Engineering topics

Butman, S. A.

Publications and source records attributed to Butman, S. A..

22 records · Page 2

NASA NTRS1976

Linear feedback rate bounds for regressive channels

Bounds for the linear feedback capacity of m-th order Gaussian autoregressive channels are derived. The upper bound is tighter than that found by Tiernan and Schalwijk (1974) for the feedback capacity of a first-order autoregressive Gaussian channel with not necessarily linear processing. The separation between the upper and lower bounds is small, and it is conjectured that the lower bound converges to the feedback capacity of the first-order channel as the number of signals tends to infinity.

Butman, S. A.↗

NASA NTRS1976

Simulation of the Effects of Hard Limiting on Image Quality of Synthetic Aperture Radar

Starting with a magnetic tape of a scene viewed by the Landsat satellite, the radar return of reflectors whose average intensity matched that of the picture elements in the scene has been simulated. The returns were processed in three ways: normally or with no quantization, with a procedure simulation IF hard limiting, and with a procedure simulating video (baseband) hard limiting. For each type of processing an image for a one, two, and four-look system has been developed. It was found that IF limiting is slightly better than video limiting, while both can be reasonable trade-offs of image quality for reduced data rates when the number of looks is four or less. These conclusions are supported by photographs representing the different processing techniques.

Lipes, R. G.↗

NASA NTRS1974

Capacity of noncoherent MFSK channels

Performance limits theoretically achievable over noncoherent channels perturbed by additive Gaussian noise in hard decision, optimal, and soft decision receivers are computed as functions of the number of orthogonal signals and the predetection signal-to-noise ratio. Equations are derived for orthogonal signal capacity, the ultimate MFSK capacity, and the convolutional coding and decoding limit. It is shown that performance improves as the signal-to-noise ratio increases, provided the bandwidth can be increased, that the optimum number of signals is not infinite (except for the optimal receiver), and that the optimum number decreases as the signal-to-noise ratio decreases, but is never less than 7 for even the hard decision receiver.

Bar-David, I.↗

NASA NTRS1974

Performance of noncoherent MFSK channels with coding

Computer simulation of data transmission over a noncoherent channel with predetection signal-to-noise ratio of 1 shows that convolutional coding can reduce the energy requirement by 4.5 dB at a bit error rate of 0.001. The effects of receiver quantization and choice of number of tones are analyzed; nearly optimum performance is attained with eight quantization levels and sixteen tones at predetection S/N ratio of 1. The effects of changing predetection S/N ratio are also analyzed; for lower predetection S/N ratio, accurate extrapolations can be made from the data, but for higher values, the results are more complicated. These analyses will be useful in designing telemetry systems when coherence is limited by turbulence in the signal propagation medium or oscillator instability.

Butman, S. A.↗