Modified Multiple Signal Classification Research Articles

Sound Source Identification of Vehicle Noise Based on a Microphone Array With a Modified Multiple Signal Classification Algorithm

In this paper, a modified multiple signal classification (MUSIC) algorithm tailored for sound source identification (SSI) of vehicle noise is introduced and experimentally validated. A uniform planar microphone array (UPMA) is formulated for mathematical modeling, with its SSI-oriented parameters selected based on the primary frequency spectrum of vehicle noise. Simulations are conducted to compare the SSI accuracy of two conventional spatial spectra estimation (SSE) algorithms: the Capon algorithm and the MUSIC algorithm. The results demonstrate that the MUSIC algorithm, which relies on the eigenvalues of a covariance matrix to estimate signal direction, exhibits superior SSI resolution under low signal-to-noise ratio (SNR) conditions. However, it faces challenges in distinguishing between coherent or closely spaced signals. To address this, a modified MUSIC algorithm is proposed by reconstructing the covariance matrix of received signals and the SSE function. Simulation outcomes indicate that the modified MUSIC significantly outperforms the conventional version, owing to its enhanced SSI resolution. The accuracy of the SSI system, incorporating the UPMA and the modified MUSIC, is verified using a low-frequency volume source. Ultimately, the devised SSI system is successfully deployed to identify noise sources in a vehicle at different operational conditions, further validating the efficacy of the modified MUSIC. The UPMA and the modified MUSIC presented in this study have direct applicability in vehicle noise source identification and may be extended to other sound-related engineering fields for SSI purposes.

Single Snapshot 2D-DOA Estimation in Wireless Location System

In this research paper, we have devised single-snapshot direction-of-arrival (DOA) algorithms for a uniform circular array (UCA) to resolve multiple targets. Estimation of signal parameters via rotational invariance technique (ESPRIT) and the modified multiple signal classification (MUSIC) algorithms are employed for high-resolution estimation of DOAs. Experimental results are shown to validate the efficacy of the proposed methods. Computer simulations prove that the proposed UCA-ESPRIT and the UCA-modified MUSIC algorithms using single snapshot provide the accurate estimation of DOAs but with the reduced computational complexity. The CPU time obtained using an intel i5 processor shows a significant reduction in computation time as compared to the classical methods. Hence the proposed DOA methods using single snapshot for UCA are suitable in wireless location systems when only a few snapshots or, in the worst case, a single snapshot of the antenna array is on hand for the signal estimation.

Angle Estimation of Weak Scatterers Using Improved MUSIC for Bistatic MIMO Radar

This letter studies the problem of estimating direction-of-departure (DOD) and direction-of-arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar with unknown source number, and a modified multiple signal classification (MUSIC) algorithm for weak scattering targets is proposed. We utilize the weighting coefficients to weight the corresponding eigenvectors to reduce the influence of signal subspace vectors that are erroneously classified as noise subspace vectors on angle estimation, which can effectively avoid ignoring weak scatterers by incorrect source number estimation. Some simulations are conducted to validate the effectiveness and robustness of the improved MUSIC method.

A Modified MUSIC Algorithm for Direction of Arrival Estimation in the Presence of Antenna Array Manifold Mismatch

We introduce a modified multiple signal classification (MUSIC) algorithm to estimate the directions of arrival (DOAs) of multiple radio frequency signals. We specifically consider an antenna array with an imperfectly calibrated array response. The modified MUSIC algorithm estimates the DOA of each signal individually after attempting to null the other signals that are present. We compare the new approach to the conventional MUSIC algorithm in simulations, and show that the new approach leads to improved performance. The new approach converges quickly and produces DOA estimates with smaller mean error and standard deviation than MUSIC over a range of incident signal separations. We recommend our approach for systems with challenging antenna placements, a time varying host platform, and limited processing capabilities.

Modified MUSIC estimation for correlated signals with compressive sampling arrays

This paper addresses the issue of the direction of arrival(DOA) estimation under the compressive sampling(CS) framework. A novel approach, modified multiple signal classification(MMUSIC) based on the CS array(CSA-MMUSIC), is proposed to resolve the DOA estimation of correlated signals and two closely adjacent signals. By using two random CS matrices, a large size array is compressed into a small size array, which effectively reduces the number of the front end circuit. The theoretical analysis demonstrates that the proposed approach has the advantages of low computational complexity and hardware structure compared to other MMUSIC approaches. Simulation results show that CSAMMUSIC can possess similar angular resolution as MMUSIC.

Optimal Design of Non-Uniform Linear Array Using Genetic Algorithm

An arrangement method for non-uniform linear array using genetic algorithm (GA) is proposed. It is a general purpose method and needs only the angle range of the signal directions and the desired aperture of the array. It has few parameters, simple processing steps and a strong stabilization. It can be applied to optimize arbitrary array configuration. Modified multiple signal classification (MMUSIC) algorithm is discussed for estimating coherent sources using the non-uniform linear array. Simulation results show that the performance of the direction of arrival (DOA) estimation has been improved effectively on contrast with other array structures, the validity of the proposed method is proved.

Performance Optimization of DOA Estimation in Non-Uniform Linear Antenna Array by PSO Algorithm

In order to estimate the coherent source, a modified multiple signal classification (MUSIC) algorithm is introduced. And a novel arrangement method for the non-uniform linear array by particle swarm optimization (PSO) algorithm is proposed. This method needs merely a signal source whose direction-of-arrival (DOA) has been exactly known. The proposed method has a simple processing and a strong stabilization. It could be applied to optimized arbitrary array configuration. The simulation verifies that the performance of DOA estimation is improved effectively, which has proved the validity of the proposed method.

2-D direction of arrival estimation based on signal correlation and modified MUSIC algorithm

The coherent signal is widespread in the actual environment.Concerning the problem that traditional 2-D Direction Of Arrival(DOA) algorithm of Multiple Signal Classification(MUSIC) cannot process the coherent signal,the Modified MUSIC(MMUSIC) algorithm was used to realize the 2-D DOA estimation for the coherent imaginaries signal.The applied range of the modified MUSIC algorithm was extended from 1-D Uniform Linear Array(ULA) to 2-D centre-symmetric array,and it had been deduced by theory that bearing performance of MMUSIC algorithm was inversely proportional to the cosine value of phase difference.For two coherent signals of being separated by more than 4 degrees,the successful probability of the 2-D MMSUIC algorithm can be more than 90% in simulation experiments.

Channel Parameter Estimation for Scatter Cluster Model Using Modified MUSIC Algorithm

Recently, the scatter cluster models which precisely evaluate the performance of the wireless communication system have been proposed in the literature. However, the conventional SAGE algorithm does not work for these scatter cluster-based models because it performs poorly when the transmit signals are highly correlated. In this paper, we estimate the time of arrival (TOA), the direction of arrival (DOA), and Doppler frequency for scatter cluster model by the modified multiple signal classification (MUSIC) algorithm. Using the space-time characteristics of the multiray channel, the proposed algorithm combines the temporal filtering techniques and the spatial smoothing techniques to isolate and estimate the incoming rays. The simulation results indicated that the proposed algorithm has lower complexity and is less time-consuming in the dense multipath environment than SAGE algorithm. Furthermore, the estimations’ performance increases with elements of receive array and samples length. Thus, the problem of the channel parameter estimation of the scatter cluster model can be effectively addressed with the proposed modified MUSIC algorithm.

An improved MMUSIC algorithm of direction of arrival estimation

SUMMARY An improved algorithm is presented for a signal's direction of arrival estimation to reduce the computational complexity of the existing modified multiple signal classification (MMUSIC) algorithm. In this paper, FFT algorithm is introduced to narrow the searching range of the direction angle. Afterwards, a compromise approach-based SVD and orthogonal–triangular decomposition takes the place of traditional double SVDs. Then, during matrix calculation, the noise subspace is achieved for the signal's direction of arrival. Finally, the system simulation demonstrates the efficiency and reliability of this novel MMUSIC algorithm. Compared with the existing algorithm, MMUSIC effectively saves the computational complexity with the approximation estimation performance. Copyright © 2011 John Wiley & Sons, Ltd.

Reactance Domain MUSIC Algorithm for Electronically Steerable Parasitic Array Radiator

Conventional adaptive array antenna processing must observe signals on all of the array antenna elements. However, because the low-cost electronically steerable parasitic array radiator (ESPAR) antenna has only a single-port output, none of the signals on the antenna's parasitic elements can be observed. A direct application of most of the algorithms for the conventional adaptive array antenna is impractical. In this paper, A technique of estimation of direction-of-arrivals (DoAs) is proposed for the ESPAR antenna. This technique is based on the modified MUltiple SIgnal Classification (MUSIC) algorithm. The correlation matrix used in the MUSIC algorithm is estimated from the signal received through the single-port output of the ESPAR antenna as it switches over a set of antenna patterns. Simulation results show that DoAs can be estimated by the reactance domain MUSIC algorithm for ESPAR antennas. Furthermore, the statistical performance on estimation error variance of the reactance domain MUSIC estimator is analyzed and compared with the Crame/spl acute/r-Rao lower bound. Analytic and empirical results show that high-resolution DoAs estimation can be achieved by using the reactance domain MUSIC algorithm for ESPAR antennas.