scholarly journals Noisy Radar Interception Based on Using Detection Theory

2019 ◽  
pp. 323-330
Author(s):  
Mohammad Lohrasbi ◽  
Hojjat Raisee
Keyword(s):  
Detection Theory ◽  
Power Reduction ◽  
Transmitted Wave ◽  
Transmission Power ◽  
Unknown Parameters ◽  
Low Probability

Low Probability of Interception (LPI) radars are difficult to be detected by interception devices and anti-radiation missiles due to their special properties. Using techniques such as transmission power reduction, these radars significantly reduce the probability of interception for intercept receivers. Noisy radar is a type of Low Probability of Interception (LPI) radars that reduces the probability of interception using a noise-like waveform. This paper intends to study this particular type of radar, and intercept it in intercept receivers. Furthermore, using detection theory, and also known or unknown parameters of the noisy radar's transmitted wave, the probability of interception is studied in these radars.

scholarly journals Low-PAPR Waveforms with Shaped Spectrum for Enhanced Low Probability of Intercept Noise Radars

2021 ◽  
Vol 13 (12) ◽  
pp. 2372
Author(s):  
Kubilay Savci ◽  
Gaspare Galati ◽  
Gabriele Pavan
Keyword(s):  
Average Power ◽  
Field Trials ◽  
Power Reduction ◽  
Spectral Kurtosis ◽  
Concise Review ◽  
Noise Radar ◽  
Correlation Processing ◽  
Probing Signal ◽  
Low Probability ◽  
Low Probability Of Intercept

Noise radars employ random waveforms in their transmission as compared to traditional radars. Considered as enhanced Low Probability of Intercept (LPI) radars, they are resilient to interference and jamming and less vulnerable to adversarial exploitation than conventional radars. At its simplest, using a random waveform such as bandpass Gaussian noise as a probing signal provides limited radar performance. After a concise review of a particular noise radar architecture and related correlation processing, this paper justifies the rationale for having synthetic (tailored) noise waveforms and proposes the Combined Spectral Shaping and Peak-to-Average Power Reduction (COSPAR) algorithm, which can be utilized for synthesizing noise-like sequences with a Taylor-shaped spectrum under correlation sidelobe level constraints and assigned Peak-to-Average-Power-Ratio (PAPR). Additionally, the Spectral Kurtosis measure is proposed to evaluate the LPI property of waveforms, and experimental results from field trials are reported.

scholarly journals Power Control in Wireless Sensor Networks with Variable Interference

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Michele Chincoli ◽  
Aly Aamer Syed ◽  
Georgios Exarchakos ◽  
Antonio Liotta
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Control ◽  
Network Performance ◽  
Interference Mitigation ◽  
Power Reduction ◽  
Sensor Nodes ◽  
Spectrum Efficiency ◽  
Wireless Sensor ◽  
Transmission Power

Adaptive transmission power control schemes have been introduced in wireless sensor networks to adjust energy consumption under different network conditions. This is a crucial goal, given the constraints under which sensor communications operate. Power reduction may however have counterproductive effects to network performance. Yet, indiscriminate power boosting may detrimentally affect interference. We are interested in understanding the conditions under which coordinated power reduction may lead to better spectrum efficiency and interference mitigation and, thus, have beneficial effects on network performance. Through simulations, we analyze the performance of sensor nodes in an environment with variable interference. Then we study the relation between transmission power and communication efficiency, particularly in the context of Adaptive and Robust Topology (ART) control, showing how appropriate power reduction can benefit both energy and spectrum efficiency. We also identify critical limitations in ART, discussing the potential of more cooperative power control approaches.

So, Whatever Happened to Signal Detection Theory?

1995 ◽  
Vol 40 (10) ◽  
pp. 972-972
Author(s):  
Jerome R. Busemeyer
Keyword(s):  
Signal Detection ◽  
Signal Detection Theory ◽  
Detection Theory
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