Noise radars are electromagnetic systems that use random signals for detecting and locating reflecting objects. Besides high performance against external interferences (intentional or not), the stochastic nature of the transmitted waveforms may lead to the suppression of range ambiguity in the detection of targets and low range sidelobes, if systems parameters are properly chosen. This paper addresses a probabilistic analysis to derive mathematical expressions for the signal-to-noise ratios, the peak-to-sidelobe ratios and the signal-to-interference ratios (due to ambiguous targets) throughout a typical receiver processing chain of a pulsed FM noise radar. A receiver that employs matched filtering and pulse integration prior to detection was considered. Pulse compression and integration gains (in signal-to-noise and signal-to-interference, due to ambiguous targets, ratios) are also derived. The analysis provides closed-form expressions relating the precise dependence of sidelobe levels as well as interference levels due to ambiguous targets to the integration time, the transmit signal bandwidth, and the number of integrated pulses.
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.
International audiencePolarimetric incoherent target decomposition aims at accessing physical parameters of illuminated scatters through the analysis of the target coherence or covariance matrix. In this framework, independent component analysis (ICA) was recently proposed as an alternative method to eigenvector decomposition to better interpret non-Gaussian heterogeneous clutter (inherent to high-resolution synthetic aperture radar systems). Until now, the two main drawbacks reported of the aforementioned method are the greater number of samples required for an unbiased estimation, when compared to the classical eigenvector decomposition, and the inability to be employed in scenarios under the Gaussian clutter assumption. In this paper, both drawbacks are analyzed. First, a Monte Carlo approach is performed in order to investigate the bias in estimating Touzi's target-scattering-vector-model parameters when ICA is employed. Simulated data and a RAMSES X-band image acquired over Brétigny, France, are taken into consideration to investigate the bias estimation under different scenarios. Finally, the performance of the algorithm is also evaluated under the Gaussian clutter assumption and when spatial correlation is introduced in the model
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