本文關(guān)鍵詞：�；脚_純方位主動無源跟蹤技術(shù)研究　出處：《浙江大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： �；脚_ 無源觀測 CKF CRTSS 最優(yōu)機(jī)動

【摘要】：在科技高速發(fā)展的今天,信息戰(zhàn)、電子戰(zhàn)被越來越多地應(yīng)用于各種戰(zhàn)爭系統(tǒng)及國防系統(tǒng),發(fā)揮著日益重要的作用。無源定位跟蹤技術(shù)作為精準(zhǔn)定位技術(shù)的佼佼者,具有探測距離遠(yuǎn)、隱蔽性強(qiáng)等技術(shù)優(yōu)勢,因此被廣泛地應(yīng)用于軍事領(lǐng)域并得到了國內(nèi)外眾多學(xué)者的研究。本文基于�；脚_無源觀測系統(tǒng)展開了對純方位主動無源定位跟蹤技術(shù)的研究,結(jié)合實際應(yīng)用場景提出若干新問題,并給出相應(yīng)的解決方案。�；脚_無源觀測系統(tǒng)是一個典型的非線性系統(tǒng)。在實際應(yīng)用中,噪聲、初始誤差等原因一定程度上影響了無源觀測系統(tǒng)的估計精度。針對此問題,本文提出加入控制輸入的容積卡爾曼平滑算法(CRTS-U),該算法將經(jīng)典容積卡爾曼濾波(Cubature Kalman Filter, CKF)和Racuch-Tung-Striebel Smoother (RTSS)平滑算法結(jié)合起來,進(jìn)一步提高了系統(tǒng)的估計精度。另外,該算法將系統(tǒng)控制輸入作為待設(shè)計量引入系統(tǒng)方程并參與迭代,使同時求解系統(tǒng)的最優(yōu)狀態(tài)估計和�；脚_的最優(yōu)機(jī)動策略成為可能,仿真結(jié)果顯示了CRTS-U算法的可行性及有效性。研究發(fā)現(xiàn),系統(tǒng)的控制輸入不僅影響系統(tǒng)的可觀測性,而且會影響系統(tǒng)的估計性能,因此選擇恰當(dāng)?shù)目刂戚斎胧欠浅Ｖ匾�。常用的最�?yōu)機(jī)動判別指標(biāo)都僅能從單一維度評估觀測機(jī)機(jī)動的好壞,本文考慮到�；脚_機(jī)動時的燃料消耗,將燃耗控制指標(biāo)與普通判別指標(biāo)相結(jié)合,構(gòu)建了最優(yōu)機(jī)動復(fù)合判別指標(biāo),使我們能夠多維度地評估觀測機(jī)機(jī)動的優(yōu)劣。隨后,基于CRTS-U算法及最優(yōu)機(jī)動判別復(fù)合判別指標(biāo),本文提出了最優(yōu)控制求解算法和最優(yōu)估計求解算法,同時解決了燃耗約束下系統(tǒng)的最優(yōu)狀態(tài)估計問題和�；脚_最優(yōu)機(jī)動策略設(shè)計問題。仿真結(jié)果顯示了最優(yōu)控制求解算法的可行性及有效性。
[Abstract]:With the rapid development of science and technology, information warfare and electronic warfare are more and more used in various war systems and national defense systems. Passive positioning and tracking technology, as the best in precision positioning technology, has the advantages of long detection distance and strong concealment. Therefore, it has been widely used in military field and has been studied by many scholars at home and abroad. In this paper, the research of azimuth-only active passive location and tracking technology is carried out based on the passive observation system of sea-based platform. Based on the practical application, some new problems are proposed, and the corresponding solutions are given. The passive observation system of the sea-based platform is a typical nonlinear system. In practical application, noise is used. The initial error and other factors affect the estimation accuracy of passive observation system to some extent. In order to solve this problem, a volume Kalman smoothing algorithm (CRTS-U) with control input is proposed in this paper. The algorithm uses classical volume Kalman filter cuboid Kalman Filter. CKF) and Racuch-Tung-Striebel Smoother / RTSS smoothing algorithm are combined to further improve the estimation accuracy of the system. The algorithm introduces the system control input into the system equation and participates in the iteration, which makes it possible to simultaneously solve the optimal state estimation of the system and the optimal maneuvering strategy of the sea-based platform. Simulation results show the feasibility and effectiveness of the CRTS-U algorithm. It is found that the control input of the system not only affects the observability of the system, but also affects the estimation performance of the system. Therefore, it is very important to select the appropriate control input. The commonly used optimal maneuvering discriminant indexes can only evaluate the maneuverability of the observing machine from a single dimension. This paper takes into account the fuel consumption of the sea-based platform maneuvering. By combining the burnup control index with the ordinary discriminant index, the optimal maneuvering composite discriminant index is constructed, which enables us to evaluate the maneuverability of the observing machine in many dimensions. Based on the CRTS-U algorithm and the optimal maneuvering discriminant compound discriminant index, the optimal control algorithm and the optimal estimation algorithm are proposed in this paper. At the same time, the optimal state estimation problem and the optimal maneuvering strategy design problem of the sea-based platform are solved. The simulation results show the feasibility and effectiveness of the optimal control algorithm.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：E11;TP13

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本文編號：1430617