This paper presents the consensus iterated posterior linearisation filter (IPLF) for distributed state estimation. The consensus IPLF algorithm is based on a measurement model described by its conditional mean and cov...
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This paper presents the consensus iterated posterior linearisation filter (IPLF) for distributed state estimation. The consensus IPLF algorithm is based on a measurement model described by its conditional mean and covariance given the state, and performs iterated statistical linear regressions of the measurements with respect to the current approximation of the posterior to improve estimation performance. Three variants of the algorithm are presented based on the type of consensus that is used: consensus on information, consensus on measurements, and hybrid consensus on measurements and information. Simulation results show the benefits of the proposed algorithm in distributed state estimation.
Conventional sparse uniform arrays (SUAs) is composed of multiple identical and rigorously collinear uniform linear arrays. By adjusting the baseline length between the subarrays, the array aperture can be arbitrarily...
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Conventional sparse uniform arrays (SUAs) is composed of multiple identical and rigorously collinear uniform linear arrays. By adjusting the baseline length between the subarrays, the array aperture can be arbitrarily large, thus substantially improving the accuracy of the direction-of-arrival (DOA) estimation. However, in practical applications, it is challenging to meet the strict collinearity requirement due to geographical constraints. In this letter, to address this problem, we propose the non-collinear sparse uniform array (NCSUA) model to mitigate the influence of the non-ideal terrain and enhance the practicality of the SUA. A novel estimation algorithm is then proposed to resolve the angle ambiguity in NCSUA and effectively achieve high-accuracy DOA estimation. Compared with the conventional SUA, numerical simulation results demonstrate the superiority of NCSUA employing the new de-ambiguity algorithm in DOA estimation performance and practical applications.
Recently, principal skewness analysis (PSA) has been introduced into the domain of feature extraction. It is equivalent to the skewness version of Fast independent component analysis (FastICA). Unlike FastICA, PSA doe...
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Recently, principal skewness analysis (PSA) has been introduced into the domain of feature extraction. It is equivalent to the skewness version of Fast independent component analysis (FastICA). Unlike FastICA, PSA does not require all sample points when searching for the projection directions, making it faster. However, for the data of dimension L, PSA needs to calculate the eigenvectors of L tensors, each of size LxLxL. When L is large, PSA still requires significant computational time to find all projection directions. In this letter, we find that the (m+1)th projection direction in PSA can be obtained by calculating the eigenvector of a tensor of size (L-m) x (L-m) x (L-m). Furthermore, we propose a fast version of PSA (FastPSA) that is mathematically equivalent to PSA. The experimental results demonstrate that FastPSA has lower computational complexity than PSA.
This letter provides a solution to a robust adaptive beamformer used in remote sensing systems under the effect of two factors: 1) the impulsive noise satisfying Gaussian mixture distribution and 2) unknown model mism...
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This letter provides a solution to a robust adaptive beamformer used in remote sensing systems under the effect of two factors: 1) the impulsive noise satisfying Gaussian mixture distribution and 2) unknown model mismatches, such as look direction errors and sensor location perturbations. First, a beamformer output-based cost function is designed to effectively suppress the influence of impulsive noise. Second, the weight vector of the beamformer is regularized through norm constraints to reduce the impact of model mismatches. Furthermore, an efficient iterative scheme is established to quickly minimize the designed cost function based on their properties, and a robust adaptive beamformer can be obtained. Finally, computer simulations are presented to demonstrate the robust performance and fast convergence of the novel algorithm in the presence of impulsive noise and model mismatches.
To leverage distributed data communication and learning in sensor networks effectively, edge learning (EL) methods have garnered significant attention. In the realm of distributed sensor networks, achieving consensus ...
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To leverage distributed data communication and learning in sensor networks effectively, edge learning (EL) methods have garnered significant attention. In the realm of distributed sensor networks, achieving consensus estimation of interested variables stands as a pivotal challenge. To address this challenge using EL methods, several approaches have been proposed combining message passing (MP) algorithms. In this article, we first describe the distributed consensus algorithm based on MP and summarize the sampling-based and parameter-based representation of the beliefs exchanged in the distributed MP algorithm. To improve the accuracy of estimation while retaining the low-complexity advantage of the parametric representation method, we propose a distributed consensus framework based on the Gaussian mixture model (GMM) MP. We approximate and keep the form beliefs as GMM in the iterations. Two different simulation scenarios are performed to shed light on the proposed distributed consensus estimation framework, i.e., static target localization and dynamic target tracking. Finally, simulation results show the performance advantages of the algorithm proposed.
In this article, we propose a new fully distributed cell-free multiple-input and multiple-output (MIMO) architecture that enables independent deployment of access points (APs) without any infrastructure such as a cent...
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In this article, we propose a new fully distributed cell-free multiple-input and multiple-output (MIMO) architecture that enables independent deployment of access points (APs) without any infrastructure such as a central processing unit (CPU). In the proposed architecture, all baseband processing is distributively executed by homogeneous APs without any CPU, thanks to the fully distributed functional split structure. Since the proposed architecture only consists of APs, we expect that a 5G/6G-based private network with a cell-free MIMO capability can easily be built for Industrial Internet of Things (IIoT) applications without the help of mobile network operators (MNOs). We also propose a novel precoding algorithm for orthogonal frequency division multiplexing (OFDM)-based cell-free MIMO systems. In the proposed precoding algorithm, the APs in a user-centric cluster cooperatively maximize the capacity of the target user equipment (UE) while minimizing the interference to nearby UEs. The proposed precoding algorithm solves the OFDM power allocation problem to allocate power on a per-subcarrier basis under the per- AP power constraints, which has not been addressed in previous works. We have built a full-fledged and real-time cell-free MIMO testbed that implements the proposed cell-free MIMO architecture and precoding algorithm. The validity of the proposed architecture and precoding algorithm is verified by experiments and simulations.
One of the most relevant physiological parameters in dogs is respiratory rate (RR). The aim of this paper is to present a novel wearable system that allows to accurately estimate RR in dogs, and to compare it to a gol...
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One of the most relevant physiological parameters in dogs is respiratory rate (RR). The aim of this paper is to present a novel wearable system that allows to accurately estimate RR in dogs, and to compare it to a gold standard in static conditions. Data from 12 dogs were acquired while the animals were anesthetized and attached to a vital signs monitor. The experimental setup consisted of three Inertial Measurement Units (IMUs) applied on the dog, and a video camera filming the RR value shown on the monitor. The range of RR values analyzed in the study is 0 to 29 breaths per minute, read by the vital signs monitor. The mean RMSE for the data acquisitions is 1.68 breaths per minute. The values of the filtering parameters that allow to obtain the best performance depend on the specific acquisition. This result demonstrates that adaptive filtering is a viable method for the application. Future developments include tests on a larger dataset, and trials on dogs in unconstrained environments and during movement.
Existing photomultiplier tube (PMT)-based gating control systems embedded in bathymetric light detection and ranging (LiDAR) devices cannot automatically adjust the PMT gain in real time, resulting in the saturation d...
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Existing photomultiplier tube (PMT)-based gating control systems embedded in bathymetric light detection and ranging (LiDAR) devices cannot automatically adjust the PMT gain in real time, resulting in the saturation distortion of echo signals or failure to receive echo signals. Therefore, this study proposes an adaptive adjustment method for the laser energy and PMT gain based on the self-feedback of echo data from a high-speed sampling and storage system. Zynq-7000 All Programmable SoC (ZYNQ) was used as the primary controller, and the echo data analysis module, laser energy adjustment module, and PMT gain adjustment were designed based on the field-programmable gate array (FPGA). The corresponding control software was developed based on the advanced reduced instruction set computer machines (ARM) end to call the custom intellectual property (IP) cores. The proposed method was experimentally validated using indoor corridors and outdoor water areas, such as pools, reservoirs, Wujiu Beach, and the Beibu Gulf. The experimental results demonstrated that the proposed method could adaptively adjust laser energy and PMT gain, with which the voltage amplitude fluctuation range of the echo signal was effectively reduced from the original -1.7-0 V (acquisition range of high-speed sampling and storage system) to -1.04 to -0.26 V. With the comparative verification with multibeam sounder, the mean and standard deviation of the difference between the Z coordinates are -0.21 and 0.15, respectively, which indicates that the LiDAR using the proposed method achieves a similar accuracy of bathymetry.
This article focuses on the parameter estimation problem in wireless sensor networks (WSNs) under adversarial attacks, considering the complexities of sensing and communication in challenging environments. In order to...
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This article focuses on the parameter estimation problem in wireless sensor networks (WSNs) under adversarial attacks, considering the complexities of sensing and communication in challenging environments. In order to mitigate the impact of these attacks on the network, we propose a novel AP-DLMS algorithm with adaptive threshold attack detection and malicious punishment mechanism. The adaptive threshold is constructed using the observation matrix and network topology to detect the location of malicious attacks, while the standard reference estimation is designed to obtain the estimated deviation of each node. To mitigate the impact of data tampering on network performance, we introduce the honesty factor and punishment factor to combine the weights of normal nodes and malicious nodes respectively. Additionally, we propose a new probabilistic random attack model. Simulations are conducted to investigate the influence of key parameters in the adaptive threshold on the performance of the proposed AP-DLMS algorithm, and the mean square performance of the algorithm is analyzed under various attack models. The results demonstrate that the proposed algorithm exhibits strong robustness in adversarial networks, and the proposed attack model effectively demonstrates the impact of attacks.
Photon-assisted generation of terahertz (THz)signals can support larger modulation bandwidths in future ultrahigh-speed wireless communication systems. Single-input-multiple-output(SIMO)or multiple-input-multiple-outp...
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Photon-assisted generation of terahertz (THz)signals can support larger modulation bandwidths in future ultrahigh-speed wireless communication systems. Single-input-multiple-output(SIMO)or multiple-input-multiple-output(MIMO) technology effectively uses the spatial dimension to improve the quality of the received signal, such as signal-to-noise ratio (SNR) and bit error rate (BER) ratio. Therefore, we experimentally demonstrate D-bandphoton-assisted THz 1 x 2 SIMO 4.6-km wireless communication. We use a photon-assisted THz technology to generate a 124.37-GHzD-band signal, which is received according to the maximum ratio combining (MRC) technology after 1 x 2 SIMO wireless transmission and uses fully blind digital signalprocessing (DSP)for carrier recovery algorithm without any training sequence. According to the experimental results, the maximum SNR diversity gain between 1 x 1 and 1 x 2 SIMO can be up to2.8 dB. After the MRC algorithm, the BER can be reduced by one order of magnitude, which is less than 15% of soft decision forward error correction (SD-FEC). This is the first experimental demonstration of high gain and long range in a D-band THz 1 x 2 SIMO system with a line rate of 43.5 Gb/s. These results will provide important assistance to our future efforts in photon-assisted THz wireless communications.
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