Performance enhancement of MPM DoA estimation technique using wavelet De-noising filter

Direction-of-arrival (DoA) estimation is now an imperative part in many radar applications and localization techniques. There are numerous algorithms that have been studied in the previous decades for DoA, for example: MUSIC, ESPRIT, and Matrix Pencil Method (MPM), which are subspace super resolution methods. MPM is one of the most commonly used subspace based techniques. It is generally utilized for DoA estimation because of its effortlessness and high resolution contrasted with other subspace techniques. But, it suffers from performance deterioration under low Signal-to-Noise Ratio (SNR) conditions. This paper, explores the possibility of utilizing the wavelet de-noising technique to intercept the degradation in the performance of MPM under different SNR values. Wavelet De-noising is intended to remove noise or distortion from signals while retaining the original quality of the signal. The simulation results indicate that the Daubechies wavelet (db12) at 5 levels of decomposition is the most suitable wavelet for de-noising the signals under test. Also, the results show that the proposed wavelet de-noising matrix pencil method (WDMPM) outperforms the traditional MPM.