Free shipping on orders over $99
Kalman Filtering

Kalman Filtering

With Real-Time Applications

by Charles K. Chui and Guanrong Chen
Hardback
Publication Date: 09/02/2017

Share This Book:

  $136.84
or 4 easy payments of $34.21 with
afterpay
This item qualifies your order for FREE DELIVERY
Preliminaries.- Kalman Filter: An Elementary Approach.- Orthogonal Projection and Kalman Filter.- Correlated System and Measurement Noise Processes.- Colored Noise.- Limiting Kalman Filter.- Sequential and Square-Root Algorithms.- Extended Kalman Filter and System Identification.- Decoupling of Filtering Equations.- Kalman Filtering for Interval Systems.- Wavelet Kalman Filtering.- Distributed Estimation on Sensor Networks.- Notes.- Answers and Hints to Exercises.
ISBN:
9783319476100
9783319476100
Category:
Program concepts / learning to program
Format:
Hardback
Publication Date:
09-02-2017
Language:
English
Publisher:
Springer
Country of origin:
United States
Edition:
5th Edition
Dimensions (mm):
235x155mm
Weight:
5.27kg

This title is in stock with our Australian supplier and should arrive at our Sydney warehouse within 2 - 3 weeks of you placing an order.

Once received into our warehouse we will despatch it to you with a Shipping Notification which includes online tracking.

Please check the estimated delivery times below for your region, for after your order is despatched from our warehouse:

ACT Metro  2 working days

NSW Metro  2 working days

NSW Rural  2 - 3 working days

NSW Remote  2 - 5 working days

NT Metro  3 - 6 working days

NT Remote  4 - 10 working days

QLD Metro  2 - 4 working days

QLD Rural  2 - 5 working days

QLD Remote  2 - 7 working days

SA Metro  2 - 5 working days

SA Rural  3 - 6 working days

SA Remote  3 - 7 working days

TAS Metro  3 - 6 working days

TAS Rural  3 - 6 working days

VIC Metro  2 - 3 working days

VIC Rural  2 - 4 working days

VIC Remote  2 - 5 working days

WA Metro  3 - 6 working days

WA Rural  4 - 8 working days

WA Remote  4 - 12 working days

Reviews

Be the first to review Kalman Filtering.