本文選題：目標(biāo)跟蹤 + 定位��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：隨著網(wǎng)絡(luò)通信技術(shù)的不斷發(fā)展,由于多傳感器觀測(cè)通常會(huì)削減估計(jì)的不確定性,現(xiàn)代防御觀測(cè)系統(tǒng)逐漸向多基地化和網(wǎng)絡(luò)化發(fā)展。在多傳感器觀測(cè)系統(tǒng)中,如何進(jìn)行數(shù)據(jù)融合是一個(gè)十分重要的問(wèn)題,尤其是伴隨著傳感器工藝的不斷提升,越來(lái)越多的系統(tǒng)使用低成本的傳感器進(jìn)行較大規(guī)模的組網(wǎng)觀測(cè),經(jīng)典的方法在沒(méi)有融合中心的情形下具有一定的局限性;此外,在實(shí)際的觀測(cè)過(guò)程中,時(shí)常會(huì)伴隨著檢測(cè)的不確定性,因此導(dǎo)致虛警,這使得經(jīng)典的貝葉斯濾波算法在上述情形中具有局限性;為了解決上述問(wèn)題,本文在這兩方面展開了較為深入的研究。特別的,本文將目標(biāo)跟蹤問(wèn)題歸類為良好檢測(cè)條件下和非良好檢測(cè)條件下的狀態(tài)估計(jì)問(wèn)題。在良好檢測(cè)條件下,本文研究了基于后驗(yàn)概率密度一致性的分布式估計(jì)算法,并分析了協(xié)方差嵌入數(shù)據(jù)融合和基于信息散度的一致性估計(jì)之間的聯(lián)系,在純方位跟蹤情形下的數(shù)值分析證明了該算法的有效性。在非良好檢測(cè)條件下,本文在隨機(jī)有限集框架下研究了多傳感器目標(biāo)跟蹤問(wèn)題,利用序貫似然函數(shù)更新,實(shí)現(xiàn)了多傳感器伯努利粒子濾波,并以方位觀測(cè)為模型,進(jìn)行了理論分析。數(shù)值仿真結(jié)果證明了該算法的有效性。最后,本文對(duì)于基于標(biāo)記隨機(jī)有限集的多目標(biāo)跟蹤算法進(jìn)行了研究。數(shù)值仿真結(jié)果表明該算法能夠同步標(biāo)記以及估計(jì)多個(gè)目標(biāo)的狀態(tài)。
[Abstract]:With the continuous development of network communication technology, because the uncertainty of estimation is usually reduced by multi-sensor observation, the modern defense observation system is gradually becoming multi-static and networked. In the multi-sensor observation system, how to fuse data is a very important problem, especially with the continuous improvement of sensor technology, more and more systems use low-cost sensors to conduct large-scale network observation. The classical method has some limitations in the absence of fusion center. In addition, in the actual observation process, the uncertainty of detection is often accompanied, which leads to false alarm. This makes the classical Bayesian filtering algorithm have limitations in the above cases. In order to solve the above problems, this paper has carried out a more in-depth study in these two aspects. In particular, the target tracking problem is classified as the state estimation problem under the condition of good detection and non-good detection. In this paper, a distributed estimation algorithm based on posteriori probability density consistency is studied under good detection conditions, and the relationship between covariance embedded data fusion and consistency estimation based on information divergence is analyzed. The effectiveness of the algorithm is proved by numerical analysis in the case of azimuth-only tracking. In this paper, the problem of multi-sensor target tracking is studied under the frame of random finite set under the condition of unfavorable detection. The Bernoulli particle filter of multi-sensor is realized by updating the sequential likelihood function, and the azimuth observation model is used as the model. Theoretical analysis is carried out. Numerical simulation results show the effectiveness of the algorithm. Finally, this paper studies the multi-target tracking algorithm based on labeled random finite set. Numerical simulation results show that the algorithm can synchronously mark and estimate the state of multiple targets.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN713;TP212

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本文編號(hào)：1949079