Target tracking with fast adaptive revisit time based on steady state IMM filter

Time is a valuable resource in phased-array radar and proper use of it enables the radar to track more targets simultaneously. Appropriate usage of time in the tracking means the radar can minimize the revisit rate of the tracked targets while the accuracy of targets position estimation is still acceptable. In this paper, a new method for determination of target revisiting time is proposed which has relatively low computational cost and uses steady state filters with Interactive Multiple Models structure. The proposed algorithm allows the radar designer to determine the filter parameters by some criteria such as the desired accuracy and targets maneuver. The performance of the proposed method is compared with the conventional filters for standard maneuverable flying targets. Simulations show the better accuracy and reliability of the proposed method beside its less target revisit time and computational load.     

Fusion of Gaussian mixture models for possible mismatches of sensor model

This paper addresses estimation fusion in the presence of possible mismatches of sensor model. The main concerns of the paper lie in two aspects. One is to improve the filter performance of the single sensor when there are possible mismatches about the sensor model. The other one is to adopt a good fusion scheme to combine local estimates. For these purposes, the measurement process of the local sensor is modeled by multiple models firstly, and the IMM (interacting multiple model) estimator is implemented to produce estimates for individual models. Next, we describe the local estimate by a Gaussian mixture rather than a single Gaussian density in the baseline IMM filter. Such a GMM (Gaussian mixture model) representation of the system state allows us to keep the detailed information about the local tracker, which contributes to the further fusion if treated properly. Finally, the fusion of two Gaussian mixtures is done in the probabilistic framework, and a close-form solution is derived without complex numerical operations. Simulation results demonstrate the effectiveness of the proposed approach.     

一种可行的高超声速飞行器跟踪算法

通过分析临近空间高超声速飞行器的运动特性,建立了高超声速飞行器运动模型,并运用交互式多模型(IMM)算法对其进行跟踪,通过100次蒙特卡罗实验,获得了误差较小的理想跟踪效果,说明了IMM算法用于高超声速目标跟踪中的可行性.     

改进的交互式当前统计模型算法

交互式多模型(IMM)算法是一种有效的机动目标跟踪算法,但其性能与模型的选择、个数以及参数有关,而当前统计模型(CSM)是一种模型自适应的方法.因此,利用CSM为IMM提供模型,提出一种改进的交互式当前统计模型算法.仿真结果表明,该算法能有效地提高机动目标跟踪精度.     

交互式多模型算法的远程控制机器人设计

设计了一种远程控制机器人,并采用了交互式多模型(IMM)算法,使操作人员在远离危险的情况下完成控制操作,提高了机器人服务器跟踪目标的能力。实验证明:机器人系统在远程可控性和实时性上有良好的表现。     

基于外辐射源的机动目标单站无源定位IMM算法

探讨一种基于外辐射源的信号到达时间差 (TDOA)和信号到达方向 (DOA)信息 ,利用固定单站对机动目标进行无源定位与跟踪的新方法。在建立目标机动模型与量测方程的基础上 ,运用交互式多模型 (IMM)算法 ,实现对机动目标进行定位与跟踪。通过计算机仿真 ,验证了该方法的正确性与有效性     

基于IMM的伺服系统前馈控制设计

IMM算法主要应用于特性随时间变化的系统的状态估计．它特别适用于机动目标跟踪。文中提出的基于IMM的跟踪雷达伺服系统前馈控制设计思想．并通过仿真，验证了该控制方法可以满足伺服系统对大机动目标快速、高精度的跟踪要求。     

两级交互式多模型算法

在混合估计中,交互式多模型滤波器(IMM--Interacting Multiple Model)以其优越 的性能而受到广泛的研究.由于马尔可夫参数的限定,交互式多模型在模型数较多时会出现精 度下降,从而限制了它在高维参数空间建模的有效性.利用模型集的概念,首次提出了双马氏 过程的模型切换假设,从而构造出一种两级交互式多模型滤波器.通过辨识系统噪声的多个统 计参数比较了两级交互式多模型滤波器与常规交互式多模型滤波器.结果表明:对于大信噪比 信源(即小的量测噪声),两级交互式多模型滤波器与常规交互式多模型滤波器性能基本相当; 而对于小信噪比信源(即大的量测噪声),两级交互式多模型滤波器明显优于常规交互式多模型 滤波器.     

高速高机动目标自适应IMM跟踪算法

针对目标运动过程中有转弯机动等复杂运动模式的高速高机动目标,设计了自适应两层IMM跟踪算法。该算法内层由改进的机动目标当前统计模型构成,把目标速度方向角作为伪测量值进行滤波,实时获得目标的角速度和角加速度;外层模型由常速模型和曲线模型构成,把内层模型得到的切向加速度和转弯角速度作为曲线模型参数,利用IMM算法进行滤波。仿真结果表明,该算法对高速高机动目标具有较高的跟踪精度,算法实现简单,具有一定的实际应用价值。     

Implementation of Multimodel-Based PID and Intelligent Controller for Simulated and Real-Time Temperature Control of Injection Molding Machine

The proposed real-time multimodel for the injection molding process mainly contributes to the barrel temperature control. Good control of the plastic melt temperature is very important for injection molding in reducing the operator setup time, ensuring product quality, and preventing thermal degradation of the melt. The controllability and set points of the barrel temperature also depend on the precise monitoring and control of the plastic melt temperature. Motivated by the practical temperature control of injection molding, this article proposes a multimodel-based proportional integral derivative (PID) control scheme in real-time and the simulation studies of the PID, fuzzy, and adaptive neuro fuzzy inference system (ANFIS) control schemes. The injection molding process consists of three zones, and the mathematical model for each zone is different. The control output for each zone controller is assigned a weight, based on the computed probability of each model, and the resulting action is the weighted average of the control moves of the individual zone controller.