本文關(guān)鍵詞：群目標(biāo)跟蹤自適應(yīng)IMM算法　出處：《哈爾濱工業(yè)大學(xué)學(xué)報(bào)》2016年10期 　論文類型：期刊論文

  更多相關(guān)文章： 群目標(biāo) 模型轉(zhuǎn)換概率 強(qiáng)跟蹤 質(zhì)心狀態(tài) 擴(kuò)展?fàn)顟B(tài)

【摘要】：為提高對(duì)機(jī)動(dòng)群目標(biāo)在高量測誤差下的跟蹤性能,提出了一種自適應(yīng)IMM群目標(biāo)跟蹤算法.首先,在群質(zhì)心狀態(tài)估計(jì)中,引入帶有多重次優(yōu)漸消因子的強(qiáng)跟蹤濾波算法,提高機(jī)動(dòng)階段時(shí)對(duì)群質(zhì)心狀態(tài)估計(jì)的精度.其次,在擴(kuò)展?fàn)顟B(tài)估計(jì)中,考慮量測精度對(duì)于擴(kuò)展?fàn)顟B(tài)的影響,將量測誤差和擴(kuò)展?fàn)顟B(tài)同時(shí)納入到量測似然函數(shù)的構(gòu)建中,應(yīng)用新息計(jì)算和漸消記憶迭代過程自適應(yīng)更新量測誤差協(xié)方差矩陣.最后,通過quasi-Bayesian方法自適應(yīng)更新模型轉(zhuǎn)換概率,利用量測數(shù)據(jù)修正模型轉(zhuǎn)換概率,抑制非匹配模型作用,放大匹配模型作用,實(shí)時(shí)匹配跟蹤模型與目標(biāo)運(yùn)動(dòng)狀態(tài).仿真實(shí)驗(yàn)結(jié)果表明,該方法有效提高了對(duì)群質(zhì)心狀態(tài)和擴(kuò)展?fàn)顟B(tài)的估計(jì)精度.
[Abstract]:In order to improve the tracking performance of maneuvering group targets with high measurement error, an adaptive IMM group target tracking algorithm is proposed. A strong tracking filtering algorithm with multiple suboptimal fading factors is introduced to improve the accuracy of mass center state estimation in maneuvering phase. Secondly, the effect of measurement precision on extended state is considered in extended state estimation. The measurement error and the extended state are incorporated into the construction of the measurement likelihood function at the same time, and the measurement error covariance matrix is updated adaptively by using the innovation calculation and the iterative process of fading memory. Finally. The model transformation probability is updated adaptively by quasi-Bayesian method, and the model transformation probability is corrected by measuring data to suppress the effect of non-matching model and amplify the function of matching model. The simulation results show that the proposed method can effectively improve the estimation accuracy of group centroid state and extended state.
【作者單位】： 空軍工程大學(xué)防空反導(dǎo)學(xué)院;
【基金】：國家自然科學(xué)基金(61501495)
【分類號(hào)】：TN713
【正文快照】： 群目標(biāo)跟蹤場景,如機(jī)群編隊(duì)和地面移動(dòng)車隊(duì),一系列擁有類似運(yùn)動(dòng)方式的空間臨近目標(biāo)組成跟蹤對(duì)象.在群目標(biāo)跟蹤問題中,群目標(biāo)的量測數(shù)目是時(shí)變的,傳統(tǒng)多目標(biāo)跟蹤算法難以實(shí)現(xiàn)對(duì)群目標(biāo)的穩(wěn)定跟蹤[1].文獻(xiàn)[2]按照群內(nèi)目標(biāo)數(shù)目將群目標(biāo)跟蹤分成兩類:大群目標(biāo)跟蹤和小群目標(biāo)跟蹤.其

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1 戴曉強(qiáng);劉維亭;朱志宇;;基于模糊自適應(yīng)IMM算法的機(jī)動(dòng)目標(biāo)跟蹤方法[J];船舶工程;2007年03期

2 王志敏,肖衛(wèi)初;機(jī)動(dòng)目標(biāo)跟蹤中IMM算法的性能分析[J];湖南城市學(xué)院學(xué)報(bào);2003年06期

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本文編號(hào)：1405502