本文選題：車輛跟蹤　切入點(diǎn)：無跡卡爾曼濾波　出處：《安徽理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在經(jīng)濟(jì)高度發(fā)展的今天,汽車作為一種更便捷化的交通工具已經(jīng)逐漸代替原有的電瓶車,成為人們出行的代步工具。但是有一個(gè)嚴(yán)峻的問題擺在面前,那就是交通問題,車輛繁多導(dǎo)致了交通路況的復(fù)雜度升高,并且在這樣復(fù)雜的交通路況中極易發(fā)生交通事故。因此建立起智能交通系統(tǒng)成為當(dāng)今社會(huì)迫在眉睫的事情,而車輛的目標(biāo)跟蹤又是智能交通系統(tǒng)的一個(gè)重要部分,對減少交通阻塞和交通事故的發(fā)生有著重大的意義。本文采用無跡卡爾曼濾波與小波變換結(jié)合的算法來實(shí)現(xiàn)車輛的跟蹤,首先在理論上通過對比了幾種卡爾曼跟蹤算法的優(yōu)缺點(diǎn),選擇了無跡卡爾曼濾波算法作為目標(biāo)跟蹤的算法,并且通過MATLAB進(jìn)行仿真,驗(yàn)證了無跡卡爾曼濾波算法的優(yōu)越性,為了提高車輛跟蹤的精度,在無跡卡爾曼濾波算法的基礎(chǔ)上,加入小波變換的多尺度分析,其原理是:對處理的信號(hào)在尺度N上進(jìn)行多尺度分解,再對分解在不同尺度i上的平滑信號(hào)進(jìn)行無跡卡爾曼濾波處理得到平滑信號(hào)的最優(yōu)估計(jì),然后把得到的平滑信號(hào)的最優(yōu)估計(jì)和細(xì)節(jié)信號(hào)進(jìn)行重構(gòu),得到尺度i上的最優(yōu)估計(jì),接著把不同尺度上的最優(yōu)估計(jì)進(jìn)行融合,從而得到整個(gè)系統(tǒng)的最優(yōu)估計(jì),并且通過MATLAB對其進(jìn)行了仿真。最后對兩種跟蹤算法仿真的結(jié)果進(jìn)行分析,從而驗(yàn)證了無跡卡爾曼濾波與小波變換結(jié)合的算法在車輛跟蹤中的誤差較小,估計(jì)的精度更高。在硬件方面為了適應(yīng)雷達(dá)信號(hào)的高速采集,系統(tǒng)采用FPGA作為硬件控制芯片。整個(gè)系統(tǒng)的硬件設(shè)計(jì)部分包括三個(gè)模塊:數(shù)據(jù)采集模塊,數(shù)據(jù)處理模塊,顯示模塊。每個(gè)模塊的銜接流程為:首先通過激光雷達(dá)獲取車輛的位置和速度信息,然后通過A/D轉(zhuǎn)換電路實(shí)現(xiàn)從模擬信號(hào)到數(shù)字信號(hào)的轉(zhuǎn)換,再將轉(zhuǎn)換后的數(shù)字信號(hào)送入FPGA中進(jìn)行數(shù)據(jù)的處理,最后對處理后的最優(yōu)估計(jì)值進(jìn)行顯示。在系統(tǒng)硬件設(shè)計(jì)的基礎(chǔ)上,使用Verilog語言對系統(tǒng)的每個(gè)硬件接口進(jìn)行編程,同時(shí)對算法也進(jìn)行軟件的編程。最后對搭建的硬件平臺(tái),通過Quartus ii軟件平臺(tái)進(jìn)行下載程序和調(diào)試。
[Abstract]:In today's highly developed economy, cars, as a more convenient means of transportation, have gradually replaced the original electric vehicles and become a means of transportation for people to travel. However, there is a serious problem in front of them, that is, the traffic problem. The complexity of traffic conditions is increased due to the large number of vehicles, and traffic accidents are easy to occur in such complex traffic conditions. Therefore, it is urgent to establish an intelligent transportation system in today's society. Vehicle target tracking is an important part of Intelligent Transportation system, which has great significance to reduce traffic congestion and traffic accidents. In this paper, the unscented Kalman filter and wavelet transform algorithm are used to achieve vehicle tracking. Firstly, by comparing the advantages and disadvantages of several Kalman tracking algorithms in theory, the unscented Kalman filter algorithm is chosen as the target tracking algorithm, and the superiority of the unscented Kalman filter algorithm is verified by MATLAB simulation. In order to improve the accuracy of vehicle tracking, the multi-scale analysis of wavelet transform is added on the basis of unscented Kalman filter algorithm. The principle is that the processed signal is decomposed on scale N. The smooth signal decomposed on different scales I is processed by unscented Kalman filter to obtain the optimal estimation of the smooth signal, and then the optimal estimation of the smooth signal and the detail signal are reconstructed to obtain the optimal estimation on the scale I. Then the optimal estimation on different scales is fused to get the optimal estimation of the whole system, and the simulation is carried out by MATLAB. Finally, the simulation results of the two tracking algorithms are analyzed. It is proved that the algorithm combined with unscented Kalman filter and wavelet transform has less error and higher estimation precision in vehicle tracking. In order to adapt to the high-speed acquisition of radar signal in hardware, The hardware design of the whole system includes three modules: data acquisition module, data processing module, data processing module. Display module. The link flow of each module is as follows: first, the position and speed information of vehicle is obtained by lidar, and then the conversion from analog signal to digital signal is realized by means of A / D conversion circuit. Then the converted digital signal is sent into FPGA for data processing, and the optimal estimated value after processing is displayed. On the basis of the hardware design of the system, every hardware interface of the system is programmed with Verilog language. At the same time, the algorithm is also programmed. Finally, the hardware platform is downloaded and debugged through Quartus II software platform.
【學(xué)位授予單位】：安徽理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN713

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