BaTiO3-xBi（Ni0.5Zr0.5）O3 powders were synthesized by traditional solid-state reaction method, and then BaTiO3-xBi（Ni0.5Zr0.5）O3 ceramics were prepared by spark plasma sintering. The crystal ...structure, micro-morphology, dielectric and impedance properties of the prepared ceramics were studied. The results show that BaTiO3-xBi（Ni0.5Zr0.5）O3 ceramics have perovskite structure and the crystal structure is pseudo-cubic phase, the grain size is about 0.64 μm, the density of the sample is 5.81 g/cm3, the maximum of the dielectric constant is 7149, and the phase transition temperature shifts to higher temperature with the increase of frequency. At 1 kHz, the slope of the fitting curve between ln（1/ε-1/εm） and ln（T-Tm） of BaTiO3-0.10Bi（Ni0.5Zr0.5）O3 ceramics is 1.61, and Δε/ε25 ℃≤±15% at -41-169 ℃, indicating that the sample has a good temperature stability. In addition, the impedance decreases with the increase of temperature and frequency； when the frequency is 100 Hz at 50 ℃, the resistance is 2.33×106 Ω and the io
For the integrated system of underwater detection and communication, an emitted waveform should satisfy both detection and communication requirements. The signal processing method at the receiving ...end must also accomplish target detection and communication accordingly. This study uses a generalised sinusoidal frequency modulated (GSFM) waveform with a carrier to ensure detection performance. At the same time, the communication information encoding with Gaussian Minimum Shift Keying is modulated to the GSFM signal for communication purposes. Unlike previous work, an improved Blind Source Separation algorithm is utilised at the receiving end, which is better adapted to waveform separation and processing in the underwater time‐varying unknown environment. The analysis of detection probability, peak‐to‐average ratio, and signal processing results show that the proposed waveform and corresponding signal processing scheme can effectively meet the need for integrated underwater detection and communication system.
For the integrated system of underwater detection and communication (ISUDC), an emitted waveform should satisfy both detection and communication requirements. This paper uses a generalised sinusoidal frequency modulated (GSFM) waveform with a carrier to ensure detection performance. At the same time, the communication information encoding with Gaussian Minimum Shift Keying (GMSK) is modulated to the GSFM signal for communication purposes. Unlike previous work, an improved blind source separation (BBS) algorithm is utilised at the receiving end, which is better adapted to waveform separation and processing in the underwater time‐varying unknown environment.
Transmitting orthogonal waveforms are the basis for giving full play to the advantages of MIMO radar imaging technology, but the commonly used waveforms with the same frequency cannot meet the ...orthogonality requirement, resulting in serious coupling noise in traditional imaging methods and affecting the imaging effect. In order to effectively suppress the mutual coupling interference caused by non-orthogonal waveforms, a new non-orthogonal waveform MIMO radar imaging method based on deep learning is proposed in this paper: with the powerful nonlinear fitting ability of deep learning, the mapping relationship between the non-orthogonal waveform MIMO radar echo and ideal target image is automatically learned by constructing a deep imaging network and training on a large number of simulated training data. The learned imaging network can effectively suppress the coupling interference between non-ideal orthogonal waveforms and improve the imaging quality of MIMO radar. Finally, the effectiveness of the proposed method is verified by experiments with point scattering model data and electromagnetic scattering calculation data.
In the integrated system of underwater single-node detection and communication, active self-interference cancellation requires high accuracy of time delay estimation. To solve this problem, this ...Letter proposes a high accuracy delay estimation method based on phase correction. Initially, the normalised frequency correction is calculated according to the phase difference and time delay of the signal. Then, the discrete spectrum is reconstructed, and the phase is obtained by the least square method. Finally, the time delay is estimated by the phase difference between the received signal and the reference signal. This method corrects the phase and frequency errors caused by the Fourier transform and improves the accuracy of time delay estimation. Simulation results confirm the efficacy of the proposed method and achieve the accuracy requirement of the active self-interference cancellation.
We study the problem of energy-efficient target tracking in underwater wireless sensor networks (UWSNs). Since sensors of UWSNs are battery-powered, it is impracticable to replace the batteries when ...exhausted. This means that the battery life affects the lifetime of the whole network. In order to extend the network lifetime, it is worth reducing the energy consumption on the premise of sufficient tracking accuracy. This paper proposes an energy-efficient filter that implements the tradeoff between communication cost and tracking accuracy. Under the distributed fusion framework, local sensors should not send their weak information to the fusion center if their measurement residuals are smaller than the pre-given threshold. In order to guarantee the target tracking accuracy, artificial measurements are generated to compensate for those unsent real measurements. Then, an adaptive scheme is derived to take full advantages of the artificial measurements-based filter in terms of energy-efficiency. Furthermore, a computationally efficient optimal sensor selection scheme is proposed to improve tracking accuracy on the premise of employing the same number of sensors. Simulation demonstrates that our scheme has superior advantages in the tradeoff between communication cost and tracking accuracy. It saves much energy while loosing little tracking accuracy or improves tracking performance with less additional energy cost.
This paper investigates the direction of arrival (DOA) estimation performance in the presence of polarity inconsistency in uniform acoustic vector sensor (AVS) linear array. We analyze the influence ...of polarity bias on beampattern directivity of the AVS array. The analysis results show that the polarity bias leads to asymptotically biased estimation. Then, the analytical expression for the asymptotic bias based on classical beamforming is derived in the presence of polarity error. Moreover, to improve the DOA estimation performance in the presence of polarity inconsistency, a polarity calibration method is proposed. Numerical simulations reveal the effectiveness and superiority of the proposed calibration method when the polarity error satisfies with the uniform distribution and the normal distribution.
Underwater wireless sensor networks (UWSNs) can provide a promising solution to underwater target tracking. Due to the limited computation and bandwidth resources, only a small part of nodes are ...selected to track the target at each interval. How to improve tracking accuracy with a small number of nodes is a key problem. In recent years, a node depth adjustment system has been developed and applied to issues of network deployment and routing protocol. As far as we know, all existing tracking schemes keep underwater nodes static or moving with water flow, and node depth adjustment has not been utilized for underwater target tracking yet. This paper studies node depth adjustment method for target tracking in UWSNs. Firstly, since a Fisher Information Matrix (FIM) can quantify the estimation accuracy, its relation to node depth is derived as a metric. Secondly, we formulate the node depth adjustment as an optimization problem to determine moving depth of activated node, under the constraint of moving range, the value of FIM is used as objective function, which is aimed to be minimized over moving distance of nodes. Thirdly, to efficiently solve the optimization problem, an improved Harmony Search (HS) algorithm is proposed, in which the generating probability is modified to improve searching speed and accuracy. Finally, simulation results are presented to verify performance of our scheme.
This paper addresses sparse channels estimation problem for the generalized linear models (GLM) in the orthogonal time frequency space (OTFS) underwater acoustic (UWA) system. OTFS works in the ...delay-Doppler domain, where time-varying channels are characterized as delay-Doppler impulse responses. In fact, a typical doubly spread UWA channel is associated with several resolvable paths, which exhibits a structured sparsity in the delay-Doppler domain. To leverage the structured sparsity of the doubly spread UWA channel, we develop a structured sparsity-based generalized approximated message passing (GAMP) algorithm for reliable channel estimation in quantized OTFS systems. The proposed algorithm has a lower computational complexity compared to the conventional Bayesian algorithm. In addition, the expectation maximum algorithm is employed to learn the sparsity ratio and the noise variance. Simulation and experimental results show that the proposed algorithm has superior performance and low computational complexity for quantized OTFS systems.
Target tracking is one of the broad applications of underwater wireless sensor networks (UWSNs). However, as a result of the temporal and spatial variability of acoustic channels, underwater acoustic ...communications suffer from an extremely limited bandwidth. In order to reduce network congestion, it is important to shorten the length of the data transmitted from local sensors to the fusion center by quantization. Although quantization can reduce bandwidth cost, it also brings about bad tracking performance as a result of information loss after quantization. To solve this problem, this paper proposes an optimal quantization-based target tracking scheme. It improves the tracking performance of low-bit quantized measurements by minimizing the additional covariance caused by quantization. The simulation demonstrates that our scheme performs much better than the conventional uniform quantization-based target tracking scheme and the increment of the data length affects our scheme only a little. Its tracking performance improves by only 4.4% from 2- to 3-bit, which means our scheme weakly depends on the number of data bits. Moreover, our scheme also weakly depends on the number of participate sensors, and it can work well in sparse sensor networks. In a 6 × 6 × 6 sensor network, compared with 4 × 4 × 4 sensor networks, the number of participant sensors increases by 334.92%, while the tracking accuracy using 1-bit quantized measurements improves by only 50.77%. Overall, our optimal quantization-based target tracking scheme can achieve the pursuit of data-efficiency, which fits the requirements of low-bandwidth UWSNs.
To reduce the influence of gain-phase errors and improve the performance of direction-of-arrival (DOA) estimation, a robust sparse Bayesian two-dimensional (2D) DOA estimation method with gain-phase ...errors is proposed for L-shaped sensor arrays. The proposed method introduces an auxiliary angle to transform the 2D DOA estimation problem into two 1D angle estimation problems. A sparse representation model with gain-phase errors is constructed using the diagonal element vector of the cross-correlation covariance matrix of two submatrices of the L-shaped sensor array. The expectation maximization algorithm derives unknown parameter expression, which is used for iterative operations to obtain off-grid and signal precision. Using these parameters, a new spatial spectral function is constructed to estimate the auxiliary angle. The obtained auxiliary angle is substituted into a sparse representation model with gain and phase errors, and then the sparse Bayesian learning method is used to estimate the elevation angle of the incident signal. Finally, according to the relationship of the three angles, the azimuth angle can be estimated. The simulation results show that the proposed method can effectively realize the automatic matching of the azimuth and elevation angles of the incident signal, and improves the accuracy of DOA estimation and angular resolution.
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