Direction of arrival estimation of sources with intersecting signature in time–frequency domain using a combination of IF estimation and MUSIC algorithm

An Efficient Direction of Arrival Estimation Algorithm for Sources with Intersecting Signature in the Time–Frequency Domain

“…The length of the Gaussian window used in this study is 63 with σ = 1 and ∆ f = 2. Let us compare the proposed algorithm with the conventional TF-MUSIC algorithm, the ADTFD based ridge tracking algorithm [25], and spatial adaptive TFD based Viterbi algorithm [24], for the signal to noise ratios (SNR) ranging from −10 dB to 10 dB, using the mean square error (MSE) as a criterion. The MSE between the original angle, i.e., θ and estimated angle, i.e.,θ is computed as:…”

“…Let us now repeat the experiment for an under-determined scenario, i.e., for the number of sensors is equal to 2. In this scenario, the conventional TF-MUSIC algorithm cannot be employed, so the performance comparison is only made with the ADTFD based ridge tracking algorithm [25] and spatial adaptive TFD based Viterbi algorithm [24]. Figure 7 illustrates that the proposed method has the lowest MSE for SNR greater than −5 dB.…”

“…Matching pursuit has also been used to estimate the TF signature of sources but this method requires signals to follow some parametric model [23]. In our earlier studies [24,25], two DOA estimation methods have been developed for sources that intersect each other in the TF domain. One of these methods first enhances STFD with directional smoothing and then employs the Viterbi algorithm for IF estimation followed by source localization [24,26].…”

“…The major limitation of this method is the extensive computational cost due to both the computation of the STFD and the Viterbi algorithm [27]. The other method uses ridge detection and tracking-based IF estimation method for source separation followed by DOA estimation [25,28]. The method is thus computationally efficient as compared to the Viterbi algorithm, but it still requires the computation of adaptive directional time-frequency distribution (ADTFD), which is a computationally expensive step.…”

“…• A computationally efficient and accurate multi-sensor IF estimation algorithm is developed that achieves better performance without requiring the computation of Adaptive directional time-frequency distributions [28] or Viterbi algorithm [26] thus both reducing computational cost and resulting in improved performance. • A DOA estimation method, based on multi-sensor IF estimation, is developed that achieves better performance as compared to our existing methods, which are the Viterbi-based algorithm and ridge tracking approach, both in terms of computational cost and performance [24,25].…”