Robust AOA-Based Source Localization in Correlated Measurement Noise via Nonconvex Sparse Optimization

In order to analyse synthetic aperture radar (SAR) images of the sea surface, ship wake detection is essential for extracting information on the wake generating vessels. One possibility is to assume a linear model for wakes, in which case detection approaches are based on transforms such as Radon and Hough. These express the bright (dark) lines as peak (trough) points in the transform domain. In this paper, ship wake detection is posed as an inverse problem, which the associated cost function including a sparsity enforcing penalty, i.e. the generalized minimax concave (GMC) function. Despite being a non-convex regularizer, the GMC penalty enforces the overall cost function to be convex. The proposed solution is based on a Bayesian formulation, whereby the point estimates are recovered using maximum a posteriori (MAP) estimation. To quantify the performance of the proposed method, various types of SAR images are used, corresponding to TerraSAR-X, COSMO-SkyMed, Sentinel-1, and ALOS2. The performance of various priors in solving the proposed inverse problem is first studied by investigating the GMC along with the L1, Lp, nuclear and total variation (TV) norms. We show that the GMC achieves the best results and we subsequently study the merits of the corresponding method in comparison to two state-of-the-art approaches for ship wake detection. The results show that our proposed technique offers the best performance by achieving 80% success rate.

Sparse reconstruction for blade tip timing signal using generalized minimax-concave penalty

Owing to the multipath effect, the source localization in shallow water has been an area of active interest. However, most methods for source localization in shallow water are sensitive to the assumed model of the underwater environment and have poor robustness against the underwater channel uncertainty, which limit their further application in practical engineering. In this paper, a new method of source localization in shallow water, based on vector optimization concept, is described, which is highly robust against environmental factors affecting the localization, such as the channel depth, the bottom reflection coefficients, and so on. Through constructing the uncertainty set of the source vector errors and extracting the multi-path sound rays from the sea surface and bottom, the proposed method can accurately localize one or more sources in shallow water dominated by multipath propagation. It turns out that the natural formulation of our approach involves minimization of two quadratic functions subject to infinitely many nonconvex quadratic constraints. It shows that this problem (originally intractable) can be reformulated in a convex form as the so-called second-order cone program (SOCP) and solved efficiently by using the well-established interior point method, such as the software tool, SeDuMi. Computer simulations show better performance of the proposed method as compared with existing algorithms and establish a theoretical foundation for the practical engineering application.

Matched-field processing (MFP) achieves underwater source localization by measuring the correlation between the array and replica signals, with traditional MFP being equivalent to estimating the Euclidean distance between the data cross-spectral density matrix (CSDM) and replica matrices. However, in practical applications, random inhomogeneities in the marine environment and inaccurate estimation of CSDM reduce MFP performance. The traditional minimum variance matched-field processor with environmental perturbation constraints perturbs a priori environment parameters to obtain linear constraints and yields the optimal weight vectors as the replica vectors. In this paper, within the framework of information geometry, the geometric properties of CSDMs as semi-positive definite and Hermitian enable CSDMs to be described as points in a Riemannian manifold. Source localization can be achieved by quantifying the similarity between the CSDMs as the geodesic distance between the points on the manifold. This paper introduces a constrained replica CSDM composed of perturbed replica vectors and proposes a robust matched-field processor based on two non-Euclidean distances: the Riemannian distance and the modified Jensen-Shannon distance. Simulations and experimental results demonstrate that the proposed processors are more robust against environmental and statistical mismatches than traditional processors and can also reduce sidelobe level and improve the resolution.

Angle of arrival (AOA) and received signal strength (RSS) measurements have been commonly used in wireless localization due to easy access and simple implementation. In this paper, we investigate source localization using the AOA-only and hybrid RSS/AOA measurements, respectively. In AOA localization, we approximate the angle error using a range-related quantity. Then the optimization problem based on maximum likelihood (ML) is converted to a convex semidefinite programming (SDP) problem. In hybrid AOA/RSS localization, the ML estimator is decomposed into an RSS part and an AOA part. The AOA part follows a similar procedure as in the AOA localization. Taylor series expansion and relaxation are applied in optimizing the RSS part. These two parts are closely related through the range. The proposed methods avoid the nonconvexity in the original ML estimators for both AOA-only and hybrid AOA/RSS localization problems. Numerical examples show good performance of the proposed methods in both AOA and hybrid AOA/RSS localizations. They are close to or better than the LS methods in the literature.

Successful localization of sound sources in reverberant enclosures is an important prerequisite for many spatial signal processing algorithms. We investigate the use of a weighted fuzzy <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</b> -means cluster algorithm for robust source localization using location cues extracted from a microphone array. In order to increase the algorithm's robustness against sound reflections, we incorporate observation weights to emphasize reliable cues over unreliable ones. The weights are computed from local feature statistics around sound onsets because it is known that these regions are least affected by reverberation. Experimental results illustrate the superiority of the method when compared with standard fuzzy clustering. The proposed algorithm successfully located two speech sources for a range of angular separations in room environments with reverberation times of up to 600 ms.

The localization of outdoor acoustic sources has attracted attention in wireless sensor networks. In this paper, the steered response power (SRP) localization of band-pass signal associated with steering time delay uncertainty and coarser spatial grids is considered. We propose a modified SRP-based source localization method for enhancing the localization robustness in outdoor scenarios. In particular, we derive a sufficient condition dependent on the generalized cross-correlation (GCC) waveform function for robust on-grid source localization and show that the SRP function with GCCs satisfying this condition can suppress the disturbances induced by the grid distance and the uncertain steering time delays. Then a GCC refinement procedure for band-pass GCCs is designed, which uses complex wavelet functions in multiple sub-bands to filter the GCCs and averages the envelopes of the filtered GCCs as the equivalent GCC to match the sufficient condition. Simulation results and field experiments demonstrate the excellent performance of the proposed method against the existing SRP-based methods.

Acoustic source localization has been an hot research topic with widespread applications in many fields. In the noisy environment or when the reverberation is considerable, the source localization problem becomes challenging and many existing algorithms deteriorate. The paper proposes a robust algorithm which combines the RANdom SAmple Consensus (RANSAC) algorithm, and the Generalized Cross-Correlation (GCC) based Time Difference of Arrival (TDOA). Experiments in real world data show that the proposed algorithm has significantly better performance than the traditional algorithm.

Both convex and non-convex penalties have been widely proposed to tackle the sparse index tracking problem. Owing to their good property of generating sparse solutions, penalties based on the least absolute shrinkage and selection operator (LASSO) and its variations are often suggested in the stream of convex penalties. However, the LASSO-type penalty is often shown to have poor out-of-sample performance, due to the relatively large biases introduced in the estimates of tracking portfolio weights by shrinking the parameter estimates toward to zero. On the other hand, non-convex penalties could be used to improve the bias issue of LASSO-type penalty. However, the resulting problem is non-convex optimization and thus is computationally intensive, especially in high-dimensional settings. Aimed at ameliorating bias introduced by LASSO-type penalty while preserving computational efficiency, this paper proposes a multi-step convex optimization approach based on the multi-step weighted LASSO (MSW-LASSO) for sparse index tracking. Empirical results show that the proposed method can achieve smaller out-of-sample tracking errors than those based on LASSO-type penalties and have performance competitive to those based on non-convex penalties.

Efficient and robust sound source recognition and localization is one of the basic techniques for humanoid robots in terms of reaction to environments. Due to the fixed sensor arrays and limited computation resources in humanoid robots, there comes challenge for sound source recognition and localization. This article proposes a sound source recognition and localization framework to realize real-time and precise sound source recognition and localization system using humanoid robots’ sensor arrays. The type of the audio is recognized according to the cross-correlation function. And steered response power-phase transform function in discrete angle space is used to search the sound source direction. The sound source recognition and localization framework presents a new multi-robots collaboration system to get the precise three-dimensional sound source position and introduces a distance weighting revision way to optimize the localization performance. Additionally, the experiment results carried out on humanoid robot NAO demonstrate that the proposed approaches can recognize and localize the sound source efficiently and robustly.

A novel angle-of-arrival (AoA) estimation method for both azimuth and elevation based on Bayesian inference and statistical state transition probabilities is presented for the dynamic indoor terahertz (THz) channel. A precise AoA estimation is crucial for the deployment of a directive antenna, which can compensate for the high path loss and reduce the intersymbol interference. In many application scenarios, the user equipment is moved by the user during the data transmission, and the AoA is not constant. The novel algorithm exploits the fact that the AoA movement can be represented as a Markov process and that the Bayesian inference can be used to combine the likelihood and a priori information to provide a more precise estimate than using the likelihood alone. An indoor human movement model is developed to generate the realistic application scenario and obtain the statistical transition probabilities. The forward–backward algorithm is implemented to carry out the Bayesian inference. The algorithm performance is illustrated using the channel models generated by a ray launching simulator. The background log-likelihood is suggested to adapt the algorithm to the instant channel state change in a multipath environment.

As an impact source localization technique, Lamb waves are commonly used to detect low velocity impacts in composite structures. However, the performance of Lamb waves is susceptible to vibration conditions. In this paper, vibration effects on Lamb waves are explored, and an adaptive-filtering enhanced multiple signal classification (AF-MUSIC) method is proposed to generate a relative robust impact source localization method. The response sensor array signals are contaminated by noises, which makes the traditional multiple signal classification (MUSIC) method unable to localize impact sources. In present method, the adaptive-filtering is utilized to reduce vibration induced noises of array data, then the impact position represented by the angle and distance in polar coordinates are finally estimated by scanning the whole monitoring area using MUSIC. The experimental results show that the present method can relatively robust localizes impact sources effectively, especially for the vibration conditions.

For shallow underwater acoustic sources in deep-sea environments, the received signals in the direct wave zone exhibit distinct multipath arrival structures during large-depth reception, which can be exploited for passive source localization. This paper presents a method for estimating source depth and range through joint matching of arrival angles and time-delay differences between the direct wave and the first-order sea-surface reflected wave. The arrival angles are obtained from deep-sea particle velocity fields based on ray theory, while the time-delay differences are extracted from the autocorrelation function of the received signals. Simulation results show that both arrival angles and time-delay differences can be accurately estimated simultaneously at signal-to-noise ratios (SNRs) above 10 dB, allowing high-precision passive source localization with the proposed method. Under fixed source-receiver geometry, seabed topography and sea-surface roughness significantly alter acoustic propagation paths. Seabed variations affect paths of both direct and bottom-interacted waves, and sea-surface roughness specifically distorts the path of the first-order sea-surface reflected wave, thereby reducing the accuracy of time delay difference estimation in matched field processing. Validated using data from a 2020 deep-sea acoustic propagation experiment in the Western Pacific—where a single vector hydrophone captured signals from a towed source—the method effectively estimated the depth and range of shallow underwater acoustic sources within a 15 km range, with mean relative errors of 5.5% in depth and 6.2% in range. Compared to conventional methods that use only arrival angles or time-delay differences, this joint-matching strategy offers an efficient and robust solution for passive localization of underwater acoustic sources in the deep-sea direct wave zone.

Global navigation satellite system (GNSS) has been widely used in many fields due to their low cost and high positioning accuracy. Because of the open frequency of navigation signals, the low power of navigation signals, and the growing reliance of many modern wireless systems on satellite-based navigation, GNSS performance may be easily affected by interference signals. Monitoring and troubleshooting of interference sources are important means to guarantee the normal use of satellite navigation applications and are an important part of GNSS operation in complex electromagnetic environments; however, traditional angle of arrival (AOA) algorithms cannot efficiently operate with coherent signals, so a decoherence-based orientation scheme is proposed to optimize the AOA algorithm. Furthermore, a joint AOA and time difference of arrival (TDOA) interference localization algorithm is proposed for problems such as the lack of accuracy in a single interference source localization algorithm. Numerical simulation results show that decoherence-based AOA localization can be well applied to various interference signals, and the accuracy of the joint AOA and TDOA interference localization algorithm is higher than that of single-method interference localization. In addition, the physical verification further verifies the usability and reliability of the GNSS interference source positioning algorithm proposed in this paper.

Convex sparse regularization techniques such as l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -regularization enjoy robustness to random initialization and computational efficiency, but often underestimate the true solution. To mitigate the estimation bias, the generalized minimax concave (GMC) penalty (Selesnick 2017) and its linearly involved extension LiGME (Abe et al. 2020) have been proposed recently to yield improved convex sparse regularization. However, empirical results indicate that existing proximal splitting algorithms for GMC/LiGME do not necessarily achieve the convergence speed desired in some real-world applications (e.g., stream data processing).To resolve this situation, we propose a fast and well scalable algorithm (termed DC-LiGME) based on DC programming, which allows us to solve the difficult original problem via solution to a sequence of simpler subproblems. We establish convergence guarantee of the DC-LiGME algorithm and present an intuitive explanation of its fast convergence. Numerical experiments demonstrate superior convergence speed of the DC-LiGME algorithm, and suggest that mere two iterations of the algorithm suffice to stably produce satisfactory solutions (at the cost of solving three standard convex regularization models), which is a favourable feature in practice.