【摘要】：針對(duì)戰(zhàn)場(chǎng)目標(biāo)分群中存在的類數(shù)未知和閾值選取欠缺有效方法的問(wèn)題,提出一種基于改進(jìn)空間劃分的目標(biāo)分群算法。首先,通過(guò)敵我及作戰(zhàn)單位屬性劃分,約減分群目標(biāo)數(shù)規(guī)模,降低計(jì)算量;其次,通過(guò)對(duì)空間距離劃分進(jìn)行改進(jìn),能夠動(dòng)態(tài)地優(yōu)選閾值,有效解決類數(shù)未知的分群?jiǎn)栴}。通過(guò)引入劃分獨(dú)立性和逆χ~2分布概率區(qū)間約束,消除計(jì)算冗余并提取出候選閾值,在此基礎(chǔ)上選取最大的候選閾值作為最終分群閾值,可以有效濾除過(guò)程噪聲與觀測(cè)噪聲干擾,提高分群準(zhǔn)確率。仿真結(jié)果表明,該算法對(duì)戰(zhàn)場(chǎng)環(huán)境下的多目標(biāo)編隊(duì)分群具有良好的有效性、穩(wěn)健性和實(shí)時(shí)性。
[Abstract]:In order to solve the problem of unknown class number and lack of effective method of threshold selection in battlefield target clustering, a new algorithm based on improved space partition for target clustering is proposed. First, by dividing the attributes of the enemy and the other and the operational units, the size of the number of targets in the cluster is reduced, and the computation is reduced. Secondly, by improving the space distance division, the threshold can be dynamically selected and the clustering problem with unknown number of classes can be effectively solved. By introducing partition independence and inverse 蠂 ~ 2 distribution probability interval constraints, the computational redundancy is eliminated and candidate thresholds are extracted. On this basis, the largest candidate threshold is selected as the final clustering threshold, and the process noise and observation noise interference can be effectively filtered. Improve the accuracy of clustering. Simulation results show that the algorithm is effective, robust and real-time for multi-target formation clustering in battlefield environment.
【作者單位】： 中國(guó)電子科技集團(tuán)公司第五十四研究所;
【基金】：中國(guó)博士后科學(xué)基金(2015M580217) 河北省博士后科學(xué)基金(B2015005003)資助課題
【分類號(hào)】：TP301.6;E91
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本文編號(hào)：2309165