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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd"> <html> <head> <title>page_95</title> <link rel="stylesheet" href="reset.css" type="text/css" media="all"> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /> </head> <body> <table summary="top nav" border="0" width="100%"> <tr> <td align="left" width="30%" style="background: #EEF3E2"><a style="color: blue; font-size: 120%; font-weight: bold; text-decoration: none; font-family: verdana;" href="page_93.html">< previous page</a></td> <td id="ebook_previous" align="center" width="40%" style="background: #EEF3E2"><strong style="color: #2F4F4F; font-size: 120%;">page_95</strong></td> <td align="right" width="30%" style="background: #EEF3E2"><a style="color: blue; font-size: 120%; font-weight: bold; text-decoration: none; font-family: verdana;" href="page_96.html">next page ></a></td> </tr> <tr> <td id="ebook_page" align="left" colspan="3" style="background: #ffffff; padding: 20px;"> <table border="0" width="100%" cellpadding="0"><tr><td align="center"> <table border="0" cellpadding="2" cellspacing="0" width="100%"><tr><td align="left"></td> <td align="right"></td> </tr></table></td></tr><tr><td align="left"><p></p><table border="0" cellspacing="0" cellpadding="0" width="100%"><tr><td align="right"><font face="Times New Roman, Times, Serif" size="2" color="#FF0000">Page 95</font></td></tr></table><table border="0" cellspacing="0" cellpadding="0"><tr><td rowspan="5"></td> <td colspan="3" height="48"></td> <td rowspan="5"></td></tr><tr><td colspan="3"></td></tr><tr><td></td> <td><font face="Times New Roman, Times, Serif" size="4">Chapter 4<br />Discrete Linear and Nonlinear Kalman Filtering Techniques</font></td><td></td></tr><tr><td colspan="3"></td></tr><tr><td colspan="3" height="1"></td></tr></table><table border="0" cellspacing="0" cellpadding="0"><tr><td rowspan="5"></td> <td colspan="3" height="12"></td> <td rowspan="5"></td></tr><tr><td colspan="3"></td></tr><tr><td></td> <td><font face="Times New Roman, Times, Serif" size="3">Section 3.3.5 has already introduced the concept of a state estimator. Given that it is possible to estimate the state of a system, it is natural to ask if there is an optimal means of doing so. This was the topic addressed in 1960 by Kalman [79, 80]. The Kalman filter is the main tool used in this book to integrate data from multiple sensors in order to provide an accurate estimate of the vehicle state. Since nearly all Kalman filter implementations are typically carried out on digital computers (with discrete measurements), this presentation focuses on the discrete Kalman filter.</font></td><td></td></tr><tr><td colspan="3"></td></tr><tr><td colspan="3" height="1"></td></tr></table><table border="0" cellspacing="0" cellpadding="0"><tr><td rowspan="5"></td> <td colspan="3" height="12"></td> <td rowspan="5"></td></tr><tr><td colspan="3"></td></tr><tr><td></td> <td><font face="Times New Roman, Times, Serif" size="3">In Sec. 4.1 the Kalman filter is derived in the context of least-squares estimation to provide an intuitive understanding of the Kalman filter objectives and philosophy. In Sec. 4.2 four alternative implementations of the Kalman filter are derived and presented. In Sec. 4.3 the benefits and the results of rigorous stochastic process analysis of the Kalman filter are discussed. In Sec. 4.4 methods for analyzing filter performance during the design stage are discussed. In Sec. 4.5 various issues related to Kalman filter implementation are discussed. Finally, in Secs. 4.6 and 4.7 numeric issues and suboptimal filtering, respectively, are described. Of particular interest is Sec. 4.7.5, in which extensions of the Kalman filter algorithm applicable to nonlinear systems are presented.</font></td><td></td></tr><tr><td colspan="3"></td></tr><tr><td colspan="3" height="1"></td></tr></table><table border="0" cellspacing="0" cellpadding="0"><tr><td rowspan="5"></td> <td colspan="3" height="12"></td> <td rowspan="5"></td></tr><tr><td colspan="3"></td></tr><tr><td></td> <td><font face="Times New Roman, Times, Serif" size="3">Additional Kalman filter references include Refs. 23, 53, 58, 74, 100, and 109. In the majority of these references the Kalman filter and its properties are approached and derived within the context of stochastic processes. Although this approach is summarized in Sec. 4.3, the required background for a thorough presentation is beyond the scope of this text. The least-squares approach taken in the following sections presents the Kalman filter in an understandable fashion with a minimum of background in stochastic processes. After gaining an understanding of the objectives, application, and basic theory of the Kalman filter in this chapter, the reader is encouraged to consult the above references</font><font face="Times New Roman, Times, Serif" size="3" color="#FFFF00"></font></td><td></td></tr><tr><td colspan="3"></td></tr><tr><td colspan="3" height="1"></td></tr></table></td></tr></table><p><font size="0"></font></p>聽 </td> </tr> <tr> <td align="left" width="30%" style="background: #EEF3E2"><a style="color: blue; font-size: 120%; font-weight: bold; text-decoration: none; font-family: verdana;" href="page_93.html">< previous page</a></td> <td id="ebook_next" align="center" width="40%" style="background: #EEF3E2"><strong style="color: #2F4F4F; font-size: 120%;">page_95</strong></td> <td align="right" width="30%" style="background: #EEF3E2"><a style="color: blue; font-size: 120%; font-weight: bold; text-decoration: none; font-family: verdana;" href="page_96.html">next page ></a></td> </tr> </table> </body> </html>⌨️ 快捷键说明
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