Multiple Signal Classification Research Articles

Waveform Design and DoA-DoD Estimation of OFDM-LFM Signal Based on SDFnT for MIMO Radar

In comparison to orthogonal frequency division multiplexing (OFDM), the OFDM linear frequency modulation (OFDM-LFM) signal has similar time-bandwidth product, lower peak-to-average power ratio, and higher sensitivity to Doppler frequency shift, making it a promising candidate waveform for multiple-in multiple-out (MIMO) radar applications. However, the OFDM-LFM signal is unsatisfactory in practical applications due to the challenges in waveform design and its non-stationary property, which significantly degrades the performance of current high-resolution subspace techniques for direction of departure (DoD) and direction of arrival (DoA) angle estimation. In this paper, we first proposed a novel OFDM-LFM waveform design method based on the scale discrete Fresnel transform (SDFnT), which efficiently generates orthogonal LFM signals with unlimited number of subcarriers and can be convieniently implemented using the fast Fourier transform (FFT). Then, for angle estimation in OFDM-LFM MIMO radars, we presented a new scale discrete Fresnel transform multiple signal classification (SDFnT-MUSIC) method. By converting the steering vector into the chirp domain and making it non-time-vary, high resolution angle estimation for OFDM-LFM wideband signals can be achieved. Simulation results shows that the proposed waveform design method requires less computational time than conventional method based based on the fractional Fourier transform (FrFT), whereas the proposed DoA-DoD estimation method outperform the FrFT-MUSIC method in terms of computational complexity and target angle estimate performance.

High-Precision Fast Direction-of-Arrival Estimation Method for Planar Array

The multiple signal classification method for direction-of-arrival estimation is widely applied in practical scenarios. However, the multiple signal classification method with planar array requires 2-dimensional on-grid spectrum searches, which would lead to the grid mismatch and high computational complexity. Therefore, a high-precision fast direction-of-arrival estimation method for planar array is proposed. In the proposed method, a 2-stage grid search approach over the 2-dimensional spectrum is firstly applied to obtain a quick coarse estimation of direction of arrival. Then, the estimation of higher precision is achieved via a quadratic surface fitting method. Simulation results verified the effectiveness of the proposed method.

Single Channel Multiple Signal Classification Using Pseudo-Doppler

Single Channel Multiple Signal Classification Using Pseudo-Doppler

DOA Estimation Method in Echo-localization Ability for Human-beings

The principle of echo-localization processing of human beings is quite similar with animals like bats and dolphins, which has been widely applied in sonar. Direction of Arrival (DOA) methods plays an important role in estimating the radar, sonar and many other echo-localization communication fields. Typical methods in resolving echo-localization problems including classical (Multiple Signal Classification) MUSIC, (Estimation of Signal Parameter via Rotational Invariance Technique), ESPRIT and compressed sensing. The echo-localization methods and the resolving ambiguity methods via uniform array are systematically introduced in this paper. On this basis, the fast and accurate algorithms for human-being echo-localization in parameter (azimuth angle, elevation angle and range) estimation of signal are further introduced from time domain and frequency domain. The accuracy of the angle and distance estimation of the algorithm calculated by computer simulation experiment is over 99.9% and 97% respectively. The effectiveness of DOA method was verified according to MUSIC algorithm, which provides a rapid and accuracy information in echo-localization ability of human being.

Super-resolution time delay estimation using exponential kernel correlation in impulsive noise and multipath environments

The time delay estimation (TDE) is an important and fundamental part in wireless communication signal processing, which has been widely used in localization, intelligent navigation, and radio monitoring. However, it remains a challenging task to support such TDE mechanisms with super-resolution in impulsive noise and multipath environments. Aiming at improving the TDE performance in both impulsive noise and multipath environments, in this paper, a novel super-resolution TDE method is proposed by using the defined exponential kernel correlation (EKC) function. In the proposed TDE method, EKC function is defined to suppress the non-Gaussian impulsive noise. Specifically, the theoretical analysis is also given to prove its boundedness and its effectiveness in non-Gaussian impulsive noise suppression. Further, a novel super-resolution TDE method is proposed by combining EKC function and multiple signal classification (MUSIC) method, which can significantly improve the estimation performance in terms of resolution and accuracy with both non-Gaussian impulsive noise and multipath environments. Finally, simulation experiments demonstrate that the proposed super-resolution TDE method maintains good performance even with a low characteristic exponent and low generalized signal-to-noise ratio (GSNR).

Spectral analysis for pulse rate variability assessment from simulated photoplethysmographic signals.

Introduction: Pulse rate variability (PRV) refers to the changes in pulse rate through time and is extracted from pulsatile signals such as the photoplethysmogram (PPG). Although PRV has been used as a surrogate of heart rate variability (HRV), which is measured from the electrocardiogram (ECG), these variables have been shown to have differences, and it has been hypothesised that these differences may arise from technical aspects that may affect the reliable extraction of PRV from PPG signals. Moreover, there are no guidelines for the extraction of PRV information from pulsatile signals. Aim: In this study, the extraction of frequency-domain information from PRV was studied, in order to establish the best performing combination of parameters and algorithms to obtain the spectral representation of PRV. Methods: PPG signals with varying and known PRV content were simulated, and PRV information was extracted from these signals. Several spectral analysis techniques with different parameters were applied, and absolute, relative and centroid-related frequency-domain indices extracted from each combination. Indices from extracted and known PRV were compared using factorial analyses and Kruskal-Wallis tests to determine which spectral analysis technique gave the best performing results. Results: It was found that using fast Fourier transform and the multiple signal classification (PMUSIC) algorithms gave the best results, combined with cubic spline interpolation and a frequency resolution of 0.0078Hz for the former; and a linear interpolation with a frequency resolution as low as 1.22 × 10-4, as well as applying a fifth order model, for the latter. Discussion: Considering the lower complexity of FFT over PMUSIC, FFT should be considered as the appropriate technique to extract frequency-domain information from PRV signals.

Cascade AOA estimation technology based on combined array antenna with computational complexity analysis

Since most studies for estimating an angle-of-arrival (AOA) based on the antenna array have considered the antenna array with a single configuration, they are not proper to simultaneously estimate AOAs of multiple signals with various frequencies. In this paper, we introduce a cascade AOA estimation technique consisting of CAPON and Beamspace Multiple Signal Classification (MUSIC), based on a Combined Array Antenna (CAA) with Uniform Rectangular Frame Array (URFA) and Uniform Circular Array (UCA), for enhancing the above problem. In addition, we provide the computational complexity analysis for showing the low computational complexity of this technique comparing to the conventional technique.

An Indoor Environment Sensing and Localization System via mmWave Phased Array

In this paper, an indoor layout sensing and localization system with testbed in the 60-GHz millimeter wave (mmWave) band, named mmReality, is elaborated. The mmReality system consists of one transmitter and one mobile receiver, both with a phased array and a single radio frequency (RF) chain. To reconstruct the room layout, the pilot signal is delivered from the transmitter to the receiver via different pairs of transmission and receiving beams, so that multipath signals in all directions can be captured. Then spatial smoothing and the two-dimensional multiple signal classification (MUSIC) algorithm are applied to detect the angle-of-departures (AoDs) and angle-of-arrivals (AoAs) of propagation paths. Moreover, the technique of multi-carrier ranging is adopted to measure the path lengths. Therefore, with the measurements of the receiver in different locations of the room, the receiver and virtual transmitters can be pinpointed to reconstruct the room layout. Experiments show that the reconstructed room layout can be utilized to localize a mobile device via the AoA spectrum.

Description of the development of an experimental stand for the detection of defects in pipelines

The article discusses a bench that allows you to monitor the change in the dynamic characteristics of the medium when various types of defects appear in the connection lines. The setting makes it possible to select the optimal method for recording the measured values. The experimental stand was created as close as possible to the real conditions of the pipeline operation. To detect damage to the air circuit, the integration of technical developments with a software algorithm based on the use of the multiple signal classification method (MUSIC) was applied. The developed stand opens up opportunities for conducting practical experiments and obtaining experimental data directly when detecting pipeline defects.

A novel displaced coprime parallel array for two-dimensional direction of arrival estimation

ABSTRACT The traditional coprime parallel array (TrCPA) is prone to suffer from the problem of angle ambiguity, due to its larger inter-element spacing and limited array aperture, for two-dimensional direction of arrival estimation. And the classic multiple signal classification (MUSIC) algorithm has heavy complexity and has poor estimation ability in some unsatisfactory situations. Both restrict the improvement of angle estimation performance. In this paper, we design a novel displaced coprime parallel array termed NDCPA, by rearranging and misplacing all subarrays of TrCPA along the coordinate axis, which brings about larger array aperture making contributions to superior angle estimation accuracy. Besides, we propose an improved two-step reduced-dimensional MUSIC algorithm to further enhance angle estimation accuracy. The proposed method reconstructs the covariance matrix, which can fully exploit the information contained in uniform linear subarrays. Meanwhile, the transformation of angle domain divides the original two-dimensional search into two one-dimensional searches, reducing complexity to a certain extent. Finally, simulation results verify the effectiveness and applicability of the proposed configuration and method compared with the other structures and methods.

Scalable-resolution structured illumination microscopy.

Structured illumination microscopy suffers from the need of sophisticated instrumentation and precise calibration. This makes structured illumination microscopes costly and skill-dependent. We present a novel approach to realize super-resolution structured illumination microscopy using an alignment non-critical illumination system and a reconstruction algorithm that does not need illumination information. The optical system is designed to encode higher order frequency components of the specimen by projecting PSF-modulated binary patterns for illuminating the sample plane, which do not have clean Fourier peaks conventionally used in structured illumination microscopy. These patterns fold high frequency content of sample into the measurements in an obfuscated manner, which are de-obfuscated using multiple signal classification algorithm. This algorithm eliminates the need of clean peaks in illumination and the knowledge of illumination patterns, which makes instrumentation simple and flexible for use with a variety of microscope objective lenses. We present a variety of experimental results on beads and cell samples to demonstrate resolution enhancement by a factor of 2.6 to 3.4 times, which is better than the enhancement supported by the conventional linear structure illumination microscopy where the same objective lens is used for structured illumination as well as collection of light. We show that the same system can be used in SIM configuration with different collection objective lenses without any careful re-calibration or realignment, thereby supporting a range of resolutions with the same system.

Displacement Estimation Using 3D-Printed RFID Arrays for Structural Health Monitoring.

Radio frequency identification (RFID) tags are small, low-cost, wearable, and wireless sensors that can detect movement in structures, humans, or robots. In this paper, we use passive RFID tags for structural health monitoring by detecting displacements. We employ a novel process of using 3D printable embedded passive RFID tags within uniform linear arrays together with the multiple signal classification algorithm to estimate the direction of arrival using only the phase of the backscattered signals. We validate our proposed approach via data collected from real-world experiments using a unipolar RFID reader antenna and both narrowband and wideband measurements.

Stable subspace dimension reduced MUSIC for blade tip timing

Multiple signal classification (MUSIC) is a parameter extraction method for blade tip timing to suppress the under-sampled problem. Its faster version, subspace dimension reduced MUSIC (SDR-MUSIC), has been proposed, which uses a single noise vector instead of the whole noise subspace to reduce computational complexity. However, the principle of random noise vector selection leads to instability in frequency identification. Therefore, we replace the random vector with the min-norm vector to obtain a stable SDR-MUSIC (SSDR-MUSIC). The superiority of the min-norm vector can be mathematically proven by the location of the zeros of the polynomial. Simulated and experimental tests indicate that SSDR-MUSIC gains stability when the min-norm vector is used while maintaining computational efficiency.

High‐Resolution Fault‐Rupture Imaging by Combining a Backprojection Method With Binarized MUSIC Spectral Image Calculation

AbstractBackprojection (BP) methods are widely used for estimating the fault rupture processes; however, they are inherently susceptible to noise. Hence, noise suppression is an important research target. In this paper, we develop a fault rupture imaging method by combining beamforming‐based BP and MUltiple Signal Classification (MUSIC), which realizes artificial noise suppression at a high spatial resolution. The stations are grouped into arrays according to the SH wave radiation coefficients, and MUSIC analysis is performed on each array. The MUSIC spectral images of these arrays are binarized and then multiplied by the BP images. Spatial filtering is also applied to the images based on the possible range of the rupture velocity and rise time. When tested using synthetic test data, the proposed method worked as expected. We then applied this method to the 2016 Kumamoto earthquake by interpolating the travel times from the observed travel times of relocated hypocenters using a 3‐D velocity structure model. In the area of large slip and slip rate approximately 30–50 km from the hypocenter on the Futagawa fault, the spatiotemporal evolution of the fault ruptures and waveform inversion results were generally in harmony. The distributions of the low‐ and high‐frequency seismic radiations are complementary, as is understood in the context of fault rupture physics. This method can aid in understanding and modeling the details of seismic radiation sources, enabling the accurate prediction of strong ground motion even in near‐fault areas.

Back-projection results for the M w 7.5, 28 September 2018 Palu earthquake-tsunami

A back-projection technique allows seismologists to analyse rupture properties once seismic signals from a dense array of seismic networks are available. The observed waveforms are then traced back in space and time to the source region of an earthquake under investigation. In this study, the method utilised the back-projection image of the recorded high-frequency P-seismic waveforms filtered at 0.25-1.0 Hz by Multiple Signal Classification (MUSIC) processing to estimate the extent and the spread of earthquake rupture propagation of the M w 7.5 Palu event on 28 September 2018, generating a severe tsunami. This study aims to estimate rupture duration, its extent (the distance over which rupture propagates) and the corresponding speed, and rupture directivity (where most of the seismic energy propagates). The results revealed that the front of rupture propagates slightly offset southward at about 2.9 km/s over a distance of ~140 km away from the epicentre for about 49 s before slowing down at much smaller amplitudes after reaching the south end of Palu Bay. This finding is consistent with an NNW-SSE orientation of the active Palu-Koro Fault lying along the bay, suggesting that the strong ground motion is associated with the fault activities. This study has therefore substantial implications for enhanced earthquake and tsunami early warnings, helping the government and local authority build community resilience by warning people at risk from future possible earthquake and tsunami hazards.

Two Low-Complexity DOA Estimators for Massive/Ultra-Massive MIMO Receive Array

Eigen-decomposition-based direction finding methods of using large-scale/ultra-large-scale fully-digital receive antenna arrays lead to a high or ultra-high complexity. To address the complexity dilemma, in this letter, three low-complexity estimators are proposed: partitioned subarray auto-correlation combining (PSAC), partitioned subarray cross-correlation combining (PSCC) and power iteration max correlation successive convex approximation (PI-Max-CSCA). Compared with the conventional no-partitioned direction finding method like root multiple signal classification (Root-MUSIC), in the PSAC method, the total set of antennas are equally partitioned into subsets of antennas, called subarrays, each subarray performs independent DOA estimation, and all DOA estimates are coherently combined to give the final estimation. For a better performance, the cross-correlation among sub-arrays is further exploited in the PSCC method to achieve the near-Cramer-Rao lower bound (CRLB) performance with the help of auto-correlation. To further reduce the complexity, in the PI-Max-CSCA method, using a fraction of all subarrays to make an initial coarse direction measurement (ICDM), the power iterative method is adopted to compute the more precise steering vector (SV) by exploiting the total array, and a more accurate DOA value is found using ICDM and SV through the maximum correlation method solved by successive convex approximation. Simulation results show that as the number of antennas goes to large-scale, the proposed three methods can achieve a dramatic complexity reduction over conventional Root-MUISC. Particularly, the PSCC and PI-Max-CSCA can reach the CRLB while the PSAC shows a substantial performance loss.

Tunable High‐Resolution Synthetic Aperture Radar Imaging

AbstractWe have recently introduced a modification of the multiple signal classification method for synthetic aperture radar. This method incorporates a user‐defined parameter, ϵ, that allows for tunable quantitative high‐resolution imaging. However, this method requires relatively large single‐to‐noise ratios (SNR) to work effectively. Here, we first identify the fundamental mechanism in that method that produces high‐resolution images. Then we introduce a modification to Kirchhoff Migration (KM) that uses the same mechanism to produce tunable, high‐resolution images. This modified KM method can be applied to low SNR measurements. We show simulation results that demonstrate the features of this method.

Mapping the invasive palm species Arenga obtusifolia using multiple endmember spectral mixture analysis (MESMA) and PRISMA hyperspectral data in Ujung Kulon National Park, Indonesia

Arenga obtusifolia or “langkap” is an invasive palm that interferes with the growth of native vegetation in Ujung Kulon National Park. This study aims to map A. obtusifolia distribution in Ujung Kulon using PRISMA hyperspectral imagery and MESMA (Multiple Endmember Spectral Mixture Analysis). 38 spectra samples for the A. obtusifolia, sand, wetland vegetation and other vegetation were collected, and optimized using the IES (Iterative Endmember Selection), Multiple Signal Classification (MUSIC) and Automated MUsic and Spectral Separability based Endmember Selection (AMUSES) algorithms. Here, different schemes of MESMA were tested by using spectral weighting (SPEC) and stable zone unmixing (STABLE). Our results demonstrated that MESMA and PRISMA data was able to identify A. obtusifolia with the accuracy ranging from 55.36 (AMUSES-STABLE) to 83.93% (MUSIC-STABLE). Our results indicated the potential of MESMA and PRISMA hyperspectral data using optimized spectra from MUSIC and stable zone unmixing for mapping invasive A. obtusifolia.

The design of a four-wing chaotic system and the application of synchronous control in weak signal detection

Simple fourth-order autonomous differential equations can exhibit chaotic properties. In this paper, a chaotic system with a four-wing attractor is proposed where the varying number of attractor wings depends not only on the system parameters but also on the initial state of the system. First, the phase diagram, Lyapunov exponential (LE) spectrum, bifurcation diagram, Poincaré section diagram and 0–1 test diagram can verify that the system has more complex dynamic characteristics. Meanwhile, not only is the randomness of the system is verified by complexity analysis, but the multistability of the system, namely, the coexistence attractor, is also simulated. Second, using Multisim to build an analog circuit diagram, the circuit simulation results and numerical simulation results coincide, proving the circuit feasibility of the system. Finally, a suitable controller is designed based on Lyapunov stability theory to realize the synchronization of the drive-response system. On the basis of synchronization, disturbance (measured signal) is added to the response system to transform the signal detection into the synchronization error analysis of the drive-response synchronization system. The frequency of synchronization error is obtained by frequency domain analysis, and the frequency value estimated by the multiple signal classification (MUSIC) algorithm. It is found that the proposed chaotic system has more complex dynamics. The method of synchronization control error combined with spectrum estimation can effectively estimate the frequency of weak signal and provide a large detection threshold for weak signal detection.

Multiple-Target Localization by Millimeter-Wave Radars With Trapezoid Virtual Antenna Arrays

We consider the problem of localizing multiple targets by millimeter wave (mmWave) radars with irregular antenna placement, i.e., trapezoid virtual antenna array. The goal is to estimate both the number of targets and their 3-D locations. While many well-known algorithms have been developed for either problems, they still suffer from several limitations, such as the need for a large amount of sampled radar data and high computation complexity. In this work, we develop an efficient solution by exploring the received signal structure in two steps: 1) estimating the number of targets and their ranges by extending Barone’s method to handle data from multiple antennas and 2) estimating the angle of arrival of each target by a Least-Square algorithm optimization. The proposed algorithm has been evaluated through Monte-Carlo simulations and an indoor testbed. By comparing with baseline algorithms, including 2D-FFT and multiple signal classification (MUSIC), we find that the proposed algorithm has the best performance in high signal-to-noise ratio regimes.