【摘要】：為了提高跟蹤系統(tǒng)對水面機(jī)動(dòng)目標(biāo)的跟蹤能力,本文將水面目標(biāo)建模為橢圓形面目標(biāo),提出一種模糊化模型概率的交互多模型(interacting multiple model,IMM)強(qiáng)無跡粒子濾波算法。首先,利用現(xiàn)代高分辨率雷達(dá)獲得的面目標(biāo)擴(kuò)展測量,給出了基于面目標(biāo)的跟蹤測量方程。其次,將強(qiáng)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(qiáng)跟蹤無跡卡爾曼濾波(strong tracking unscented Kalman filter,STUKF)產(chǎn)生粒子建議分布。由于STUKF采用漸消因子調(diào)整UKF的狀態(tài)模型協(xié)方差和觀測模型協(xié)方差的比例,使得建議分布更符合真實(shí)狀態(tài)的后驗(yàn)概率分布,從而提高了IMM算法中子模型濾波器的估計(jì)精度。最后,基于模糊隸屬度函數(shù)對粒子的模型概率進(jìn)行模糊化,從而在提高真實(shí)模型濾波器中粒子模型概率的同時(shí),減小非匹配模型濾波器中粒子模型概率,進(jìn)而提高IMM算法的估計(jì)融合精度。Monte-Carlo仿真實(shí)驗(yàn)表明,相比于傳統(tǒng)的基于質(zhì)點(diǎn)目標(biāo)的IMM-UPF算法,文中所提的基于面目標(biāo)的IMM算法跟蹤精度更高,且所提算法的誤差超調(diào)量更小,收斂更快。此外,所提面目標(biāo)IMM算法的跟蹤精度也要高于面目標(biāo)IMM-UPF算法。不同于傳統(tǒng)的質(zhì)點(diǎn)目標(biāo)IMM算法,文中將水面目標(biāo)建模為橢圓形面目標(biāo),并利用面目標(biāo)擴(kuò)展測量信息設(shè)計(jì)了模糊化模型概率的IMM-SUPF算法。該算法進(jìn)一步提高了跟蹤系統(tǒng)對水面機(jī)動(dòng)目標(biāo)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(xué)自動(dòng)化學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(61273076)
【分類號】：TN953

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本文編號：2327661