Arrival Estimation Research Articles

Self correction technique for amplitude and phase errors of underwater acoustic arrays based on variable decibel Bayesian inference

Abstract Direction of arrival (DOA) estimation of underwater targets is an important research direction in the field of underwater acoustic array signal processing. Due to factors such as assembly errors and component aging, amplitude and phase errors between each channel of the array are common, resulting in distortion of the array flow pattern matrix. At the same time, the non-uniform distribution of noise intensity at the positions of each array element further increases the difficulty of DOA for underwater targets. Based on the characteristics of sparse spatial distribution of underwater targets, this paper uses the flexibility of hyperparametric modeling to construct an array observation signal graph model. On this basis, the effects of amplitude and phase errors and non-uniform noise on the array model are added, and a comprehensive observation model with clear distribution types of amplitude and phase errors, signals, and noise is established. The variational Bayesian inference theory is used to obtain closed solutions of various variables and parameters, so as to achieve joint estimation of target azimuth and error parameters. Simulation results and sea trial data processing results show that the algorithm can achieve amplitude and phase error self correction while completing DOA estimation in the absence of prior information on the number of sources.

A Multi-Objective Direction of Arrival Estimation Technique MinimizingEnergy Consumption in Wireless Sensor Network

Wireless Sensor Network (WSN) demand for secure communication is challenging due to its limited resource constraints. Hence, this research developed an effective distributed DOA estimation model through Direction Optimization Integrated Ranking Voting (DOIRV) to improve the performance of WSNs. The developed model comprises the multi-objective optimization model with the Whale technique for the WSN. The constructed DOIRV model uses objective function estimation with branch-and-bound for effective data transmission. The developed DOIRV model uses the Artificial Intelligence-based machine learning model for the DOA routing path computation and estimation. Finally, the DOIRV estimation is evaluated for the performance analysis with the consideration of the Cramer-Rao analysis for the data transmission in the WSN model. Simulation results stated that the proposed weighted technique significantly improves the network performance such as packet delivery ratio, throughput, and network delay. Analysis of the technique expressed that the proposed DOIRV technique exhibits effective performance rather than the conventional technique in terms of network delay, throughput, and packet delivery ratio. The comparative analysis stated that the performance of the proposed DOIRV model is ~13% higher than the conventional technique in terms of packet delivery ratio and ~18% higher for the throughput.

Augmented correlation matrix reconstruction for direction of arrival estimation using dual-spread array

Due to the large degrees of freedom (DOF) by generating difference coarray, nonuniform arrays have great potential in direction of arrival (DOA) estimation. However, some existing nonuniform arrays (e.g., (sparse) nested arrays) still have comparatively strong mutual coupling in physical array domain, whereas the others (e.g., coprime arrays) have holes or only used part available in difference coarray domain, for the performance loss of DOA estimation. To address these issues, we propose two kinds of dual-spread arrays which achieve more and continuous DOF by exploiting correlation matrix reconstruction, rather than the generation of difference coarray. All inter-antenna spacings of the proposed arrays are larger than half-wavelength, resulting in extended aperture and reduced mutual coupling. An ESPRIT-based estimation method is further provided for unique DOA estimation with high-accuracy and low-complexity. The analyses of mutual coupling and computational complexity are provided in detailed. The theoretical analyses and simulation results show the superiority of the proposed arrays and method over the existing techniques.

A TDOA sequence estimation method of underwater sound source based on hidden Markov model

To address the time difference of arrival (TDOA) estimation problem in the passive positioning system with wideband underwater motion sound sources and distributed hydrophones, a hidden Markov model-based (HMM) TDOA sequence estimation method is proposed in this paper. The method estimates the TDOA with multi-frame output of cross-correlated signals received on hydrophones. The transfer equation of the TDOA is established as a first-order hidden Markov process by analyzing the motion characteristics of the moving sound source and delays obtained from different hydrophones. Dynamic assignment of the HMM parameters is proposed to address the inconsistent change rate of the TDOA. We then achieve an HMM expression of the TDOA sequence by fitting the transfer equation and dynamic assignment of parameters into the HMM. Then, the Viterbi algorithm (VA) is applied to distinguish the optimal sequence of the TDOA among ambiguous estimations. To deal with the problem of data loss or unreliable issues caused by interferences, a data prediction algorithm which could produce possible time delays is added to VA to avoid the impact of outliers on the estimation results. By utilizing multi-frame processing, the proposed method reduces the signal-to-noise ratio (SNR) requirement of single frames since it does not require accurate estimations of TDOA for each frame. Moreover, the method adapts to a lower SNR, which has significant advantages in terms of whole sequence estimation compared with common methods. The results from the simulations and lake experiments validated the proposed TDOA sequence estimation method.

High-resolution direction-finding algorithm using Qth−order cumulants tensor: Q−TMUSIC

To resolve more sources than sensors, the 2q−MUSIC algorithm has been developed recently by rearranging the 2qth−order circular cumulants tensor into a suitable size matrix. However, in 2q−MUSIC, the angular resolution and estimation accuracy have not attained their optimum values due to the loss of differential information by matricizing the cumulants tensor. This paper proposes a subspace-based MUSIC-like algorithm, namely Qth−order cumulants-based tensor MUSIC algorithm for the direction of arrival (DOA) estimation of statistically independent non-Gaussian sources by exploiting the Qth−order cumulants tensors, Q∈{3,4,5,⋯}, directly using multilinear algebra tools. The proposed algorithm offers an improved DOA estimation performance in terms of angular resolution and estimation accuracy compared to their equivalent matrix-based Q−MUSIC. Moreover, the computational complexity of the proposed Q-TMUSIC is lesser than that of their equivalent matrix-based Q−MUSIC for Q>4. However, the proposed Q−TMUSIC can identify atmost (M−1) sources using an array of M−sensors than that of (M⌊Q2⌋−1) with their equivalent matrix based Q−MUSIC.

Closed-Form Method for Unified Far-Field and Near-Field Localization Based on TDOA and FDOA Measurements

When the near-field and far-field information of a target is uncertain, it is necessary to choose a suitable localization method. The modified polar representation (MPR) method integrates the two scenarios and achieves a unified localization with direction of arrival (DOA) estimation in the far field and position estimation in the near field. Previous studies have only proposed solutions for stationary environments and have not considered the motion factor. Therefore, this paper proposes a new unified positioning algorithm using multi-sensor time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements without prior target source information. The method represents the position of the target source using MPR and describes the localization problem as a weighted least squares (WLS) problem with two constraints. We first obtain the initial estimates by WLS without considering the constraints and then investigate a two-step error correction method based on the constraints. The first step corrects the initial estimate using the Taylor series expansion technique, and the second step corrects the DOA estimate in the previous step using the direct error compensation technique based on the properties of the second constraint. Simulation experiments show that the method is effective for the unified positioning of moving targets and can achieve the Cramer–Rao lower bound (CRLB).

Readout studies for the HIKE main electromagnetic calorimeter

The High-Intensity Kaon Experiment (HIKE) is a proposed future kaon experiment designed for the CERN SPS accelerator. To cope with a planned increase of beam intensity by a factor of 4 with respect to the current NA62 experiment, the new Main Electromagnetic Calorimeter (MEC) has to provide a similar improvement in time resolution over the current Liquid Krypton calorimeter, down to ≈100ps for a 40GeV photon, while maintaining a comparable energy and spatial resolution. To achieve these goals, the planned detector consists of a shashlik calorimeter paired with high-resolution analog to digital converter for both energy deposit and arrival time estimates, operating at fast sample rates (∼1GHz).This contribution describes a prototype of the readout system using commercial “off-the-shelf” boards. In particular, we focused on feature extraction and tested it using a pulse generator. We included a data readout logic based on the TCP/IP protocol allows for easy deployment in beam tests.

Multi-task recognition of modulation types and arrival directions of underwater acoustic signals based on convolutional neural networks

Abstract Nowadays, underwater acoustic communication reconnaissance technology is of great importance in military, marine science, and resource exploration fields. The fields involve military reconnaissance and intelligence collection, ocean monitoring and security, marine scientific research, and resource exploration. This article mainly focuses on the identification of modulation types and emission angles contained in underwater acoustic signals. Convolutional Neural Networks (CNNs) are used for deep learning to train, validate, and obtain final results on signal data. This network achieves the independent tasks of automatic modulation classification and Direction of Arrival (DOA) estimation of radio signals simultaneously. The model achieves efficient identification and filtering by designing different filter sizes and sizes, as well as different Y-shaped connection structures. It includes one input block and two output blocks, and the two output blocks correspond to the outputs of modulation classification and DOA estimation. At the same time, based on this, the parameters of the network layer are modified to achieve the generalization ability of the neural network. The training and validation data of the network consists of simulation data from underwater acoustic communication emissions and real data from the Songhua Lake experiment in Jilin Province, China, to test the network recognition efficiency.

Sparse reconstruction methods for wideband source localization in spherical harmonics domain

Wideband acoustic source localization is a challenging task especially in the presence of noise. Generally wideband source localization is done by averaging the Direction of Arrival (DOA) estimates obtained over multiple frequencies or narrow subbands by the method of frequency smoothing. Localization of multiple wideband sources which are correlated is even more challenging. In this work acoustic source localization under these challenging conditions is addressed. A sparse reconstruction framework is developed for wideband source localization in the Spherical Harmonics (SH) domain. The proposed framework jointly computes both the azimuth and elevation. In contrast to earlier methods of source localization in the SH domain, this work utilizes the expression for the SH coefficients of the amplitude density of the plane wave, to develop the sparse reconstruction framework. An expression for Cramér Rao Lower Bound (CRLB) on the DOA using this framework is also derived for the wideband sources. This CRLB is shown to easily generalize for narrowband sources as well. The performance of the proposed method is compared with MUltiple Signal Classification in SH domain (MUSIC-SH), Steered Response Power in SH domain (SRP-SH), MUSIC with Direct Path Dominance test in SH domain (MUSIC-DPD-SH) and Pressure based Compressive Sensing in SH domain (PCS-SH). The performance is evaluated for correlated narrowband and wideband sources through simulations, conducting experiments in an anechoic chamber and under reverberant conditions. Using the proposed framework, it is shown that correlated narrowband and wideband sources can be resolved reasonably well when compared to conventional methods of acoustic source localization in SH domain.

A high-resolution DOA estimation via random forest virtual array extension

In sonar detection, underwater recognition, and similar fields, the challenge of using small-scale arrays is frequently encountered. With small-scale arrays, the degrees of freedom (DOFs) and accuracy of direction of arrival (DOA) estimation decrease significantly. Moreover, existing algorithms are less robust in complex environments and typically require substantial computing resources to ensure accurate estimation. To overcome these problems, this paper proposes a high-resolution DOA estimation via Random Forest virtual array extension (RFVAE). The algorithm first trains the random forest (RF) network using real array element data, and thus proposes a virtual covariance reconstruction technique. This technique allows for a substantial increase in the number of virtual array elements. Then, based on the virtual covariance reconstruction technique, a high resolution estimation network technique is proposed, which can increase the accuracy and DOFs of DOA estimation. Experimental results show that the proposed algorithm reduces error by 30% to 80% compared to recent technologies and can provide up to twice as many virtual elements. Additionally, the estimation speed can be increased by 3 to 1000 times, and it has significantly stronger robustness, functioning normally at signal-to-noise ratios 10 dB lower than the conditions under which other algorithms fail.

Single-Snapshot Direction of Arrival Estimation for Vehicle-Mounted Millimeter-Wave Radar via Fast Deterministic Maximum Likelihood Algorithm

As one of the fundamental vehicular perception technologies, millimeter-wave radar’s accuracy in angle measurement affects the decision-making and control of vehicles. In order to enhance the accuracy and efficiency of the Direction of Arrival (DoA) estimation of radar systems, a super-resolution angle measurement strategy based on the Fast Deterministic Maximum Likelihood (FDML) algorithm is proposed in this paper. This strategy sequentially uses Digital Beamforming (DBF) and Deterministic Maximum Likelihood (DML) in the Field of View (FoV) to perform a rough search and precise search, respectively. In a simulation with a signal-to-noise ratio of 20 dB, FDML can accurately determine the target angle in just 16.8 ms, with a positioning error of less than 0.7010. DBF, the Iterative Adaptive Approach (IAA), DML, Fast Iterative Adaptive Approach (FIAA), and FDML are subjected to simulation with two targets, and their performance is compared in this paper. The results demonstrate that under the same angular resolution, FDML reduces computation time by 99.30% and angle measurement error by 87.17% compared with the angular measurement results of two targets. The FDML algorithm significantly improves computational efficiency while ensuring measurement performance. It provides more reliable technical support for autonomous vehicles and lays a solid foundation for the advancement of autonomous driving technology.

Near-field DOA estimation oriented sparse array design via symmetric augmentation of three ULAs

A symmetric sparse array, via symmetric augmentation of three (SAT) uniform linear subarrays, has been designed for direction of arrival (DOA) estimation of uncorrelated near-field or coexisted near-field and far-field narrowband source signals. With an explicit design of the subarray structures and positions, this SAT array 1) involves only two closely spaced antenna pairs (with quarter-wavelength spacing); 2) has a large uniform degree of freedom (uDOF) and difference co-array; 3) can have either even or odd number of antennas; and 4) has closed form expressions for antenna positions, uDOF and weight functions. Simulation results show that the SAT array is superior to the current symmetric sparse arrays in both antenna mutual coupling and consecutive virtual aperture.

Super-resolution diffractive neural network for all-optical direction of arrival estimation beyond diffraction limits

Wireless sensing of the wave propagation direction from radio sources lays the foundation for communication, radar, navigation, etc. However, the existing signal processing paradigm for the direction of arrival estimation requires the radio frequency electronic circuit to demodulate and sample the multichannel baseband signals followed by a complicated computing process, which places the fundamental limit on its sensing speed and energy efficiency. Here, we propose the super-resolution diffractive neural networks (S-DNN) to process electromagnetic (EM) waves directly for the DOA estimation at the speed of light. The multilayer meta-structures of S-DNN generate super-oscillatory angular responses in local angular regions that can perform the all-optical DOA estimation with angular resolutions beyond the diffraction limit. The spatial-temporal multiplexing of passive and reconfigurable S-DNNs is utilized to achieve high-resolution DOA estimation over a wide field of view. The S-DNN is validated for the DOA estimation of multiple radio sources over 5 GHz frequency bandwidth with estimation latency over two to four orders of magnitude lower than the state-of-the-art commercial devices in principle. The results achieve the angular resolution over an order of magnitude, experimentally demonstrated with four times, higher than diffraction-limited resolution. We also apply S-DNN’s edge computing capability, assisted by reconfigurable intelligent surfaces, for extremely low-latency integrated sensing and communication with low power consumption. Our work is a significant step towards utilizing photonic computing processors to facilitate various wireless sensing and communication tasks with advantages in both computing paradigms and performance over electronic computing.

DOA Estimation Based on Virtual Array Aperture Expansion Using Covariance Fitting Criterion

Providing higher precision Direction of Arrival (DOA) estimation has become a hot topic in the field of array signal processing for parameter estimation in recent years. However, when the physical aperture of the actual array is small, its aperture limitation means that even with super-resolution estimation algorithms, the achievable estimation precision is limited. This paper takes a novel approach by constructing an optimization algorithm using the covariance fitting criterion based on the array output’s covariance matrix to fit and obtain the covariance matrix of a large aperture virtual array, thereby providing high-precision angular resolution through virtual aperture expansion. The covariance fitting expansion analysis and discussion are unfolded for both uniform linear arrays (ULAs) and sparse linear arrays (SLAs) under four different scenarios. Theoretical analysis and simulation experiments demonstrate that these methods can enhance the effective performance of angle estimation, especially in low signal-to-noise ratios (SNRs) and at small angular intervals by fitting virtual extended aperture data.

Cyclic Beam Direction of Arrival Estimation Method for Ship Propeller Noise

Cyclic Beam Direction of Arrival Estimation Method for Ship Propeller Noise

DIRECTION OF ARRIVAL ESTIMATION BASED ON SMOOTH MUSIC ALGORITHM COMBINED SPECTRUM SELECTION TECHNIQUES OF NOISE CHARACTERISTICS OF MARINE TARGETS EQUIPPED PROPELLER

This article demonstrates a proposal for a direction of arrival (DOA) estimation algorithm for passive sonar systems based on the combination of the smooth multiple signal classification (Smooth MUSIC) algorithm and frequency bin selection (BS) of the sound of marine propeller-equipped targets, so-called BS-SMUSIC. The harmonics of propeller shaft and blade frequencies are considered useful information for detecting targets and estimating their parameters, including DOA. Low frequency analysis recording (LOFAR) is utilized to implement BS. By analyzing MUSIC-based algorithm together BS, passive DOA estimation algorithms have been significantly improved. Simulation results have been given in both cases: uncorrelated and correlated sources. Outcomes demonstrate that the BS-SMUSIC algorithm has the best performance in comparison to three MUSIC-based algorithms (MUSSIC, Modified MUSIC and Root MUSIC) in all investigation cases: narrow band signals at 5 dB of signal to noise (SNR); wideband signals at -5 dB of SNR. Besides, BS-SMUSIC algorithm also archives the lowest root mean square error (RMSE) in SNR range -5 dB to 20 dB. In the SNR range, the RMSE of BS-SMUSIC algorithm remains stable and is about 0.3 ◦ at -5 dB of SNR. This performance reflects its ability to ensure stable operation, super resolution and high accuracy of BS-SMUSIC algorithm for the passive sonar systems.

Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning.

As one of the most widely used spread spectrum techniques, the frequency-hopping spread spectrum (FHSS) has been widely adopted in both civilian and military secure communications. In this technique, the carrier frequency of the signal hops pseudo-randomly over a large range, compared to the baseband. To capture an FHSS signal, conventional non-cooperative receivers without knowledge of the carrier have to operate at a high sampling rate covering the entire FHSS hopping range, according to the Nyquist sampling theorem. In this paper, we propose an adaptive compressed method for joint carrier and direction of arrival (DOA) estimations of FHSS signals, enabling subsequent non-cooperative processing. The compressed measurement kernels (i.e., non-zero entries in the sensing matrix) have been adaptively designed based on the posterior knowledge of the signal and task-specific information optimization. Moreover, a deep neural network has been designed to ensure the efficiency of the measurement kernel design process. Finally, the signal carrier and DOA are estimated based on the measurement data. Through simulations, the performance of the adaptively designed measurement kernels is proved to be improved over the random measurement kernels. In addition, the proposed method is shown to outperform the compressed methods in the literature.

Distance- and Angle-Based Hybrid Localization Integrated in the IEEE 802.15.4 TSCH Communication Protocol

Accurate localization of devices within Internet of Things (IoT) networks is driven by the emergence of novel applications that require context awareness to improve operational efficiency, resource management, automation, and safety in industry and smart cities. With the Integrated Localization and Communication (ILAC) functionality, IoT devices can simultaneously exchange data and determine their position in space, resulting in maximized resource utilization with reduced deployment and operational costs. Localization capability in challenging scenarios, including harsh environments with complex geometry and obstacles, can be provided with robust, reliable, and energy-efficient communication protocols able to combat impairments caused by interference and multipath, such as the IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol. This paper presents an enhancement of the TSCH protocol that integrates localization functionality along with communication, improving the protocol’s operational capabilities and setting a baseline for monitoring, automation, and interaction within IoT setups in physical environments. A novel approach is proposed to incorporate a hybrid localization by integrating Direction of Arrival (DoA) estimation and Multi-Carrier Phase Difference (MCPD) ranging methods for providing DoA and distance estimates with each transmitted packet. With the proposed enhancement, a single node can determine the location of its neighboring nodes without significantly affecting the reliability of communication and the efficiency of the network. The feasibility and effectiveness of the proposed approach are validated in a real scenario in an office building using low-cost proprietary devices, and the software incorporating the solution is provided. The experimental evaluation results show that a node positioned in the center of the room successfully estimates both the DoA and the distance to each neighboring node. The proposed hybrid localization algorithm demonstrates an accuracy of a few tens of centimeters in a two-dimensional space.

Unsupervised Learning-Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone.

To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health. This study presents an unsupervised learning approach for the automatic detection of first arrival time and estimation of ultrasonic velocity from axial transmission waveforms, which potentially indicates bone quality. The proposed method combines the ReliefF algorithm and fuzzy C-means clustering. It was first validated using an in vitro dataset measured from a Sawbones phantom. It was subsequently applied on in vivo signals collected from 40 infants, comprising 21 males and 19 females. The extracted neonatal ultrasonic velocity was subjected to statistical analysis to explore correlations with the infants' anthropometric features and biochemical indicators. The results of in vivo data analysis revealed significant correlations between the extracted ultrasonic velocity and the neonatal anthropometric measurements and biochemical markers. The velocity of first arrival signals showed good associations with body weight (ρ = 0.583, P value <.001), body length (ρ = 0.583, P value <.001), and gestational age (ρ = 0.557, P value <.001). These findings suggest that fuzzy C-means clustering is highly effective in extracting ultrasonic propagating velocity in bone and reliably applicable in in vivo measurement. This work is a preliminary study that holds promise in advancing the development of a standardized ultrasonic tool for assessing neonatal bone health. Such advancements are crucial in the accurate diagnosis of bone growth disorders.

Direction of arrival (DOA) estimation using switched beam antenna with butler matrix at mm-wave frequency

This paper presents a butler matrix-based microstrip hexagonal patch antenna with a direction of arrival (DOA) estimation approach for mm-wave application. The hexagonal-shaped patch antenna had been designed with a Z-shaped slot with an eight-port butler matrix. The DOA estimation is also done for the switched beam antenna using a novel direction of arrival (DOA) estimation algorithm known as Cramer-Rao lower bound (CRLB). The proposed design leads to significant size reduction and loss minimization. In addition to this, the design had the advantages of being low-cost, lightweight, and small-volume. The entire proposed design provides an operating frequency range of 28 GHz to 39 GHz with a center frequency of 33 GHz. The proposed work makes use of two butler matrices with two 45° phase shifters and 120° phase shifters. The effectiveness of the proposed algorithm and antenna design using the Butler matrix is evaluated for various performance metrics separately. The antenna is designed using Rogers RT/duroid 5880(tm) substrate, and the fabricated prototype is studied. The designed antenna attains high radiation efficiency, and it ranges between 97 and 98 % under both the measures and simulated scenarios under the operating frequency range of 28 GHz to 39 GHz.