Principle of 3-D Passive Localization Based on Vertical Linear Array and Precise Analysis

The scale of vertical linear array (VLA) used in deep-sea is usually several thousand meters. The accurate estimation of its array is an important prerequisite for the application of VLA signal processing technology. In this paper, a deep-sea vertical array measurement method using depth sensor and azimuth sensor is proposed. Firstly, a number of depth sensors and azimuth sensors set on different depth nodes are used to obtain the tilt angle and tilt azimuth angle of deep-sea vertical array at different depth nodes. Then, through polynomial curve fitting method, the inclination angle and azimuth angle of deep-sea vertical array at different depths are recursively processed. Finally, the three-dimensional coordinate position of the deep-sea VLA is calculated, and the three-dimensional formation of the deep-sea vertical array is obtained. The experimental data processing results show that the VLA shape in a 4000-meter sea area of South China Sea may be controlled by the northeast-southwest sea current and changes at any time in a 12-hour tidal cycle, and the relative variation distance of the top of the VLA can reach 500 m at most.

Estimation of modal wavenumbers is important for inference of geoacoustic properties and data-driven matched field processing in shallow water waveguides. This paper introduces a deep neural network called combining physical informed neural network (CPINN) for modal wavenumber estimation using a vertical linear array (VLA). Note that the sound field recorded by a VLA can be expressed as a linear superposition of finite modal depth functions, and the differential equations satisfied by the modal depth functions are related to the corresponding modal wavenumbers. CPINN can estimate the modal wavenumbers by introducing the proxies of the modal depth functions and constraining them to satisfy the corresponding differential equations. The performance of the CPINN is evaluated by simulated data in a noisy shallow water environment. Numerical results show that, when compared with the previous methods, CPINN does not need to know the exact horizontal distance between the sound source and the VLA. Moreover, CPINN can estimate the modal wavenumbers at the VLA position in the case where the range segment traversed by the source, i.e., the aperture in the range direction, is smaller than the maximum modal cycle distance and in a range-dependent waveguide.

Firstly, the elementary principle of passive estimation of target depth is studied, which is supported by underwater acoustic detection system of vertical linear array and is conducted in deep sea. Then, error sources of passive estimation of target depth are analysed theoretically from points of view of random error and deployment deviation of vertical linear array, and correlative formulae are deduced. After simulation experiments, it is concluded that how these error sources impact accuracy of passive estimation of target depth, which is the basis of further research for passive estimation of target depth and improvement of accuracy of passive estimation of target depth.

In shallow water, modal wavenumbers are useful for many applications. This paper presents an approach for wavenumber estimation of range-independent waveguide based on pressure and its finite difference (FD) data matrices built on the vertical linear array (VLA) data. The core of this method is that wavenumber information is expressed in the singular value decomposition (SVD) of the FD data matrix. Therefore, wavenumbers can be estimated directly by combining the SVDs of the pressure data matrix and FD data matrix. The method is validated on simulated data. The dependence of the proposed method on relevant parameters is discussed.

The fundamental and practical problem of passive localization in range and depth, of an acoustic underwater source is addressed, with application to an at-sea experiment. We propose and try a new matching method based on a metric called as Hausdorff distance as a cost-function to be minimized, in order to perform the localization inversion. The data set analyzed here was collected during the DGA campaign ALMA 2015, which took place in a shallow water environment of the southern coast of France. Acoustic data were measured over a 10m-high vertical linear array (VLA), composed of 64 hydrophones. The 2-D localization, in range and depth, is performed by matching the patterns of time difference of arrival (TDOA), between respectively observed and modeled sequences. Several variants of the Hausdorff Distance are applied, firstly separately in each single hydrophone, and then combined in order to improve the localization accuracy, reducing the ambiguity either is depth and in range. The performance is evaluated in terms of the localization accuracy of the proposed method, in a context of passive localization with a cooperative system considering a motionless target. Very satisfactory performance and accuracy are obtained.

In view of exponential growth in data traffic demand, the wireless communications industry has aimed to increase the capacity of existing networks by 1000 times over the next 20 years. A combination of extreme cell densification, more bandwidth, and higher spectral efficiency is needed to support the data traffic requirements for fifth generation (5G) cellular communications. In this research, the potential improvements achieved by using three major 5G enabling technologies (i.e., small cells, millimeter-wave spectrum, and massive MIMO) in rural and urban environments are investigated. This work develops SPM and KA-based ray models to investigate the impact of geometrical parameters on terrain-based multiuser MIMO channel characteristic. Moreover, a new directional 3D channel model is developed for urban millimeter-wave (mmW) small cells. Path-loss, spatial correlation, coverage distance, and coherence length are studied in urban areas. Exploiting physical optics (PO) and geometric optics (GO) solutions, closed form expressions are derived for spatial correlation. Achievable spatial diversity is evaluated using horizontal and vertical linear arrays as well as planar 2D arrays. In another study, a versatile near-ground field prediction model is proposed to facilitate accurate wireless sensor network (WSN) simulations. Monte Carlo simulations are used to investigate the effects of antenna height, frequency of operation, polarization, and terrain dielectric and roughness properties on WSNs performance.

This paper describes that in shallow water the scattered wave from the target is detected by converging the sound wave to the target using the phase conjugate wave. The phase conjugate wave radiated from the vertical linear transducer array (VLA) is converged to the sound source position. It was confirmed that the phase conjugate wave converged at the sound source position by the experiment. The sound source is replaced with a pseudo sound source. It is assumed that the sound wave is transmitted from a pseudo sound source, and obtains the wave form in the element of VLA by the calculation. The calculated waveform is excuted time reversal processing and it reradiated from VLA. And it was confirmed that the phase conjugate wave converged at the position of the pseudo sound source by the experiment. Consequently, it was confirmed to move the focus of the phase conjugate wave without a real sound source, and to be able to detect the target.

The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV), which reflects activities of the autonomic nervous system (ANS) and has been used for various health monitoring services. Accurate R wave detection is crucial for success in HRV-based health monitoring services; however, ECG artifacts often cause missing R waves and deteriorate the accuracy of HRV analysis. The present work proposes a new missing RRI interpolation technique based on Just-In-Time (JIT) modeling. In the JIT modeling framework, a local regression model is built by weighing samples stored in the database according to the distance from a query and output is estimated only when an estimate is requested. The proposed method builds a local model and estimates missing RRI only when an RRI detection error is detected. Locally weighted partial least squares (LWPLS) is adopted for local model construction. The proposed method is referred to as LWPLS-based RRI interpolation (LWPLS-RI). The performance of the proposed LWPLS-RI was evaluated through its application to RRI data with artificial missing RRIs. We used the MIT-BIH Normal Sinus Rhythm Database for nominal RRI dataset construction. Missing RRIs were artificially introduced and they were interpolated by the proposed LWPLS-RI. In addition, MEAN that replaces the missing RRI by a mean of the past RRI data was compared as a conventional method. The result showed that the proposed LWPLS-RI improved root mean squared error (RMSE) of RRI by about 70% in comparison with MEAN. In addition, the proposed method realized precise HRV analysis. The proposed method will contribute to the realization of precise HRV-based health monitoring services.

Underwater passive location technology using Dummy Time Reversal Mirror (DTRM) is a coherent source spatially and temporally refocused method, which also can positioning object without prior information about ocean itself. The feasibility of DTRM with single sensor has been validated by sea trial results. The vertical array with two sensors is used to the DTRM in the paper in order to obtain higher location precision and better robustness. The main principle and implementation of DTRM based on vertical array (ADTRM) are studied and simulations are carried out, and the results demonstrate the practicability of this technology. Pool experiments and sea trial data analyses show that strong out-of-focus in the original position of the coherent source can be get by ADTRM when the environment mismatch and environmental noise are considered. The results also provide that this technology have good robustness and better anti-isotropic interfere ability than DTRM based on single sensor.

High sensitive pyroelectric infrared (IR) sensors have been fabricated by using c-axis oriented La-modified PbTiO3 (Pb1–xLaxTi1−x/4O3, PLT) thin films. The PLT thin films were deposited on (100)-cleaved MgO single crystal substrates by intermittent rf-magnetron sputtering method. The PLT thin films have high figures of merit for IR sensor without a poling treatment. High performance pyroelectric IR sensors (single element type and linear array type) were fabricated by using PLT (x=0·1, γ=5·5x10−8 C/cm2K, ϵr=200) thin films. The sensors have remarkably high D* of 3–6x108 cmHz1/2/W and very fast response. A new compact IR sensing system using the linear array sensor (8 elements) has been developed for a new type of room air-conditioner. This system can measure thermal distribution (8x64) by horizontal scanning of the vertical linear array. Image processing with neural network concept makes possible high-accuracy using a few data from the sensor elements. This sensing system provides ‘‘smart aircond...

To maintain a high-quality machining process, it is necessary to develop an online cutting temperature prediction model accurately. However, using the conventional cutting temperature modeling methods, it is difficult to capture the continuous and multivariate time-variant characteristic of cutting temperature. Recently, local learning and switching dynamics have been employed to identify the time-variant characteristic. However, it requires quantitative and precise analysis with certain switching patterns, which are hard to realize in the machining process. In this article, a self-adaptive local learning method integrated with dynamic convolutional neural networks (DCNN) is proposed. The moving window strategy and t-test analysis of performance maps are first to give the local domains. Then, a DCNN is designed to capture spatial cross-correlations and temporal autocorrelations in each local domain. Finally, the local models are ensemble using Bayesian ensemble learning. The effectiveness of the proposed method is demonstrated through different process information and tool conditions.

Formalism from Young's two-pinhole experiment is used to study the lasing modes of coupled 2×1 photonic crystal vertical cavity laser arrays. The eigenmodes of the system as well as the coherence change with injection current.

In inter-satellite laser communication systems, angular deviations between the boresights of terminals exert an impact on system performance, thereby imposing higher requirements on performance parameters such as the angle measurement accuracy and range of the system. Conventional coherent angle measurement systems feature a fixed baseline, which results in insufficient dynamic adjustment capability for accuracy and range. To address the aforementioned issues, this paper proposes a variable-baseline coherent angle measurement method based on nutation scanning. By altering the baseline length through a nutation scanning strategy, this method achieves dynamic adjustment of angle measurement accuracy and range, enhancing the adaptability of the angle measurement system in different scenarios. For long-distance communication, a small nutation scanning angle (short baseline) is used to adapt to significant satellite relative position changes via a large angle measurement range, while a large nutation scanning angle (long baseline) is adopted for short-distance communication to ensure stable tracking with high angle measurement accuracy. Firstly, a mathematical model of the system is established, and the relationships between the nutation scanning angle, angle measurement accuracy, and angle measurement range are derived. The Monte Carlo method is employed to construct a variable-baseline angle measurement simulation system for data verification, and a tabletop experimental system is built for performance analysis. Experimental results show that when the nutation scanning angle γ increases from 10 μrad to 100 μrad, the baseline length increases from 0.05 μm to 0.5 μm, the angle measurement accuracy improves from 56.9 μrad to 6.35 μrad, and the angle measurement range narrows from 880 μrad to 560 μrad. These results are consistent with the trend of theoretical derivation and correspond to the simulation results. This study verifies the feasibility of dynamically regulating angle measurement accuracy and range via the nutation scanning angle and provides a reference for the design of space laser communication systems.

The output of a vertical linear array is used to infer about the parameters of the normal mode model that describes acoustic propagation in a shallow water. Existing subspace algorithms perform singular vector decomposition of the array data matrix to estimate the sampled model functions. Estimates are exact only if the sensing array is totally covering the water column. We design a new subspace algorithm free from this very restrictive requirement. We use two short hydrophone arrays and activate a monochromatic source at different depths. Estimates of both the modal functions and the wave numbers are obtained in a fully automatic and search-free manner. The algorithm can be qualified as truly high resolution in the sense that, while using short sensing arrays, estimation error becomes arbitrarily low if observation noise is arbitrarily low. This method compares advantageously to existing subspace techniques, as well as transform-domain techniques that require impulsive sources, among other constraints. With two (eigen and singular) vector decompositions, the proposed technique has the complexity of a regular subspace algorithm.

Most source enumeration algorithms assume a white noise background; however, in many underwater applications, the background noise is colored. Various researchers have investigated the problem of source enumeration when the background noise is colored. The standard approach is to apply a whitening filter before the signal enumeration algorithm. Most of these algorithms perform poorly when the number of noise snapshots used to estimate the whitening filter is small. Nadakuditi and Silverstein [IEEE J. Sel. Topics Signal Process., 2010] developed an algorithm for source enumeration using random matrix theory that provides the fundamental SNR limits for snapshot-deficient scenarios with arbitrary noise. Nadakuditi and Silverstein's analysis focuses on the performance when the number of signal plus noise snapshots varies while the number of snapshots used for the whitening filter stays constant. This talk analyzes the performance of this algorithm when number of snapshots available to estimate the whitening filter varies from N to 10 N. Simulations using two different colored noise models for a large vertical linear array located in the deep ocean will be presented.