MUSIC-based Algorithm Research Articles

DIRECTION OF ARRIVAL ESTIMATION BASED ON SMOOTH MUSIC ALGORITHM COMBINED SPECTRUM SELECTION TECHNIQUES OF NOISE CHARACTERISTICS OF MARINE TARGETS EQUIPPED PROPELLER

This article demonstrates a proposal for a direction of arrival (DOA) estimation algorithm for passive sonar systems based on the combination of the smooth multiple signal classification (Smooth MUSIC) algorithm and frequency bin selection (BS) of the sound of marine propeller-equipped targets, so-called BS-SMUSIC. The harmonics of propeller shaft and blade frequencies are considered useful information for detecting targets and estimating their parameters, including DOA. Low frequency analysis recording (LOFAR) is utilized to implement BS. By analyzing MUSIC-based algorithm together BS, passive DOA estimation algorithms have been significantly improved. Simulation results have been given in both cases: uncorrelated and correlated sources. Outcomes demonstrate that the BS-SMUSIC algorithm has the best performance in comparison to three MUSIC-based algorithms (MUSSIC, Modified MUSIC and Root MUSIC) in all investigation cases: narrow band signals at 5 dB of signal to noise (SNR); wideband signals at -5 dB of SNR. Besides, BS-SMUSIC algorithm also archives the lowest root mean square error (RMSE) in SNR range -5 dB to 20 dB. In the SNR range, the RMSE of BS-SMUSIC algorithm remains stable and is about 0.3 ◦ at -5 dB of SNR. This performance reflects its ability to ensure stable operation, super resolution and high accuracy of BS-SMUSIC algorithm for the passive sonar systems.

Drone Detection and Classification Using Music (Multiple Signal Classification) Algorithm

Abstract: The rapid augmentation of drones in different sectors has raised concerns regarding privacy, security, and safety. This project proposes a novel drone detection and classification approach by integrating the YOLOv5 framework with a MUSICbased algorithm to address the above issues. The aim is to postulate an efficient and accurate model capable of identifying and classifying different types of drones in real time. The project leverages the YOLOv5 architecture incorporated with the MUSIC algorithm for superior speed and accuracy. By training YOLOv5 on a large dataset of drone images, the model can learn to identify visual features and specific characteristics unique to drones, permitting effective detection and classification

A MUSIC-Based Algorithm For Localization of Hybrid Near-Field and Far-Field Sources

A MUSIC-Based Algorithm For Localization of Hybrid Near-Field and Far-Field Sources

Joint angle and delay estimation for underwater acoustic multicarrier CDMA systems using a vector sensor

In this study, the problem of joint angle and delay estimation for underwater acoustic multicarrier CDMA systems using a vector sensor is addressed. By jointly utilising the temporal, pressure and velocity information inherent in the received signal, the temporal-vector-sensor manifold vector is constructed. Then, a MUSIC-based algorithm that contains several one-dimensional searches for jointly estimating azimuth angle, elevation angle and time-delay parameters of a ‘desired source’ operating in the multi-source and multipath environment is proposed. In addition, a closed-form ESPRIT-based algorithm is also presented for the scenario where the multipath propagation signals are emanated from a single source. Simulation results are finally presented to verify the efficacy of the proposed algorithms.

An Integer Time Delay Estimation Algorithm Based on Zadoff–Chu Sequence in OFDM Systems

This paper presents a multiple signal classification (MUSIC)-based symbol integer delay estimation algorithm for orthogonal frequency-division multiplexing (OFDM) systems using the Zadoff-Chu (ZC) sequence. By exploiting a property of the ZC sequence, this algorithm is derived from the maximum-likelihood (ML) integer delay estimation, which retains the ability of coping with an incomplete OFDM symbol with reduced complexity. It can be realized without information on channel statistic and noise. Furthermore, taking into account the inefficiency of the MUSIC-based algorithm in estimating the integer delay if the fractional delay is compensated on local ZC, we present a real-valued delay-tracking scheme that iteratively adjusts the sampling clock phase of an analog-to-digital converter (ADC). In particular, the fractional delay is tracked via the ML scheme, whereas the integer delay is estimated via the MUSIC-based algorithm. The numerical results finally confirm the efficiency and the reliability of the proposed MUSIC-based algorithm, even in the case of a small number of samples.

Blind multiuser spreading sequences estimation algorithm for the direct-sequence code division multiple access signals

By combining the multiple signal classification (MUSIC)-based algorithm, proposed by Haghighat and Soleymani, and the segmentation-based idea, a blind multiuser spreading sequences estimation algorithm for the direct-sequence code division multiple access (DS-CDMA) signals is presented in this study. The main idea of the proposed algorithm is to divide the spreading sequences into short-time segments so as to recover them in a sequential procedure. First, the received signal samples are divided into K collections of temporal windows. Each collection is corresponding to a group of short-time segments of the spreading sequences. Secondly, by performing a sub-segmentation-based estimation scheme on the first collection of temporal windows, the first short-time segments of all the users' spreading sequences are extracted. Finally, with the help of the segments acquired in advance, the remaining segments of the spreading sequences can be recovered chip-by-chip through K−1 subspace projection loops. This algorithm can be applied not only to the synchronous short-code (SC) DS-CDMA signals, but also to the long-code (LC) DS-CDMA signals in non-cooperative contexts, even with minus SNR. Simulations are presented to illustrate the performance of the proposed method. In addition, for the synchronous SC-DS-CDMA signals, the computational cost of the proposed algorithm is much lower than that of the MUSIC-based algorithm. Furthermore, a derivation for blind synchronisation of the synchronous SC-DS-CDMA signals is also presented.