£128.26

Springer Non-Cooperative Target Tracking, Fusion and Control

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Description

This technical book from Springer provides a concise and comprehensive overview of non-cooperative target tracking, fusion, and control. Designed for professionals and researchers in the fields of AI and machine learning, the text focuses on practical algorithms rather than abstract theories. It is specifically tailored to address the challenges of tracking non-cooperative targets, including both air and space-borne objects. The work explores several advanced mathematical and computational techniques. Readers will find detailed information on Gaussian mixture cardinalized probability hypothesis density (CPHD) filters, optimization on manifold, the construction of filter banks and tight frames, and structured sparse representation. By providing a variety of illustrative and computational examples, this book serves as a functional guide for implementing complex tracking and fusion algorithms in real-world scenarios.

Key Features

Focuses on practical algorithms for non-cooperative targets instead of purely theoretical frameworks.

Covers advanced technical methods including Gaussian mixture cardinalized probability hypothesis density (CPHD) filters.

Explores complex mathematical concepts such as optimization on manifold and structured sparse representation.

Includes specific applications for tracking air and space-borne targets.

Provides various illustrative and computational examples to aid understanding and application.

Covers filter bank construction and tight frames for advanced data science and information fusion tasks.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
10 January 2019
Listed Since
09 January 2019

Barcode

No barcode data available

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