Article

Reducing the noise variance in ensemble-averaged randomly scaled sonar or radar signals

Centre for Integrative Genetics, Norwegian Univ. of Life Sci., CIGENE
IEE Proceedings - Radar Sonar and Navigation (Impact Factor: 0.55). 11/2006; DOI: 10.1049/ip-rsn:20050104
Source: IEEE Xplore

ABSTRACT Ensemble-averaged randomly scaled radar or sonar pulses are considered and the statistical theory for the corresponding phase and amplitude modulations are developed. Random scaling expresses varying radar cross-sections for scattering objects or varying antenna gain of a sweeping emitter. The noise variance of the modulations depends on the distribution function of the scaling, and how to minimise the variance by rejecting pulses below a certain amplitude threshold is shown. The theory is asymptotic in the sense that it is more accurate for increasing signal-to-noise ratios (SNRs). In a test case with uniformly distributed scaling, sufficient accuracy is reached for an average SNR larger than ~5 for the phase average and ~15 for the amplitude average

0 Bookmarks
 · 
68 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: A simple equation is presented that can be used to predict the improvement in bit-resolution that can be obtained by digital signal averaging in the presence of added random noise.
    Talanta 08/1981; 28(7 Pt 2):547-9. · 3.50 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: When noisy repetitive signals are observed, the noise cancellation is achieved using the synchronous averaging method but the counterpart of such an approach is the difficulty to have perfectly aligned signals. We are interested in characterizing the departures from perfect alignment; we propose a novel method to estimate both the jitter variance without estimating the delays, and the amplitude fluctuations.
    Signal Processing. 01/1996; 51(1):41-53.
  • [Show abstract] [Hide abstract]
    ABSTRACT: We investigate the properties of ensemble averaged data from a uniform quantizer, when the quantizer input signal is noisy. An expression for the mean-square error (MSE) MSE(σ,N) of the ensemble averaged data, accounting for an ensemble of finite length N, and noise RMS σ, is obtained. Previously published results for N=1 and N→∞ are recovered. For intermediate N, we show that there is an optimal noise RMS, σ<sub>opt</sub>(N), which minimizes the MSE. Such a minimum point exists regardless of the type of noise probability distribution function. Conditions on σ and N for achieving a smaller MSE than in the noise-free case (Δ<sup>2</sup>/12) are discussed. The convergence properties of MSE(σ,N) for increasing N, and the effect of applying uniformly distributed dither, is established.
    IEEE Transactions on Instrumentation and Measurement 07/2005; · 1.36 Impact Factor