本文選題：多目標(biāo)跟蹤　切入點(diǎn)：能量控制　出處：《南京航空航天大學(xué)》2014年碩士論文

【摘要】：目標(biāo)跟蹤技術(shù)是當(dāng)今雷達(dá)數(shù)據(jù)處理的研究熱點(diǎn)之一。目前常用的跟蹤濾波器是卡爾曼濾波器。本文圍繞雷達(dá)跟蹤時(shí)的能量控制方法展開(kāi)研究,主要工作是在研究幾種濾波算法的基礎(chǔ)上用更優(yōu)化的求積分卡爾曼濾波和容積卡爾曼濾波,結(jié)合交互式多模型,設(shè)計(jì)出自適應(yīng)目標(biāo)跟蹤算法,控制雷達(dá)輻射能量。即在不改變雷達(dá)跟蹤精度的前提下,增大采樣間隔,從而降低雷達(dá)輻射能量,有利于雷達(dá)跟蹤更多的目標(biāo),進(jìn)一步提高雷達(dá)的工作效率。本文主要研究?jī)?nèi)容如下:1、分析了目標(biāo)跟蹤技術(shù)的發(fā)展,以及在目標(biāo)跟蹤過(guò)程中自適應(yīng)采樣時(shí)能量控制的研究意義。分析了非線性濾波跟蹤算法的研究現(xiàn)狀和自適應(yīng)目標(biāo)跟蹤時(shí)的能量控制的研究現(xiàn)狀。2、闡述了目標(biāo)跟蹤的基礎(chǔ)理論,包括常見(jiàn)的目標(biāo)運(yùn)動(dòng)模型,重點(diǎn)研究了幾種濾波算法,仿真比較了幾種濾波算法的性能,同時(shí)分析了交互式多模型算法、灰色關(guān)聯(lián)度理論、粒子群優(yōu)化算法等,為后續(xù)章節(jié)的研究奠定了基礎(chǔ)。3、給出了一種交互式求積分卡爾曼濾波的自適應(yīng)采樣間隔目標(biāo)跟蹤算法。首先介紹了目標(biāo)協(xié)方差矩陣估計(jì),然后構(gòu)造協(xié)方差控制資源管理模型,再基于灰色關(guān)聯(lián)度理論和粒子群優(yōu)化算法,結(jié)合求積分卡爾曼濾波算法,設(shè)計(jì)了交互式求積分卡爾曼濾波的自適應(yīng)采樣間隔目標(biāo)跟蹤算法,并與基于協(xié)方差控制的兩類資源管理算法進(jìn)行了性能比較,仿真結(jié)果表明,該方法能夠在保證跟蹤精度的情況下,增大采樣間隔,可以節(jié)省更多的雷達(dá)時(shí)間資源。4、在交互式求積分卡爾曼濾波自適應(yīng)采樣間隔目標(biāo)跟蹤算法的基礎(chǔ)上,設(shè)計(jì)了交互式容積卡爾曼濾波的自適應(yīng)采樣間隔目標(biāo)跟蹤算法。首先分析了容積卡爾曼濾波算法,然后在三階球面-徑向準(zhǔn)則的基礎(chǔ)上,實(shí)現(xiàn)高階容積卡爾曼濾波算法,最后設(shè)計(jì)出基于交互式容積卡爾曼濾波的自適應(yīng)采樣間隔算法,與交互式求積分卡爾曼濾波算法進(jìn)行了性能比較,仿真結(jié)果表明,該方法較之前的濾波算法,能夠進(jìn)一步優(yōu)化采樣間隔,減少雷達(dá)照射次數(shù)。
[Abstract]:Target tracking technology is one of the hotspots in radar data processing.At present, the commonly used tracking filter is Kalman filter.An adaptive target tracking algorithm is designed to control radar radiation energy.That is, without changing the tracking accuracy of radar, the sampling interval is increased, thus reducing the radar radiation energy, which is conducive to radar tracking more targets, and further improve the working efficiency of radar.The main contents of this paper are as follows: 1. The development of target tracking technology and the significance of energy control in adaptive sampling are analyzed.The research status of nonlinear filter tracking algorithm and energy control in adaptive target tracking are analyzed. The basic theory of target tracking is expounded, including the common target motion model, and several filtering algorithms are emphatically studied.The performance of several filtering algorithms is compared, and the interactive multi-model algorithm, grey correlation degree theory, particle swarm optimization algorithm and so on are analyzed.It lays a foundation for the following chapters. 3. An adaptive sampling interval target tracking algorithm based on interactive integral Kalman filter is proposed.Firstly, the objective covariance matrix estimation is introduced, then the covariance control resource management model is constructed, and then based on the grey correlation degree theory and particle swarm optimization algorithm, the integral Kalman filtering algorithm is combined.An adaptive sampling interval target tracking algorithm based on interactive integral Kalman filter is designed and compared with two kinds of resource management algorithms based on covariance control. The simulation results show that,This method can increase the sampling interval and save more radar time resource. It is based on the interactive integrated Kalman filter adaptive sampling interval target tracking algorithm.An adaptive sampling interval target tracking algorithm based on interactive volumetric Kalman filter is designed.Firstly, the volumetric Kalman filter algorithm is analyzed, then the high-order volumetric Kalman filter algorithm is realized on the basis of the third-order spherical and radial criteria. Finally, an adaptive sampling interval algorithm based on interactive volumetric Kalman filter is designed.Compared with the interactive integral Kalman filtering algorithm, the simulation results show that the proposed method can further optimize the sampling interval and reduce the radar irradiating times.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN953

【參考文獻(xiàn)】

相關(guān)博士學(xué)位論文 前1條

1 高芳;智能粒子群優(yōu)化算法研究[D];哈爾濱工業(yè)大學(xué);2008年

，

本文編號(hào)：1715419