A new time-frequency transform, known as short-time Lv transform （STLVT）, is proposed by applying the inverse Lv distribution to process consecutive segments of long data sequence. Compared with other time-frequency...A new time-frequency transform, known as short-time Lv transform （STLVT）, is proposed by applying the inverse Lv distribution to process consecutive segments of long data sequence. Compared with other time-frequency representations, the STLVT is able to achieve better energy concentration in the time-frequency domain for signals containing multiple linear and/or non-linear frequency modulated components. The merits of the STLVT are demonstrated in terms of the effects of window length and overlap length between adjacent segments on signal energy concentration in the time-frequency domain, and the required computational complexity. An application on the spectrum sensing for cognitive ratio （CR） by using a joint use of the STLVT and Hough transform （HT） is proposed and simulated.展开更多
基金supported by the National Natural Science Foundation of China(61571174)the Zhejiang Provincial Natural Science Foundation of China(LY15F010010)+3 种基金the Open Project of Zhejiang Key Laboratory for Signal Processing(ZJKL 4 SP–OP2013–02)the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry[2013]693 and[2015]1098the Fundamental Research Funds for the Central Universities(ZYGX2014J097)the Technology Foundation for Selected Overseas Chinese Scholar
文摘A new time-frequency transform, known as short-time Lv transform （STLVT）, is proposed by applying the inverse Lv distribution to process consecutive segments of long data sequence. Compared with other time-frequency representations, the STLVT is able to achieve better energy concentration in the time-frequency domain for signals containing multiple linear and/or non-linear frequency modulated components. The merits of the STLVT are demonstrated in terms of the effects of window length and overlap length between adjacent segments on signal energy concentration in the time-frequency domain, and the required computational complexity. An application on the spectrum sensing for cognitive ratio （CR） by using a joint use of the STLVT and Hough transform （HT） is proposed and simulated.
