Ambiguity function analysis of adaptive colored-noise radar waveforms

2006 ◽  
Author(s):  
M. L. Picciolo ◽  
J. S. Goldstein ◽  
T. F. Settle ◽  
M. A. Tinston ◽  
G. N. Schoenig
Keyword(s):  
Colored Noise ◽  
Function Analysis ◽  
Ambiguity Function ◽  
Noise Radar ◽  
Radar Waveforms

Ambiguity Function Analysis of Adaptive Colored-Noise Radar Waveforms

2013 ◽  
pp. 1162-1170
Author(s):  
Picciolo ◽  
Schoenig ◽  
Goldstein ◽  
Settle ◽  
Tinston
Keyword(s):  
Colored Noise ◽  
Function Analysis ◽  
Ambiguity Function ◽  
Noise Radar ◽  
Radar Waveforms

scholarly journals On the Analysis of PM/FM Noise Radar Waveforms Considering Modulating Signals with Varied Stochastic Properties

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1727
Author(s):  
Leandro Pralon ◽  
Gabriel Beltrao ◽  
Alisson Barreto ◽  
Bruno Cosenza
Keyword(s):  
Random Phase ◽  
Research Area ◽  
General Term ◽  
Radar Systems ◽  
Noise Radar ◽  
Random Samples ◽  
Spectrum Density ◽  
Important Research Area ◽  
Radar Waveforms ◽  
Stochastic Properties

Noise Radar technology is the general term used to describe radar systems that employ realizations of a given stochastic process as transmit waveforms. Originally, carriers modulated in amplitude by a Gaussian random signal, derived from a hardware noise source, were taken into consideration, justifying the adopted nomenclature. With the advances made in hardware as well as the rise of the software defined noise radar concept, waveform design emerges as an important research area related to such systems. The possibility of generating signals with varied stochastic properties increased the potential in achieving systems with enhanced performances. The characterization of random phase and frequency modulated waveforms (more suitable for several applications) has then gained considerable notoriety within the radar community as well. Several optimization algorithms have been proposed in order to conveniently shape both the autocorrelation function of the random samples that comprise the transmit signal, as well as their power spectrum density. Nevertheless, little attention has been driven to properly characterize the stochastic properties of those signals through closed form expressions, jeopardizing the effectiveness of the aforementioned algorithms as well as their reproducibility. Within this context, this paper investigates the performance of several random phase and frequency modulated waveforms, varying the stochastic properties of their modulating signals.

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