This paper proposes an adaptive comb filter with flexible notch gain. It can appropriately remove a periodic noise from an observed signal. The proposed adaptive comb filter uses a simple LMS algorithm to update the notch gain coefficient for removing the noise and preserving a desired signal, simultaneously. Simulation results show the effectiveness of the proposed comb filter.

This paper proposes a new adaptive comb filter which automatically designs its characteristics. The comb filter is used to eliminate a periodic noise from an observed signal. To design the comb filter, there exists three important factors which are so-called notch frequency, notch gain, and notch bandwidth. The notch frequency is the null frequency which is aligned at equally spaced frequencies. The notch gain controls an elimination quantity of the observed signal at notch frequencies. The notch bandwidth controls an elimination bandwidth of the observed signal at notch frequencies. We have previously proposed a comb filter which can adjust the notch gain adaptively to eliminate the periodic noise. In this paper, to eliminate the periodic noise when its frequencies fluctuate, we propose the comb filter which achieves the adaptive notch gain and the adaptive notch bandwidth, simultaneously. Simulation results show the effectiveness of the proposed adaptive comb filter.

In this paper, a new approach to adaptive direction-of-arrival (DOA) estimation based upon a database retrieval technique is proposed. In this method, angles and signal powers are quantized, and a set of true correlation vectors of the array antenna input vectors for various combinations of the quantized angles and signal powers is stored in a database. The k-d tree is then selected as the data structure to facilitate range searching. Estimated a correlation vector, range searching is performed to retrieve several correlation vectors close to it from the k-d tree. The DOA and the signal power are estimated by taking the weighted average of angles and powers associated with the retrieved correlation vectors. Unlike the other high-resolution methods, this method requires no eigenvalue computation, thus allowing a fast computation. It is shown through simulation results that the processing speed of the proposed method is much faster than that of the root-MUSIC that requires the eigenvalue decomposition.