本文選題：概率假設密度濾波　切入點：隨機有限集　出處：《光電工程》2016年12期 　論文類型：期刊論文

【摘要】：針對帶標簽的高斯混合概率假設密度濾波算法無法獲取衍生目標的問題,提出一種可以提取衍生目標的帶標簽GM-PHD算法。首先,通過為高斯項加注標簽的方式區(qū)別不同的目標,以辨別單個目標及其航跡。其次,在濾波過程中,對每一時刻得到的狀態(tài)估計值與已形成的航跡標簽進行匹配關聯(lián),實現(xiàn)航跡維持。最后,通過設置衍生閾值來判斷狀態(tài)估計中是否存在衍生目標以及可能產生的目標個數,為新生目標高斯項和可能的衍生目標高斯項重新分配標簽,并創(chuàng)建新的航跡。仿真實驗結果表明,與傳統(tǒng)的帶標簽GM-PHD算法相比,在衍生目標存在的情況下,改進算法具有更好的跟蹤性能。
[Abstract]:In order to solve the problem of Gao Si's mixed probability assumption that the density filter algorithm can not obtain derived targets, a tagged GM-PHD algorithm is proposed to extract derived targets. Firstly, different targets are distinguished by tagging Gao Si items. In order to distinguish a single target and its track. Secondly, in the process of filtering, the state estimation obtained at each time is matched with the track label that has been formed to achieve track maintenance. The derivative threshold is set to determine whether there are derived targets and the number of possible targets in the state estimation, and to reassign labels to Gao Si item of newborn target and Gao Si term of possible derivative target. The simulation results show that compared with the traditional tagged GM-PHD algorithm, the improved algorithm has better tracking performance than the traditional tagged GM-PHD algorithm.
【作者單位】： 西安工程大學計算機科學學院;德克薩斯州大學奧斯汀分校人類生態(tài)系;
【基金】：國家自然科學基金項目(61201118) 陜西省自然科學基礎研究計劃項目(2016JM6030) 西安工程大學研究生創(chuàng)新基金項目(CX201631);西安工程大學學科建設項目
【分類號】：TN713
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本文編號：1603258