Modified Multiple Signal Classification Algorithm 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.

Wireless sensor network’s localization based on multiple signal classification algorithm

Wireless sensor networks (WSNs) are a number of sensitive nodes senses a physical phenomenon at the position of their deployment then sends information to the base station to take appropriate operation. (WSNs) are used in many applications such track military targets, discover fires, study natural phenomena such as earthquakes, humidity, heat, etc. The nodes are spread in large areas and it is difficult to locate them manually because they are published randomly by planes or any other method and since the information received from sensitive nodes is useless without knowing their location in this case a problem resulted in the positioning of the nodes. So it unacceptable to equip each sensor node with global position system (GPS) due to various problems such as raises cost and energy consumption. In this paper explained a non-GPS technique to self-positioning of nodes in (WSNs) by using the multiple signal classification (MUSIC) algorithm to determine the position of the active sensor through estimated the direction of arrival (DOA) of the node signal. Then modified MUSIC algorithm (M-MUSIC) to solve the problem of coherent signal. MATLAB program successfully used to simulate the proposed algorithm.

IBPSO-Based MUSIC Algorithm for Broken Rotor Bars Fault Detection of Induction Motors

In spectrum analysis of induction motor current, the characteristic components of broken rotor bars (BRB) fault are often submerged by the fundamental component. Although many detection methods have been proposed for this problem, the frequency resolution and accuracy are not high enough so that the reliability of BRB fault detection is affected. Thus, a new multiple signal classification (MUSIC) algorithm based on particle swarm intelligence search is developed. Since spectrum peak search in MUSIC is a multimodal optimization problem, an improved bare-bones particle swarm optimization algorithm (IBPSO) is proposed first. In the IBPSO, a modified strategy of subpopulation determination is introduced into BPSO for realizing multimodal search. And then, the new MUSIC algorithm, called IBPSO-based MUSIC, is proposed by replacing the fixed-step traversal search with IBPSO. Meanwhile, a simulation signal is used to test the effectiveness of the proposed algorithm. The simulation results show that its frequency precision reaches 10−5, and the computational cost is only comparable to that of traditional MUSIC with 0.1 search step. Finally, the IBPSO-based MUSIC is applied in BRB fault detection of an induction motor, and the effectiveness and superiority are proved again. The proposed research provides a modified MUSIC algorithm which has sufficient frequency precision to detect BRB fault in induction motors.

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.

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.