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.
Resumo-Uma gravação deáudio pode estar corrompida por um sinal da rede elétrica, a ENF (do inglês Electrical Network Frequency). A assinatura do sinal de redeé dependente da região-grandeárea ou país-de maneira que pode ser explorada para descobrir a localização de gravações de mídia. Neste trabalho, construímos métodos de prever estas localizações utilizando ferramentas consolidadas naárea de análise de dados, os classificadores SVM e Random Forest. Após apresentarmos o problema, comparamos os resultados obtidos na aplicação destas técnicas a um mesmo conjunto de dados.É discutida também a possibilidade da amostra não pertencer a nenhuma das regiões. Palavras-Chave-Frequência da rede elétrica, análise forense, rede elétrica, aprendizagem de máquina.
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