An RF tag communication system model for noise radar

Pan, Qihe; Narayanan, Ram M.

doi:10.1117/12.777394

Cited by 1 publication

(1 citation statement)

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“…The performance of the system is evaluated in term of the symbol error probability, also known as the symbol error ratio (SER) [42]. As stated above, interferences such as clutter reflections and other RF tags' transmitted signals can be either discriminated from the desired signals at the radar decoder or can be analyzed as being equivalent to a worse channel SNR.…”

Section: System Operation and Performance Analysismentioning

confidence: 99%

Delay-Modulated RF Tag System Concept Using Ultrawideband Noise Radar Waveforms

Pan

Narayanan

2011

International Journal of Distributed Sensor Networks

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Radio frequency (RF) tags have been widely used in inventory tracking, environmental monitoring, battlefield situational awareness, and combat identification due to their low cost, small size, and wireless functionality. This paper explores the application of active RF tags in outdoor environments responding to random noise radar interrogations with predetermined messages. A conceptual system design for communication between radar and RF tags using ultrawideband (UWB) noise waveforms is proposed and analyzed theoretically and via simulations. The tag structure comprises a sensing receiver and active receiver/transmitter. The sensing receiver senses the radar header consisting of a prearranged secret realization of the noise waveform. The active receiver/transmitter modulates the RF tag's message onto the radar interrogation signal through weighted tapped delays and reradiates the tag message back to the radar. System performance is evaluated in terms of symbol error probability in an additive white Gaussian noise (AWGN) channel. A technique to combat multipath interference is presented. It is shown that this system is capable of communicating a suite of messages from the tags to the radar.

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Section: System Operation and Performance Analysismentioning

confidence: 99%