EstimationwithApplicationstoTrackingandNavigation-chapter12补充资源-CSDN文库

5星 · 超过95%的资源需积分: 9 105 浏览量 2012-03-17 10:52:51 上传评论收藏 2.69MB PDF 举报

资源详情

资源评论

资源推荐

Chapter 12

INTRODUCTION TO NAVIGATION

APPLICATIONS

12.1 INTRODUCTION

12.1.1 Navigation Applications - Outline

Navigation and target tracking are closely related.

0 Target tracking

is the estimation of the state of a moving object based

on remote observations of the object.

Navigation

is tracking of the “own-ship” - the platform on which the

sensor(s) is (are) located. It answers the question “Where am I?” by estimat-

ing one’s own state (position, velocity, attitude, and possibly acceleration).

Another problem, which incorporates both of the above, is

Guidance,

which is concerned with questions like “Which way leads to

the destination?” It dete~ines an appropriate sequence of actions to reach

a destination. Guidance is a con& problem based on the information

obtained from, typically, a navigation system and, when the destination

(target) is a moving object, a system that track this object.

The primary considerations for the development, choice, and design of a

navigation device include cost, high accuracy, self-containment, security, world-

wide capability, and high availability, among others. Certain navigation systems,

such as inertial systems, are capable of being a stand-alone means for navigation

mainly because they are self-contained. Other navigation devices can be used

only as navigation aids to improve the navigation accuracy.

Estimation and filtering techniques, especially Kalman filtering, have found

extensive application in navigation systems because they can significantly im-

prove their accuracy.

This chapter presents an introduction to the application of estimation and

filtering techniques in the Global Positioning System (GPS), inertial navigation

systems, and their integration.

491

494

12 INTRODUCTION TO NAVIGATION APPLICATIONS

The complementary filtering satisfies an additional constraint important for

many applications: The signal is not distorted. Nevertheless, in the case where

the signal s(t) can be properly modeled as a random process with known spectral

characteristics, complementary filtering is a conservative philosophy. As a re-

sult, compared with Wiener filtering, its performance will suffer to some extent.

However, if the signal-to-noise ratio in the measurements is high (say, 20 dB),

the performance improvement of the Wiener filtering over the complementary

filtering is insignificant.

Navigation signals can rarely be well-modeled as random processes, not

to mention that their spectral characteristics, especially the signal-noise cross-

power spectra, would not be known in practice. In addition, these signals

usually have a much greater power than the noise and thus the performance

degradation due to the conservativeness of the complementary filtering is often

quite marginal.

Complementary filters are often used in two levels in navigation: (1) Within

an inertial navigation system, the filter is used to compensate the measurement

errors of the INS system; and (2) for the integration of the inertial system and

other navigation systems, the filter is used to estimate the estimation errors of

the primary navigation system.

Example

The heading $J of a vehicle is to be estimated based on measurements of a

fluxgate compass and a directional (rate) gyro, respectively, given in Laplace

domain by

+c (4

= $44 + TMS>

(12.2.2-3)

R,(s)

= R(s) + R,(s)

(12.2.2-4)

where

R(s) =

s+(s) is the heading change rate, and gn and

are compass and

gyro measurement noise, respectively. It is known that the compass provides

reliable information at low frequencies only (i.e., the spectrum of $J~ is mostly

in the high-frequency region), while in the same low-frequency band the gyro

exhibits a bias and drift phenomenon and is thus useful at higher frequencies

(i.e., the spectrum of

is mostly in the low-frequency region).

This is a typical example where complementary filtering is useful. Note first

that (12.2.2-3)-(12.2.2-4) can be converted into the standard form of (12.2.1-1)-

(12.2.1-2) by setting 21(s) = Q+(s) and 22(s) =

R,(s)/s.

It then follows from

(12.2.2-1) that the estimate of $J provided by a cbmplemc

form

G(s) = $J(s) + $hl(s)H(s) + (Rn(s)/s)[l - H(s)

Thus the estimation error is

4(s) = lM4H(4 + (Rn(4l4P - H(s)1

ntary filter has the

(12.2.2-5)

(12.2.2-6)

12.3 INERTIAL NAVIGATION SYSTEMS

495

Since

and $, are significant in the low- and high-frequency regions, respec-

- tively, H(s) should be low pass to attenuate $J, while letting

pass.

The simplest such filter is H(s) = A. The gain

is a design parameter

to meet a target break frequency dictated by the frequency characteristics of the

compass and gyro. Kalman filtering techniques can be used to determine the

optimal

value because a complementary filter can be treated in principle as a

simple Kalman filter.

In practice, this simple complementary filter structure is modified to meet

additional constraints, such as to reject the steady-state bias of the rate gyro.

12.3 INERTIAL NAVIGATION SYSTEMS

An inertial navigation system (INS) is a dead-reckoning system. It

provides a self-contained, nonjammable, and nonradiating means for navigation.

It can be used as a stand-alone system or can be combined with some other

navigation systems.

An INS has three basic functions: sensing, computing, and outputting. The

sensors are accelerometers and gyroscopes (gyros, for short), which mea-

sure linear acceleration and rotational rate (angular velocity) of the vehicle, re-

spectively. Estimation and filtering techniques are used in the computation of the

navigation solutions based on the measurements from these sensors. In order to

provide three-dimensional navigation for a vehicle with six degrees of freedom,

three gyros are positioned in such a way to define a three-dimensional coordi-

nate system and a triad of accelerometers are mounted on the three orthogonal

axes.

The principle of navigation by an INS system is simple. With a precise

knowledge of the initial position and attitude of the vehicle, at any time, single-

and double-integration of the linear accelerations give the velocity and position

of the vehicle, respectively, and the attitude is obtained by integrating rotational

rates from the gyro measurements. However, the presence of random errors

as well as unknown constant (or nearly comstant) errors (e.g., drift rates and

biases) makes the navigation problem quite complex.

Inertial navigation systems are either gimbaled or strapdown. In a gim-

baled system, also known as a

platform system,

a stabilized physical plat-

form is maintained through a servo system on which the accelerometers and

gyros are mounted and thus isolated from the rotations of the vehicle. This not

only greatly reduces the requirements on the inertial sensors but also greatly

simplifies the navigation computation since the attitude of the platform with

respect to a navigation reference frame is preserved. However, the need to have

a stabilized platform makes a gimbaled system more complicated, costly, and

less reliable than a strapdown system.

In a strapdown system, there is no stabilized platform - the gyros and

accelerometers are fixed (strapped down) to the vehicle body directly. The re-

剩余44页未读，继续阅读

少卓

2013-03-24

这一张恰好是讲GPS信号滤波处理的，车载导航正需要

评论收藏

内容反馈

zhiweihappy

粉丝: 0
资源: 11

Estimation with Applications to Tracking and Navigation-chapter1...

评论3

最新资源

Estimation with Applications to Tracking and Navigation-chapter1...

评论3

Estimation with Applications To Tracking and Navigation

Estimation With Applications to Tracking and Navigation

Estimation with Applications to Tracking and Navigation的第十二章

Estimation with Applications to Tracking and Navigation源码

Estimation with Applications to Tracking and Navigation (PDF) 第一部分（共2部）

Estimation with Applications to Tracking and Navigation (PDF) 第二部分（共2部）

Estimation with Applications to Tracking Navigation part2

Estimation with Applications to Tracking and Nav.pdf

Estimation with Applications to Tracking Navigation part1

DynaEst.rar_Bar Shalom-Campo_Bar-Shalom_DynEst_bar shalom_shalom

Error Analysis and Estimation for the FVM with Applications to Fluid Flows

FBMC-channel-estimation-based-on-SVR-master.zip_FBMC_SVR_channel

论文《Accurate scale estimation for robust visual tracking》代码

FACE-DETECTION-AND-TRACKING-USING-A-BOOSTED-ADAPT_faces

State Estimation for Robotics A Matrix-Lie-Group Approach, 2015

Hybrid grid multiple-model estimation with application to maneuvering target tracking

python大作业 含爬虫、数据可视化、地图、报告、及源码（整和为一个文件）（2014-2020全国各地区原油加工量）.rar

仿真电路以及操作方法

【纯干货啊】华为IPD流程管理(完整版).pptx

可编程语言标准IEC61131-3中文版.pdf

OFDM完整仿真过程与教程.zip

信号与系统——保研复习资料.pdf

Landsat_WRS2.zip

最全的Visio形状/图形库

AxureRP9项目原型50套、案例20个、元件库1套.zip

北理工+成电+东南——通信/信号保研面试真题.pdf

最新资源

python大作业含爬虫、数据可视化、地图、报告、及源码（整和为一个文件）（2014-2020全国各地区原油加工量）.rar