發(fā)布時間：2018-11-12 16:56

【摘要】：為了提高跟蹤系統對水面機動目標的跟蹤能力,本文將水面目標建模為橢圓形面目標,提出一種模糊化模型概率的交互多模型(interacting multiple model,IMM)強無跡粒子濾波算法。首先,利用現代高分辨率雷達獲得的面目標擴展測量,給出了基于面目標的跟蹤測量方程。其次,將強無跡粒子濾波(strong unscented particle filter,SUPF)算法引入到IMM中得到IMM-SUPF。該SUPF算法利用強跟蹤無跡卡爾曼濾波(strong tracking unscented Kalman filter,STUKF)產生粒子建議分布。由于STUKF采用漸消因子調整UKF的狀態(tài)模型協方差和觀測模型協方差的比例,使得建議分布更符合真實狀態(tài)的后驗概率分布,從而提高了IMM算法中子模型濾波器的估計精度。最后,基于模糊隸屬度函數對粒子的模型概率進行模糊化,從而在提高真實模型濾波器中粒子模型概率的同時,減小非匹配模型濾波器中粒子模型概率,進而提高IMM算法的估計融合精度。Monte-Carlo仿真實驗表明,相比于傳統的基于質點目標的IMM-UPF算法,文中所提的基于面目標的IMM算法跟蹤精度更高,且所提算法的誤差超調量更小,收斂更快。此外,所提面目標IMM算法的跟蹤精度也要高于面目標IMM-UPF算法。不同于傳統的質點目標IMM算法,文中將水面目標建模為橢圓形面目標,并利用面目標擴展測量信息設計了模糊化模型概率的IMM-SUPF算法。該算法進一步提高了跟蹤系統對水面機動目標的跟蹤能力。
[Abstract]:In order to improve the tracking ability of the tracking system, the surface target is modeled as an elliptical surface target in this paper, and an interactive multi-model (interacting multiple model,IMM (strong unscented particle filter) algorithm with fuzzy model probability is proposed. Firstly, using the extended surface target measurement obtained by modern high resolution radar, the tracking measurement equation based on surface target is given. Secondly, the strong unscented particle filter (strong unscented particle filter,SUPF) algorithm is introduced into IMM to get IMM-SUPF.. The SUPF algorithm uses strong tracking unscented Kalman filter (strong tracking unscented Kalman filter,STUKF) to generate particle recommendation distribution. Because STUKF adopts fading factor to adjust the ratio of state model covariance and observation model covariance of UKF, the proposed distribution is more in line with the posteriori probability distribution of the real state, thus improving the estimation accuracy of the neutron model filter of the IMM algorithm. Finally, based on the fuzzy membership function, the probability of particle model is fuzzied, so as to increase the probability of particle model in the real model filter and reduce the probability of particle model in the non-matching model filter. Monte-Carlo simulation results show that compared with the traditional IMM-UPF algorithm based on particle target, the proposed IMM algorithm has higher tracking accuracy than the traditional IMM-UPF algorithm based on particle target. The error overshoot of the proposed algorithm is smaller and the convergence is faster. In addition, the tracking accuracy of the proposed IMM algorithm is higher than that of the IMM-UPF algorithm. Different from the traditional particle target IMM algorithm, the surface target is modeled as an elliptical surface target in this paper, and the IMM-SUPF algorithm of fuzzy model probability is designed by using the surface target extension measurement information. The algorithm further improves the tracking ability of the tracking system for maneuvering targets on water surface.
【作者單位】： 南京理工大學自動化學院;
【基金】：國家自然科學基金資助項目(61273076)
【分類號】：TN953

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