The L-band geosynchronous synthetic aperture radar (GEO-SAR) has been widely praised for its advantages of short revisit time, wide coverage and stable backscattering information acquisition. However, due to the ultra-long integrated time, the echo will be affected by the time-variant background ionosphere, leading in particular to defocusing in the azimuth direction. Existing compensation methods suitable for low Earth orbit SAR (LEO-SAR) are based on the SAR image or the semi-focused image at the ionospheric phase screen, assuming that the ionosphere is time-frozen for a short integrated period; thus, accurate reconstruction of the time-variant characteristics for the ionosphere in GEO-SAR cannot be achieved. In this paper, a compensation method of background ionospheric effects on L-band GEO-SAR with fully polarimetric data is proposed. Considering the continuous variation of the ionosphere within the synthetic aperture, a decompression processing is proposed to reconstruct the echo by recovering the temporal sampling according to the imaging geometry. By virtue of the Faraday rotation angle, the time-variant total electron content (TEC) is accurately estimated with the reconstructed echo. Based on the established error model, the ionospheric effects are well compensated with the estimated TEC. Simulations with the real SAR data from ALOS-2 and the measured time-variant TEC from USTEC validate the effectiveness and performance of the proposed method. The impacts from thermal noise and polarimetric calibration error are also quantitatively analyzed. From this, the error thresholds are given to guarantee compensation accuracy, namely 18.96 dB for SNR, −15.63 dB for crosstalk and −1.02 dB to 0.31 dB for the amplitude of the channel imbalance, and the argument of the channel imbalance is suggested to be maintained as close to zero as possible.

Most of the noise in speech communication lines can be considered as Gaussian white noise. Voice activity detection (VAD) in noisy environment is an important process in many speech signal processing algorithms. Unlike the other VAD algorithms, this paper proposes a simple and novel VAD algorithm based on the probability distribution function (PDF) of FFT magnitudes of both clean speech and Gaussian white noise. When the signal-to-noise ratio (SNR) is high enough, the method using Gamma distribution to detect the speech performs well, while the method using Rayleigh distribution under lower SNR can be complementary. In addition, the threshold to determine which method to use is presented based on the tests under different SNR. Simulation results show that the proposed algorithm is efficient.Both the hardware and software of a low cost system for VAD are introduced, with the proposed algorithm achieved in a digital signal processor (DSP). Each detection takes on less than 100 ms, which can be used for real-time processing.

Abs tra C t: This paper discusses the features and application in signal processing for CMAC (Complex Multiplier Accumulator) GA3806.