三维空间中目标机动加速度的估值问题

建立了描述目标在三维空间中进行切向与法向机动的非线性状态模型。目标切向与法向机动加速度的幅值表示为修正的瑞利-马尔可夫随机过程;法向加速度的方向角则假定在2π区间内具有均匀的概率密度。在仅有含噪声位置观察数据的情况下,发展了一种推广的卡尔曼滤波和自适应算法,并由此获得一种机动目标切向与法向加速度估值的直接方法。提供了某些计算结果以证实方法的有效性。     

机动多目标跟踪问题中关联区域的研究

 本文研究了跟踪多个机动目标时,由滤波算法所获得的新息向量范数的统计性质,关联区域的大小以及接收正确回波的概率。借助拉蒙特卡洛方法,考察了不同的目标状态模型、目标机动加速度及状态噪声方差等因素对所研究的问题的影响。研究表明,文献[1]所提出的机动目标状态模型及相应的自适应算法具有较好的适应目标机动的能力,关联区域的大小及接收正确回波的概率均较为稳定。     

机动目标的模型与跟踪算法

 <正> 在机动目标的“当前”统计模型中,目标的加速度被描述为修正的瑞利—马尔科夫过程,对应的自适应跟踪算法呈现出较好的跟踪特性。文献[2]研究了该模型及其自适应算法在实际的机载雷达跟踪系统的应用;文献[3]进一步推广了基于“当前”模型的MPDAF算法。本文提出一个新的机动目标模型,即假定目标加速度为一高斯—马尔     

一种基于加速度预估计的机动目标跟踪算法

研究分析了几种典型单机动目标模型的建模方法,针对现有单机动目标模型中机动参数需要先验假设,并且不能随目标机动情况的改变而自适应调整的问题,提出了一种加速度预估计模型(Acceleration Pre-estimation Model,APM)。该模型首先用位置量测对机动加速度进行预估计;然后,将加速度估计值作为系统的输入控制项建模;将估计误差看做系统的机动控制项,并作为系统的相关噪声建模。由于APM模型中,加速度机动参数是通过位置量测实时估计得到的,不需要先验假设。与现有单机动目标模型相比,该模型的自适应能力得到了提高。     

引入神经网络的交互式多模型算法

在交互式多模型算法中引入神经网络算法以改进目标跟踪的精度。利用神经网络算法对基于机动目标“当前”统计模型的均值和方差自适应滤波算法进行修改,提高该算法的性能,然后采用交互作用多模型算法跟踪机动目标,提高了机动目标的跟踪精度。     

高超声速滑翔目标自适应跟踪方法

针对机动模式复杂多变的高超声速滑翔目标跟踪问题，提出了一种机动频率自适应跟踪方法。采用介于常速度和常加速度模型之间的Singer模型来表征目标气动力加速度的变化，从而建立跟踪系统的状态方程。根据地基雷达量测量获得系统的量测方程，鉴于距离和角度信息的量级相差较大将其由球形量测量转换为位置量测量。为了适应高超声速滑翔目标灵活多样的机动模式，基于正交性原理和无迹卡尔曼滤波算法实现了Singer模型中机动频率参数的自适应。利用滤波信息计算得到能够反映状态模型误差大小的调整因子，用于放大Singer模型中的机动频率，进而调整状态方程的过程噪声以降低模型误差。通过对2种典型机动轨迹的跟踪仿真，并与交互式多模型等方法进行比较，结果表明所提方法的跟踪精度高、计算量小，能够较好地适应阶跃机动和连续幅值变化的机动。     

基于视线角序列的机动目标视线角速率计算

 依据拦截弹与机动目标间的相对位置关系，采用导引头球面模型，基于当前统计模型，实现了强跟踪状态自适应滤波，计算出了可用于制导的视线角速率。该算法利用导引头球面模型，将测角信息转换成距离信息，进行方差自适应调整和卡尔曼滤波。在只给出视线角序列信息的情况下，根据相对状态滤波结果求出所需的视线角速率。     

增广误差模型算法在目标跟踪中的应用

针对目标机动运行过程中,滤波模型与机动状态模型失配的问题,提出了一种新的增广状态误差滤波模型。不同于现有增广方案,该模型从模型失配所致状态滤波误差的角度出发,将状态估计误差增广为一状态量,通过滤波估计后用其校正原状态量。算法分析表明,该增广滤波模型具有自适应调节多重渐消因子的等效特性,增强了对目标的跟踪能力。基于该增广状态误差滤波模型,给出了滤波算法设计并进行了仿真实验。实验结果表明,基于该模型的滤波算法在对机动目标进行跟踪时具有更强的鲁棒性。     

一种新的基于机动检测的机动目标跟踪算法

针对Kalman滤波跟踪机动目标发散和目前多数自适应Kalman滤波算法对运动模型适应性不强的问题,提出了一种新的基于机动检测的机动目标跟踪算法,通过实时自适应的改变滤波模型提高对机动目标跟踪精度。对这种方法与Kalman滤波算法进行了计算机仿真比较,结果表明,该方法计算量小,可实时精确地自适应匹配目标的运动模型,可实现对机动目标稳定可靠的跟踪。     

基于神经网络的机动目标信息融合与并行自适应跟踪

基于“当前”统计模型和 BP神经网络 ,提出一种新的机动目标神经网络信息融合与并行自适应跟踪算法 ( NIFPAT)。该算法采用双滤波器并行结构 ,利用全状态反馈 ,通过 BP网络调整系统方差以适应目标的运动变化 ,具有对目标各种运动状态的良好自适应跟踪能力     

Tracking targets using adaptive Kalman filtering

A simple algorithm for estimating the unknown process noise variance of an otherwise known linear plant, using a Kalman filter is suggested. The process noise variance estimator is essentially dead beat, using the difference between the expected prediction error variance, computed in the Kalman filter, and the measured prediction error variance. The estimate is used to adapt the Kalman filter. The use of the adaptive filter is demonstrated in a simulated example in which a wildly maneuvering target is tracked     

自寻的导弹攻击机动目标的最优制导规律的研究及实现

提出一种适用于红外和雷达自寻的制导导弹的适应性强、制导精度高、易实现的最优制导规律。为解决工程实现的关键问题,提出了目标机动加速度模型。根据该模型并利用导弹导引头的AGC信号或雷达测距信号,给出了目标机动加速度、导弹与目标相对距离和距离变化率的估计算法。     

An adaptive attitude algorithm based on a current statistical model for maneuvering acceleration

A current statistical model for maneuvering acceleration using an adaptive extended Kalman filter(CS-MAEKF) algorithm is proposed to solve problems existing in conventional extended Kalman filters such as large estimation error and divergent tendencies in the presence of continuous maneuvering acceleration. A membership function is introduced in this algorithm to adaptively modify the upper and lower limits of loitering vehicles' maneuvering acceleration and for realtime adjustment of maneuvering acceleration variance. This allows the algorithm to have superior static and dynamic performance for loitering vehicles undergoing different maneuvers. Digital simulations and dynamic flight testing show that the yaw angle accuracy of the algorithm is 30% better than conventional algorithms, and pitch and roll angle calculation precision is improved by 60%.The mean square deviation of heading and attitude angle error during dynamic flight is less than3.05°. Experimental results show that CS-MAEKF meets the application requirements of miniature loitering vehicles.     

一种IRST双机协同被动探测机动目标定位新方法

针对红外搜索跟踪系统（IRST）双机协同被动探测定位作战使用中，机动目标建模与实际运动失配造成定位误差偏大的问题，研究了一种基于曲线模型的自适应滤波新方法。该方法改进了传统方法根据方向角估计转弯率以及基于帧间插分线加速度估计切向加速度的思路，将转弯率及线加速度联合作为状态变量进行了状态扩维，并推导了扩维后的过程噪声协方差表达式，在缓解传统两层滤波结构带来的计算量大问题外，也提高了切向加速度的估计精度。另外基于反正切函数的值域，结合方向角在四象限间的转移关系，优化了方向角的设计。通过IRST双机协同仿真实例，验证了所提方法对机动目标的适应性更强、目标定位精度更高。     

Interacting acceleration compensation algorithm for trackingmaneuvering targets

The two-stage Kalman estimator has been studied for state estimation in the presence of random bias and applied to the tracking of maneuvering targets by treating the target acceleration as a bias vector. Since the target acceleration is considered a bias, the first stage contains a constant velocity motion model and estimates the target position and velocity, while the second stage estimates the target acceleration when a maneuver is detected, the acceleration estimate is used to correct the estimates of the first stage. The interacting acceleration compensation (IAC) algorithm is proposed to overcome the requirement of explicit maneuver detection of the two-stage estimator. The IAC algorithm is viewed as a two-stage estimator having two acceleration models: the zero acceleration of the constant velocity model and a constant acceleration model. The interacting multiple model (IMM) algorithm is used to compute the acceleration estimates that compensate the estimate of the constant velocity filter. Simulation results indicate the tracking performance of the IAC algorithm approaches that of a comparative IMM algorithm while requiring approximately 50% of the computations     

一种基于冗余测量的自适应卡尔曼滤波算法

针对目前自适应滤波算法的不足,在测量系统量测噪声方差未知的情况下,设计了一种基于冗余测量的自适应卡尔曼滤波(RMAKF)算法。通过对系统冗余测量值的一阶、二阶差分序列进行有效的统计分析,可以准确估计系统量测噪声统计特性,进而在滤波过程中自适应调节噪声方差阵R,提高滤波精度。以全球定位系统/惯性导航系统(GPS/INS)松组合导航系统为对象进行了仿真实验,结果表明该算法在测量系统噪声特性未知或发生改变时,可对其进行准确估计,在采用低精度惯性器件情况下,滤波结果较其他主要自适应卡尔曼滤波算法有较明显的改进。     

A Kalman Filter Based Tracking Scheme with Input Estimation

Beginning with the derivation of a least squares estimator that yields an estimate of the acceleration input vector, this paper first develops a detector for sensing target maneuvers and then develops the combination of the estimator, detector, and a "simple" Kalman filter to form a tracker for maneuvering targets. Finally, some simulation results are presented. A relationship between the actual residuals, assuming target maneuvers, and the theoretical residuals of the "simple" Kalman filter that assumes no maneuvers, is first formulated. The estimator then computes a constant acceleration input vector that best fits that relationship. The result is a least squares estimator of the input vector which can be used to update the "simple" Kalman filter. Since typical targets spend considerable periods of time in the constant course and speed mode, a detector is used to guard against automatic updating of the "simple" Kalman filter. A maneuver is declared, and updating performed, only if the norm of the estimated input vector exceeds a threshold. The tracking sclheme is easy to implement and its capability is illustrated in three tracking examples.     

Extended solution to multiple maneuvering target tracking

An improved algorithm for tracking multiple maneuvering targets is presented. This approach is implemented with an approximate adaptive filter consisting of the one-step conditional maximum-likelihood technique together with the extended Kalman filter and an adaptive maneuvering compensator. In order to avoid the extra computational burden of considering events with negligible probability, a validation matrix is defined in the tracking structure. With this approach, data-association and target maneuvering problems can be solved simultaneously. Detailed Monte Carlo simulations of the algorithm for many tracking situations are described. Computer simulation results indicate that this approach successfully tracks multiple maneuvering targets over a wide range of conditions