochastic solution. The introduction of valid inequalities, limiting indices, and the use of special ordered sets helped to speed up the computations. A greedy heuristic was also developed to obtain solutions much more quickly. Out of practical considerations, it was necessary to develop appointment templates with time slots at fixed intervals, which are not available from the WS solution. The first to be derived was the expected value (EV) template that is used to find the expected value of the EV solution (EEV). This solution provides an upper bound on the objective function value of the two‐stage stochastic program. The average gap between the EEV and WS solutions was 18%. Results from extensive computational testing are presented for the EV template and for our adaptation of three other templates found in the literature. Depending on the relative importance of the two objective function metrics, the results demonstrate the trade‐off that exists between them. For the templates investigated, the “closing time” ranged from an average of 235 to 275 minutes for a 300‐minute session, while the corresponding “total patient time in clinic” ranged from 80 to 71 minutes.     

Holmes’ front: constructing a new face of battle for America’s Civil War

ABSTRACT

In his seminal study of the changing nature of warfare between Agincourt and the Somme, military historian John Keegan proposed that future historians might consider combatants’ emotions in their assessments of the impact and nature of conflict. Recent years have witnessed the emergence of the history of emotions as an analytical approach, but rarely, if ever, is this directed toward the study of military history, far less the history of insurgencies and counter-insurgencies. This paper examines America’s civil war (1861–1865) as a case study of the ways in which an emotional history approach might illuminate not the physical experiences of but rather the immediate and longer-term reactions to counter-insurgency conflict through a focus on one specific individual, the future Supreme Court Justice Oliver Wendell Holmes, Jr. It proposes that Holmes, whilst not a man of the ranks, nevertheless can reveal the wider ramifications of civil war and its emotional impact, both individually and culturally. As a relatively limited internecine war, one not fought by professional armies but by volunteer forces, America’s civil war highlights the ways in which the soldier’s response points us toward the kind of emotional revolution that has, to date, mainly been located within the European nations.