共查询到19条相似文献,搜索用时 531 毫秒
1.
仅有角测量的被动式机动目标跟踪 总被引:7,自引:0,他引:7
以往的被动式跟踪研究往往假定目标作匀速直线运动, 采用目标与跟踪站的相对距离和速度为状态变量, 因而相应的跟踪滤波器不能跟踪机动目标. 研究了仅有角测量的机动目标跟踪问题, 采用目标的位置、速度及加速度作为状态变量, 并对测量方程进行适当变换, 推导出一种伪线性机动目标自适应跟踪算法, 可用于单站或多站被动式机动目标跟踪. 大量的仿真研究表明了本算法的有效性, 其中多站跟踪比单站跟踪具有更高的精度、算法稳定性和快速收敛性. 相似文献
2.
针对传统的EKF-IMM算法鲁棒性较差等问题,提出了一种基于强跟踪滤波器(STF)的交互式多模型算法。该算法通过引入强跟踪滤波器(STF)的渐消因子,实现了对滤波器增益的实时调节,从而提高了系统对机动目标的自适应跟踪能力和跟踪精度。仿真结果表明,在目标不发生机动时,该算法和EKF-IMM算法的跟踪效果相近,在目标发生强机动时,该算法在径向速度和方位角的跟踪精度要优于EKF-IMM算法;提出的算法具有更优的机动目标跟踪性能。 相似文献
3.
4.
根据二维空间内目标作匀速转向运动的特点,提出一种基于航向变化的目标加速度实时估计方法,并在此基础上采用采样卡尔曼滤波器对该机动目标进行跟踪.仿真结果表明,该方法不仅能够检测出目标机动开始时刻和终止时刻,而且还能近似估计出快速机动目标的加速度大小,与扩展卡尔曼滤波器相比,采样卡尔曼滤波器具有较好的跟踪精度. 相似文献
5.
6.
研究目标跟踪精度问题,针对单站被动跟踪是非线性不可观系统,容易导致滤波器发散和估计结果的不唯一.在满足系统的可观测性条件下,提出了一种改进的Bayes框架下的次优估计交互式多模型无迹卡尔曼滤波(IMM-UKF)算法.在目标做强机动时,由于滤波残差特性变换影响IMM-UKF算法的跟踪精度,引入带有机动检测环节的交互式多模型IMM-UKF算法,在目标作强机动时改善了常规IMM-UKF算法失效状况,并提高了跟踪精度.仿真结果表明,算法具有较好的收敛速度和跟踪精度. 相似文献
7.
8.
针对现有的均值漂移算法不能适应非刚性目标的复杂运动情况,本文首先利用基于边缘的背景减方法去除背景干扰;然后利用GVFSnake技术提取出目标轮廓,结合目标轮廓改进了传统的颜色直方图;最后基于该颜色直方图结合卡尔曼滤波器或粒子滤波器改进了传统的均值漂移算法。实验表明,该算法可以实现快速的非刚性目标跟踪,对目标的不
不规则运动和严重遮挡有很好的鲁棒性。 相似文献
不规则运动和严重遮挡有很好的鲁棒性。 相似文献
9.
对于带未知噪声统计的机动目标跟踪系统,本文提出了一种新的自校正跟踪方案,它由一个简单的自校正α-β-γ滤渡器、机动输入判决器和带输入估计的自校正α-β-γ滤波器组成,当判决机动加速度输入出现时,则简单的自校正α-β-γ滤波器立即被用带输入估计的自校正α-β-γ滤波器代替,以保证跟踪滤波器的精度,仿真结果说明了新方案的有效性。 相似文献
10.
11.
Kangwagye Samuel JaeWeon Choi 《International Journal of Control, Automation and Systems》2018,16(6):2763-2771
We propose a method of improving tracking filter performance of a highly maneuvering target with mixed system noises in this paper. A case study of an off-road high speed moving target is considered. The system noises consist of white Gaussian noises generated from target motion models and additional colored noises arising from the effect of rough and uneven terrain profile. we design the colored noise first order discrete Markov dynamic system representing terrain conditions. Tracking is done by using an IMM filter with discrete white noise acceleration and horizontal coordinated turn models. The designed colored noise dynamic model is augmented with each of the motion models. We use Kalman filter for linear DWNA model while extended and unscented Kalman filters are used for nonlinear HCT model. A test scenario is setup and simulations are carried out. For filter performance comparison purposes, two more cases are considered i.e., systems with white noncorrelated system noises and the system correlated noise cases. Results show that the proposed method outperforms the traditional error treatment methods in terms of robustness, small mean square error, and acceptable computation load and data processing time. 相似文献
12.
建立了图象序列中目标形心位置测量方程,并针对目标机动性,采用一种解耦的并行卡尔曼滤波跟踪算法,即速度滤波器和加速度滤波器并行独立运算,加速度滤波器的输出用于校正速度滤波器的结果,根据探测到的目标机动性情况,加速度滤波器可以实时切换,降低了计算量和存储量,提高了跟踪的实时性,仿真结果表明该算法具有很好的跟踪性能。 相似文献
13.
Jin-Long Yang Hong-Bing Ji Zhen-Hua Fan 《International Journal of Control, Automation and Systems》2013,11(2):306-316
Taking into account the difficulties of multiple maneuvering target tracking due to the unknown target number and the uncertain acceleration, a novel multiple maneuvering target tracking algorithm based on the Probability Hypothesis Density (PHD) filter and Modified Input Estimation (MIE) technique is proposed in this paper. First, the unknown acceleration vector is added to the target state to form a new augmented state vector. Then, strong tracking filter multiple fading factors are introduced to the MIE method which can adjust the prediction covariance and the corresponding filter gain at different rates in real time, so that the MIE method can adaptively track high maneuvering targets well. Finally, we combine this adaptive MIE method with the PHD filter, which can effectively track multiple maneuvering targets without much prior information. Simulation results show that the proposed algorithm has a higher tracking precision and a better real-time performance than the conventional maneuvering target tracking algorithms. 相似文献
14.
To increase the performance of maneuvering target tracking, an algorithm utilizing input estimation with multiple model based on two independent mode sets is suggested in this paper. The proposed algorithm consists of hypothesized multiple filters to estimate the unknown target acceleration and a test statistic developed from a modified version of the generalized likelihood ratio test to detect the maneuver onset time. An efficient algorithm for the target acceleration estimation is derived to reduce the computational burden of multiple model estimation. A numerical analysis is carried out to obtain the proper window length and the average delay of the algorithm. Performance of the proposed algorithm is evaluated by a series of simulation runs. 相似文献
15.
Hong Ping Gao Chang Ho Yu Jae Weon Choi Tae Il Seo 《International Journal of Control, Automation and Systems》2011,9(3):506-514
This paper presents a study involving prediction of a complicated maneuvering target, with the aim of improving the tracking
performance of a fire control system (FCS). In this study, we predict the position of a complicated maneuvering target 5 s
in advance using the information up to the current time. Because of the large error caused by the complicated maneuvers and
the long prediction time interval, the mechanical system of the fire control system will take a heavy load. In order to cope
with this problem, several approaches to decreasing the prediction error have been proposed including the prediction algorithms
based on the multiple model(MM) filter, interacting multiple model (IMM) filter, and variable dimension with input estimation
(VDIE) filter. Finally, comparative simulation results are presented to verify the performance of the filters. 相似文献
16.
针对多机动目标跟踪中,目标数目未知及加速度不确定的问题,提出一种强跟踪输入估计(modifiedinputestimation,MIE)概率假设密度多机动目标跟踪算法.在详细分析算法的基础上,通过引入强跟踪多重渐消因子,以不同速率实时调节滤波器各个通道的预测协方差及相应的滤波器增益,从而实现MIE算法对加速度未知或发生人幅度突变的机动目标白适应跟踪能力;并将该算法与概率假设密度滤波算法有效结合,町以较好地跟踪未知数目的多机动目标.仿真结果表明,新算法比传统的多机动目标跟踪算法具有更岛的跟踪精度,且具有较好的实时性. 相似文献
17.
Current statistical model (CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter (KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules, so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance. 相似文献
18.
机动目标模型的Monte Carlo仿真研究 总被引:5,自引:2,他引:3
该文对机动目标模型进行了Monte Carlo仿真研究。提出了一种描述机动目标运动状态的自适应高斯模型,在这种模型中,机动目标的加速度被认为是具有非零均值、时间相关的随机过程,并假定其概率密度函数服从高斯分布;对机动目标模型进行了Monte Carlo仿真研究,结果表明该模型对机动目标在不同机动方式下的位置、速度和加速度均有良好的跟踪精度。 相似文献