1.1 RFID Collision Problem and Research Progress of Anti-Collision
5
These problems are the technical keys to be solved urgently, and the researches
about them have attracted widespread attention at home and abroad. This book
proposes to apply the control and coordination mechanisms (such as neural control,
autonomous learning, and self-organization) existing in biological systems to RFID
multi-tag and multi-antenna systems in complex interference environments, and to
build a bionic sensor network with physical anti-collision capabilities. It intends to
solve the technical problems of semi-physical modeling of RFID multi-tag-multiantenna system collision caused by transient electromagnetic wave interference and
the technical problems of RFID multi-tag-multi-antenna sensor network adaptive
and cooperative anti-collision.
The autonomous control of the system mainly depends on the active perception
and information fusion capabilities of the system itself. It is precisely based on this
consideration that in recent years, research on information fusion based on advanced
sensors has attracted widespread attention and achieved many research results.
However, the current research are no longer satisfied with the research on multisensor (or sensor network) capabilities. More attention is paid to the more advanced
autonomous information acquisition and fusion capabilities of the system itself, that
is, the traditional multi-sensor fusion integration research is further extended to the
deeper “Multi-sensory Integration” field.
Various organisms have a wealth of sensory organs, which can sense external
stimuli through various senses such as sight, hearing, smell, taste, touch, etc., in
order to fully obtain the most valuable environmental information, and the brain and
central nervous system Respond effectively (feedback) to the acquired external incentive information. The research of bionic perceptual information fusion theory under
changing environment involves many fields such as artificial intelligence, electronic
information, cognitive psychology, neurophysiology, etc. It is an important frontier research content in the emerging interdisciplinary-bionic science. The research
starts from the basic principles of autonomous perception of organisms, explores the
environmental cognitive process of the system, and simulates the neural activities
and intelligent behaviors under the active perception mechanism of humans or other
organisms. The research focuses on the perception, screening, and understanding of
information in the natural environment. The research improves the system’s understanding of the environment from a one-sided, discrete, and passive perception level
to a global, related, and active perception level. This research explores the establishment of a method system suitable for autonomous perception and control in a
dynamic environment, which is a robust modeling requirement for the system to
achieve adaptive autonomous control [21–24].
In recent years, foreign researchers have carried out research on machine perception behavior from the perspective of bionics. Many scholars pay attention to the
application of artificial neural network methods to the control of robots, and have
designed various artificial neural networks to simulate the thinking activities of
robots, opening up the relevant research fields of robot bionic perception [25–28].
However, the artificial neural network method does not consider the complex thinking
mechanism of the actual biological neural network. Artificial neural networks need
training and memory storage. Robots require a lot of time to learn and train, which
5
These problems are the technical keys to be solved urgently, and the researches
about them have attracted widespread attention at home and abroad. This book
proposes to apply the control and coordination mechanisms (such as neural control,
autonomous learning, and self-organization) existing in biological systems to RFID
multi-tag and multi-antenna systems in complex interference environments, and to
build a bionic sensor network with physical anti-collision capabilities. It intends to
solve the technical problems of semi-physical modeling of RFID multi-tag-multiantenna system collision caused by transient electromagnetic wave interference and
the technical problems of RFID multi-tag-multi-antenna sensor network adaptive
and cooperative anti-collision.
The autonomous control of the system mainly depends on the active perception
and information fusion capabilities of the system itself. It is precisely based on this
consideration that in recent years, research on information fusion based on advanced
sensors has attracted widespread attention and achieved many research results.
However, the current research are no longer satisfied with the research on multisensor (or sensor network) capabilities. More attention is paid to the more advanced
autonomous information acquisition and fusion capabilities of the system itself, that
is, the traditional multi-sensor fusion integration research is further extended to the
deeper “Multi-sensory Integration” field.
Various organisms have a wealth of sensory organs, which can sense external
stimuli through various senses such as sight, hearing, smell, taste, touch, etc., in
order to fully obtain the most valuable environmental information, and the brain and
central nervous system Respond effectively (feedback) to the acquired external incentive information. The research of bionic perceptual information fusion theory under
changing environment involves many fields such as artificial intelligence, electronic
information, cognitive psychology, neurophysiology, etc. It is an important frontier research content in the emerging interdisciplinary-bionic science. The research
starts from the basic principles of autonomous perception of organisms, explores the
environmental cognitive process of the system, and simulates the neural activities
and intelligent behaviors under the active perception mechanism of humans or other
organisms. The research focuses on the perception, screening, and understanding of
information in the natural environment. The research improves the system’s understanding of the environment from a one-sided, discrete, and passive perception level
to a global, related, and active perception level. This research explores the establishment of a method system suitable for autonomous perception and control in a
dynamic environment, which is a robust modeling requirement for the system to
achieve adaptive autonomous control [21–24].
In recent years, foreign researchers have carried out research on machine perception behavior from the perspective of bionics. Many scholars pay attention to the
application of artificial neural network methods to the control of robots, and have
designed various artificial neural networks to simulate the thinking activities of
robots, opening up the relevant research fields of robot bionic perception [25–28].
However, the artificial neural network method does not consider the complex thinking
mechanism of the actual biological neural network. Artificial neural networks need
training and memory storage. Robots require a lot of time to learn and train, which