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Proceedings Paper

Noise radar using random phase and frequency modulation
Author(s): Sune R. J. Axelsson
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Paper Abstract

Pulse compression radar is used in a great number of radar applications. Excellent range resolution and high ECCM performance can be achieved by wide-band modulated long pulses, which spread out the transmitted energy in frequency and time. By using random noise as waveform, the range ambiguity can be suppressed as well. The same limit in doppler resolution is achieved as for a coherent doppler radar when the time compression of the reference is tuned to that of the target. Mostly, the random signal is transmitted directly from a noise generating HF-source. A sine wave, which is phase or frequency modulated by random noise, is an alternative giving similar performance but higher transmitted mean power when peak-limited transmitters are applied. A narrower modulation noise bandwidth can also be applied to generate the same output bandwidth. For phase modulation, the bandwidth amplifying factor is simply the rms value of the phase modulation, and for a frequency modulating waveform the output rms bandwidth equals the rms value of the frequency modulation. The results also show that the range sidelobes can be highly suppressed compared with the sidelobes of the modulating signal. The mean and variance of the correlation integral are derived in terms of the autocorrelation function of the modulation. Finally, random bi-phase modulation and the effects of low-bit ADC at the correlation processing are analyzed and described. The advantages of low range sidelobes and enhanced range resolution make frequency and phase modulation attractive for a great number of applications.

Paper Details

Date Published: 12 January 2004
PDF: 12 pages
Proc. SPIE 5236, SAR Image Analysis, Modeling, and Techniques VI, (12 January 2004); doi: 10.1117/12.511224
Show Author Affiliations
Sune R. J. Axelsson, Saab Bofors Dynamics (Sweden)
Swedish Defence Research Agency (Sweden)


Published in SPIE Proceedings Vol. 5236:
SAR Image Analysis, Modeling, and Techniques VI
Francesco Posa, Editor(s)

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