Algorithm Research For Signal Processing On Bioradar

The signal processing algorithm for bioradar was mainly discussed in this paper. Life signals are composed of breathing and heartbeat signals. They are low-speed target signals with very small Doppler frequency shifts and very weak echo signals easily submerged in clutter noise. Detecting and extracting the weak echo signals from the strong clutter background becomes the hot spot, and it is also the keystone in this paper.The echo signals of bioradar include life signals and clutter signals. The data sampling and analysis results show that the life signals are low SNR, low-frequency, semi-periods. The life signals can be built as hamonic modeling. Clutter signals can be approximately gaussian noise. Therefore the signals processing for bioradar can be simplified the harmonic model distributed by Gauss clutter. Extracting the life signals, restraining the clutter signals and improving the SNR can be done.Some algorithms of signal processing for bioradar and shortcoming are analyzed in this paper. The method of adaptive spectral-line enhancement was introduced, which utilized the error rate to extract the life signals and restrain the clutter signals. The result of simulation shows that the algorithm of adaptive spectral-line enhancement can pick up life signals efficiently.Finally, the comparison with the result of adaptive spectral-line enhancement with the error rate variable step length LMS, traditional variable step length LMS, fixed step length LMS at detecting low-velocity target shows that the algorithm with error rate has smaller steady-state error and convergence rate and SNR is improved. The reasonable combination of various signal processing methods can be more accurate and efficient to extract the life signals, and it will have more extensive in the future.