Detection and Fast Frequency Estimation of Multi-Component Sinusoidal Signals Using Approximate Kernel DFT

By using the approximate kernel DFT, an improved algorithm for detection and frequency estimation of multi-component sinusoidal signals is presented. A term for correct the frequency is constructed by using the real parts or the imaginary parts of the approximate kernel DFT coefficients, and a robust unsupervised threshold for detecting sinusoidal signals formed by using the ratio of the maximum value to median value of real parts or imaginany parts. The algorithm avoids the complex operations in the traditional correction and detection algorithms. Besides, a hardware implementation scheme, approximate kernel FFT is introduced and detection of the chirp signal is studied using the approximate kernel FFT as well. Examples are provided to illustrate the effectiveness of the presented algorithm.