Arrival Tracking Research Articles

Direction of arrival tracking of non-circular signals: probabilistic hypothesis density filter with an improved likelihood function

Abstract Conventional direction of arrival (DOA) tracking methods typically track circular signals with a fixed number of sources without considering time-varying numbers of non-circular (NC) signals DOA tracking. To solve this problem, we propose a DOA tracking method for NC signals with a probabilistic hypothesis density (PDH) filter based on an improved likelihood function. We improve the likelihood function of PHD by taking advantage of the NC signal characteristics, thereby separating the mixed measures and constructing the likelihood function corresponding to each source. In addition, we exponentially weight the likelihood function to improve the accuracy further. Finally, we achieved DOA tracking of the time-varying NC signals using the PHD filtering implemented by sequential Monte Carlo (SMC). Based on simulation findings, the suggested approach outperforms the conventional method in terms of tracking accuracy and can address the issue of changing NC signal numbers throughout the tracking process.

An Enhanced Multi-target Multi-Bernoulli Particle Filtering for Direction of Arrival Tracking in the Presence of Impulsive Noise

An Enhanced Multi-target Multi-Bernoulli Particle Filtering for Direction of Arrival Tracking in the Presence of Impulsive Noise

A Modified δ-Generalized Labeled Multi-Bernoulli Filtering for Multi-Source DOA Tracking With Coprime Array

For the target tracking problem where the number of targets fluctuates with time and the measurement is a point measurement, the random finite set (RFS) class filtering is an available solution. However, in direction of arrival (DOA) tracking, the array observation is a super-positional value, and the tracking performance can be severely impaired if the RFS-based filter approach is applied. As a result, a novel measurement association mapping (NMAP) approach has been presented to cope with the mapping problem between the array observations and sources. Nevertheless, the tracking performance is poor when the number of particles is small. In this paper, a modified delta-Generalized Labeled Multi-Bernoulli (δ-GLMB) DOA tracking particle filter is proposed in combination with the NMAP strategy, which can achieve the same tracking performance with a smaller number of particles by modifying the particles in the δ-GLMB prediction step. Furthermore, the approach is extended to a coprime array and can achieve better DOA tracking performance than a uniform linear array. Simulation experiments validate the effectiveness of the proposed algorithm.

An Improved PHD Filtering for DOA Tracking With Sparse Array via Unscented Transform Strategy

Traditional direction of arrival (DOA) tracking methods are sensitive to the number of sources, and their performance degrades when the number of sources changes with time. To solve the problem of multi-sources time-varying DOA tracking, an improved probability hypothesis density (PHD) method for DOA tracking is proposed in this paper. We improve the particle diversity of the resampling method and enhance the tracking performance by preprocessing the predicted particles in the prediction step of the traditional PHD particle filtering method. Moreover, the proposed method is extended to sparse array scenario to further improve the DOA tracking accuracy. Performance of the algorithm can be demonstrated by simulations with better performance than the spatial smoothing PAST (SS-PAST) and existing PHD methods.

Joint DOA tracking and spectral signature estimation of wideband sources using a multi-spectral TBD-CPHD filter

In this paper, a novel method for direction of arrival (DOA) tracking of multiple unknown time-varying number of wideband emitters is presented using a random finite set (RFS) framework. This method combines multiple narrowband covariance-based measurements to form a wideband cardinalized probability hypothesis density (CPHD) filter using the parallel combination (PC) Lemma generally used in multisensor fusion. By doing this, each narrowband frequency bin acts as a separate virtual spectral sensor. The covariance-based measurements, represented by complex Wishart random matrices, also allow the method to have a track-before-detect (TBD) property, which has shown to be advantageous in very low signal-to-noise ratios (SNR). Furthermore, the proposed method can simultaneously monitor the spectrum of the environment and extract the spectral characteristics of the active sources, i.e. can work as some sort of cognitive radio (CR). The suggested algorithm is implemented using an auxiliary particle filter. The simulation results and the comparison with the known state-of-the-art approaches confirm the superiority of the proposed method in negative SNR scenarios and low number of snapshots.

Ultrasonic-Aided Fast-Layered Alternating Iterative Tensor Channel Estimation for V2X Millimeter-Wave Massive MIMO Systems

Millimeter-wave massive multiple-input multiple-output (MIMO) vehicle-to-everything (V2X) communications can support enhanced V2X applications for connected and automated vehicles. The design of millimeter-wave V2X communications is, however, not exempt from challenges as a result of fast time-varying propagation and highly dynamic vehicular networks and topologies. To address some of these challenges, we propose an ultrasonic-aided tensor channel estimation for V2X millimeter-wave massive MIMO systems to improve the safety and traffic efficiency of cooperative automated driving. At the receiver, the dimension of multidimensional complex V2X information is reduced by the subspace tensor decomposition model. In order to quickly track beam angle changes caused by vehicle position changes, the ultrasonic-aided direction of arrival (DOA) tracking method is adopted to provide information about the surrounding environment. Based on the ultra-high resolution quantization grid and adaptive iterative update of the dictionary matrix, the DOA of the ultrasonic signal can be tracked. The angle update information of the millimeter-wave signal can be obtained by converting the estimated angle information. Using the cost function with global characteristics and ultrasonic-aided DOA tracking, a fast-layered alternating iterative tensor algorithm is proposed for joint iterative channel estimation. Simulation results show that the proposed solution outperforms some advanced alternative methods.

A novel angle of arrival tracking method in large-scale-array systems

In this paper, we investigate the problem of angle of arrival (AOA) tracking for the large-scale array in terahertz (THz) communication, which has a large size and a narrow beam, highly demanding an accurate angle estimation. On the one hand, the system usually adopts a hybrid structure with limited radio-frequency (RF) chains, which increases the difficulty of angle estimation; on the other hand, the rapid mobility of users also brings new challenges to angle estimation. To address the above challenges, a two-stage tracking framework is proposed in this paper, which employs the random phase matrix and orthogonal long pilots in the first stage to reduce the complicated multi-user estimation to multiple single-user estimations, followed by using both wide and narrow beams in the second stage to serve high-speed and low-speed users. Furthermore, a generalized-approximated-message-passing (GAMP) method is proposed for facilitating a low-accuracy estimation of the angles, followed by adopting a modified expectation-maximization (EM) algorithm based phase estimation to unbiased estimate the instantaneous angle with the help of high-gain characteristics of the beams. The proposed structure can not only simplify the estimation complexity, but also improve the estimation accuracy due to its capability of transferring the non-linear problem of angle observation into a linear gaussian model. In addition, the Kalman tracking framework is employed for performing a continuous angle tracking. Numerical results show that the angle estimation based on the random phase matrix in the initial stage can obtain a high enough estimation accuracy, while the GAMP algorithm implemented in the second stage can quickly capture the angle range under the Rayleigh limit. The performance of the proposed EM-based tracking method is shown to outperform the traditional extended Kalman filter (EKF) method.

A Robust DOA Tracking Method Using a Nested Array in Impulse Noise

To achieve the underdetermined direction of arrival (DOA) tracking in impulse noise, we propose a robust DOA tracking method in this paper. Firstly, an infinite norm difference covariance (INDC) matrix is introduced to suppress the impulse noise. Secondly, using a nested array, we present a maximum likelihood DOA tracking equation based on the INDC matrix. Furthermore, a quantum-inspired multiverse algorithm is proposed to maximize efficiently the proposed tracking equation. The simulation results show that our method has better robustness and superiority compared to other DOA tracking methods, which can achieve underdetermined DOA tracking in impulse noise as well as in the multipath environment.

PHD Filtering for Multi-Source DOA Tracking With Extended Co-Prime Array: An Improved MUSIC Pseudo-Likelihood

To solve the problem of multi-source direction of arrival (DOA) tracking in co-prime array, a multi-source DOA tracking algorithm based on probability hypothesis density (PHD) filtering is proposed, which can adapt to the scenario where DOA and number of sources change with time. In this letter, we use the minimum description length (MDL) method to estimate the number of sources and construct a new noise subspace by performing eigenvalue decomposition (EVD) on the reconstructed signal subspace. An improved multiple signal classification (MUSIC) pseudo-spectrum is utilized to calculate the likelihood function of the proposed method. The likelihood function is further exponentially weighted to increase the weight of particles. Simulation results show that compared with the existing methods, this algorithm has better tracking performance.

Sparse Bayes Tensor and DOA Tracking Inspired Channel Estimation for V2X Millimeter Wave Massive MIMO System

Efficient vehicle-to-everything (V2X) communications improve traffic safety, enable autonomous driving, and help to reduce environmental impacts. To achieve these objectives, accurate channel estimation in highly mobile scenarios becomes necessary. However, in the V2X millimeter-wave massive MIMO system, the high mobility of vehicles leads to the rapid time-varying of the wireless channel and results in the existing static channel estimation algorithms no longer applicable. In this paper, we propose a sparse Bayes tensor and DOA tracking inspired channel estimation for V2X millimeter wave massive MIMO system. Specifically, by exploiting the sparse scattering characteristics of the channel, we transform the channel estimation into a sparse recovery problem. In order to reduce the influence of quantization errors, both the receiving and transmitting angle grids should have super-resolution. We obtain the measurement matrix to increase the resolution of the redundant dictionary. Furthermore, we take the low-rank characteristics of the received signals into consideration rather than singly using the traditional sparse prior. Motivated by the sparse Bayes tensor, a direction of arrival (DOA) tracking method is developed to acquire the DOA at the next moment, which equals the sum of the DOA at the previous moment and the offset. The obtained DOA is expected to provide a significant angle information update for tracking fast time-varying vehicular channels. The proposed approach is evaluated over the different speeds of the vehicle scenarios and compared to the other methods. Simulation results validated the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art researches.

Time-Varying DOA Tracking Algorithm Based on Generalized Labeled Multi-Bernoulli

Direction of arrival (DOA) tracking for multi-sources is a hot issue in array signal processing. To deal with the problem that sources DOA and their number are time-varying, a DOA tracking algorithm based on Generalized Labeled Multi-Bernoulli (GLMB) filter is proposed. Since the measurement value has only one set of data, the measurement association mapping (MAM) does not match, which leads to deviations in the GLMB filter update step. In this regard, we used the estimated sources number of the previous time step as the measurement number of the current time step, and successfully achieved MAM matching. Subsequently, particle filtering is used to approximate the posterior distribution of DOA, where the particle likelihood function can be calculated by the multi-signal classification (MUSIC) spatial spectrum function. In addition, by exponentially weighting the likelihood function, the number of particles in the high likelihood region of the posterior distribution increases, which makes the GLMB filter pruning and merging operations more effective. Simulation results show that the method is better than the probability hypothesis density DOA (PHD-DOA) algorithm in tracking state sources and estimating the number of targets.

Particle Filter Algorithm for DOA Tracking Using Co-Prime Array

In this letter, the problem of direction of arrival (DOA) tracking using co-prime array is studied. A novel particle filter (PF)-based DOA tracking algorithm is proposed to recursively estimate the DOAs based on the measurement model and the state transition model. A modified likelihood function model based on the propagator method (PM) pseudo-spectrum is devised to update the involved particles under lower SNR or less samples condition. Compared to the existing techniques, such as the spatial smoothing multiple signal classification method (SS-MUSIC), compressive sensing (CS) and conventional likelihood PF (CL-PF) methods, the proposed algorithm can exhibit improved performance in term of estimating accuracy and computational cost. Simulation studies are carried out to show the superiority of the proposed PF algorithm.

Simultaneous Tracking and Separation of Multiple Sources Using Factor Graph Model

In this article, we present an algorithm for direction of arrival (DOA) tracking and separation of multiple speakers with a microphone array using the factor graph statistical model. In our model, the speakers can be located in one of a predefined set of candidate DOAs, and each time-frequency (TF) bin can be associated with a single speaker. Accordingly, by attributing a statistical model to both the DOAs and the associations, as well as to the microphone array observations given these variables, we show that the conditional probability of these variables given the microphone array observations can be modeled as a factor graph. Using the loopy belief propagation (LBP) algorithm, we derive a novel inference scheme which simultaneously estimates both the DOAs and the associations. These estimates are used in turn for separating the sources, by directing a beamformer towards the estimated DOAs, and then applying a TF masking according to the estimated associations. A comprehensive experimental study demonstrates the benefits of the proposed algorithm in both simulated data and real-life measurements recorded in our laboratory.

DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment.

Aiming at the problem of multiple-source direction of arrival (DOA) tracking in impulse noise, this paper models the impulse noise by using the symmetric α stable (SαS) distribution, and proposes a DOA tracking algorithm based on the Unscented Transform Multi-target Multi-Bernoulli (UT-MeMBer) filter framework. In order to overcome the problem of particle decay in particle filtering, UT is adopted to select a group of sigma points with different weights to make them close to the posterior probability density of the state. Since the α stable distribution does not have finite covariance, the Fractional Lower Order Moment (FLOM) matrix of the received array data is employed to replace the covariance matrix to formulate a MUSIC spatial spectra in the MeMBer filter. Further exponential weighting is used to enhance the weight of particles at high likelihood area and obtain a better resampling. Compared with the PASTD algorithm and the MeMBer DOA filter algorithm, the simulation results show that the proposed algorithm can more effectively solve the issue that the DOA and number of target are time-varying. In addition, we present the Sequential Monte Carlo (SMC) implementation of the UT-MeMBer algorithm.

Multipath arrival tracking for marine vehicles utilizing pattern recognition

In recent years, interest in the Arctic Region has been steadily growing as it has become more accessible due to continued ice recession. Extreme temperatures and significant ice cover have created a unique and challenging acoustic environment. At increased distances, individual ray path data become inconsistent due to improper ray path identification and fading. Through the use of pattern recognition, a multipath arrival tracking algorithm was developed to utilize the unique characteristics associated with each individual ray path for long range tracking purposes. This tracking algorithm analyzes the amplitude and arrival time patterns amongst all individual ray paths in order to accurately identify each ray path as scattering and fading occurs, thereby increasing range-tracking capabilities. This becomes especially useful in the Arctic Region as contacts of interest can be tracked regardless of their position above, below, or within the Beaufort Duct—a sound duct from 100 to 200 m depth. Simulations covering the numerous depth combinations of sources and receivers with respect to the Beaufort Duct highlight the effectiveness that is presented by utilizing multipath arrival data. The developed algorithm takes advantage of these unique patterns in order to provide a tracking capability for marine vehicles to employ. [Work supported by ONR.]

Epidemiology of patients presenting to a pediatric emergency department in Karachi, Pakistan

BackgroundThere is little data describing pediatric emergencies in resource-poor countries, such as Pakistan. We studied the demographics, management, and outcomes of patients presenting to the highest-volume, public, pediatric emergency department (ED) in Karachi, Pakistan.MethodsIn this prospective, observational study, we approached all patients presenting to the 50-bed ED during 28 12-h study periods over four consecutive weeks (July 2013). Participants’ chief complaints and medical care were documented. Patients were followed-up at 48-h and 14-days via telephone.ResultsOf 3115 participants, 1846 were triaged to the outpatient department and 1269 to the ED. Patients triaged to the ED had a median age of 2.0 years (IQR 0.5–4.0); 30% were neonates (< 28 days). Top chief complaints were fever (45.5%), diarrhea/vomiting (32.3%), respiratory (23.1%), abdominal (7.5%), and otolaryngological problems (5.8%). Temperature, pulse and respiratory rate, and blood glucose were documented for 66, 42, and 1.5% of patients, respectively. Interventions included medications (92%), IV fluids (83%), oxygen (35%), and advanced airway management (5%). Forty-five percent of patients were admitted; 11 % left against medical advice. Outcome data was available at time of ED disposition, 48-h, and 14 days for 83, 62, and 54% of patients, respectively. Of participants followed-up, 4.3% died in the ED, 11.5% within 48 h, and 19.6% within 14 days.ConclusionsThis first epidemiological study at Pakistan’s largest pediatric ED reveals dramatically high mortality, particularly among neonates. Future research in developing countries should focus on characterizing reasons for high mortality through pre-ED arrival tracking, ED care quality assessment, and post-ED follow-up.

Multi-phase arrival tracking using tetrahedral cells within a 3D layered titled transversely isotropic anisotropic model involving undulating topography and irregular interfaces

To overcome a major problem in current ray tracing methods, which are only capable of tracing first arrivals, and occasionally primary reflections (or mode conversions) in regular cell models, we extend in this paper the multistage triangular shortest-path method (SPM) into 3D titled transversely isotropic (TTI) anisotropic media. The proposed method is capable of tracking multi-phase arrivals composed of any kind of combinations of transmissions, mode conversions and reflections. In model parameterization, five elastic parameters, plus two angles defining the titled axis of symmetry of TTI media are sampled at the primary nodes of the tetrahedral cell, and velocity value at secondary node positions are linked by a tri-linear velocity interpolation function to the primary node velocity value of that of a tetrahedral cell, from which the group velocities of the three wave modes (qP, qSV and qSH) are computed. The multistage triangular SPM is used to track multi-phase arrivals. The uniform anisotropic test indicates that the numerical solution agrees well with the analytic solution, thus verifying the accuracy of the methodology. Several simulations and comparison results for heterogeneous models show that the proposed algorithm is able to efficiently and accurately approximate undulating surface topography and irregular subsurface velocity discontinuities. It is suitable for any combination of multi-phase arrival tracking in arbitrary tilt angle TTI media and can accommodate any magnitude of anisotropy.

Simultaneous travel time tomography for updating both velocity and reflector geometry in triangular/tetrahedral cell model

To conduct forward and simultaneous inversion in a complex geological model, including an irregular topography (or irregular reflector or velocity anomaly), we in this paper combined our previous multiphase arrival tracking method (referred as triangular shortest-path method, TSPM) in triangular (2D) or tetrahedral (3D) cell model and a linearized inversion solver (referred to as damped minimum norms and constrained least squares problem solved using the conjugate gradient method, DMNCLS-CG) to formulate a simultaneous travel time inversion method for updating both velocity and reflector geometry by using multiphase arrival times. In the triangular/tetrahedral cells, we deduced the partial derivative of velocity variation with respective to the depth change of reflector. The numerical simulation results show that the computational accuracy can be tuned to a high precision in forward modeling and the irregular velocity anomaly and reflector geometry can be accurately captured in the simultaneous inversion, because the triangular/tetrahedral cell can be easily used to stitch the irregular topography or subsurface interface.

Direction of arrival tracking for signals with known waveforms based on block least squares techniques

In this paper, we address the problem of tracking DOA of multiple moving targets with known signal source waveforms and unknown gains in the presence of Gaussian noise using a nonuniform linear array. Herein, we make use of the fact that the output of each sensor can be described as a linear regression model whose coefficients each contain a pair of DOA and gain information corresponding to one target. These coefficients are determined by solving a linear least squares (LS) problem and then updated recursively based on a block QR decomposition recursive least squares (QRD-RLS) technique or a block regularized LS technique. Since the coefficients from different sensors have the same amplitude but variable phase information for the same signal, along with simple algebraic manipulations the well-known generalized least squares (GLS) are used to obtain an asymptotically-optimal DOA estimate without requiring a search over a large region of the parameter space. Computer simulations show that the proposed DOA tracking techniques when applied to a sparse antenna array can provide a better tracking performance than some of the existing methods do.

Analysis on the TOA Tracking With DVB-T Signals for Positioning

The digital television signal is a promising signal of opportunity for ubiquitous wireless positioning. This letter analyzes the time of arrival (TOA) tracking errors of the European standard digital video broadcasting-terrestrial (DVB-T) signals in the context of positioning. The Cramer–Rao lower bound on the unsmoothed TOA estimation is derived and an information-theoretic lower bound (ITLB) is further computed on the smoothed TOA tracking errors in delay locked loop (DLL). Theoretical analysis shows that, the increase of the bandwidth of the equivalent loop filter will lead to larger DLL tracking errors. Despite different bandwidth of the loop filters, the tracking errors derived in theory are asymptotic to ITLB with the increase of signal-noise ratio. Comparatively, the theoretical results of the DLL tracking errors are slightly larger than the ITLB, which is due to the inequality and nonlinear approximation used in the analysis. Theoretical analysis has also verified the effectiveness of the DVB-T signals to be able to achieve high accuracy in positioning.