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Deep learning-based automatic modulation recognition networks are susceptible to adversarial attacks, posing significant performance vulnerabilities. In response, we introduce a defense framework enriched by tailored autoencoder (AE) techniques. Our design features a detection AE that harnesses reconstruction errors and convolutional neural networks to discern deep features, employing thresholds from reconstruction error and Kullback–Leibler divergence to identify adversarial samples and their origin mechanisms. Additionally, a restoration AE with a multi-layered structure effectively restores adversarial samples generated via optimization methods, ensuring accurate classification. Tested rigorously on the RML2016.10a dataset, our framework proves robust against adversarial threats, presenting a versatile defense solution compatible with various deep learning models. [ABSTRACT FROM AUTHOR]

In this article, an effective algorithm is proposed for direction‐of‐arrival (DOA) estimation of coherent signals using the l2‐norm with moving coprime arrays. The proposed algorithm exhibits high accuracy and robustness in decorrelation of coherent signals compared to existing methods. Firstly, the received signal is filtered on the moving coprime array and a signal model is constructed that is compatible with the moving coprime array. Next, virtual sensors is inserted into the holes of the moving coprime array and the received signals are initialised. Then, the Toeplitz matrix is reconstructed based on the covariance matrix of the initial signal and perform low‐rank matrix recovery to eliminate signal coherence by compensating the matrix for zero. Finally, the DOA estimation for the signal is performed using spectral peak search. Simulation results verify that the estimation accuracy of our proposed algorithm outperforms various existing algorithms. [ABSTRACT FROM AUTHOR]

In this letter, a localization algorithm for mixed‐field sources based on fourth‐order cumulant matrix rank reduction is proposed. First, a cumulant matrix is constructed, and a spectral peak search function associated with the direction‐of‐arrival (DOA) of the sources only is constructed using the rank reduction principle to achieve DOA estimation. Then the range estimation is also obtained by a spectral peak search. Compared to existing algorithms, the algorithm is able to utilize all the elements of the cumulant matrix and has a higher accuracy and degrees‐of‐freedom. The simulation results verify the excellence of the algorithm. [ABSTRACT FROM AUTHOR]

The degree of freedom (DOF) is an important performance metric for evaluating the design of a sparse array structure. Designing novel sparse arrays with higher degrees of freedom, while ensuring that the array structure can be mathematically represented, is a crucial research direction in the field of direction of arrival (DOA) estimation. In this paper, we propose a novel L-shaped sparse sensor array by adjusting the physical placement of the sensors in the sparse array. The proposed L-shaped sparse array consists of two sets of three-level and single-element sparse arrays (TSESAs), which estimate the azimuth and elevation angles, respectively, through one-dimensional (1-D) spatial spectrum search. Each TSESA is composed of a uniform linear subarray and two sparse subarrays, with one single common element in the two sparse subarrays. Compared to existing L-shaped sparse arrays, the proposed array achieves higher degrees of freedom, up to 4 Q 1 Q 2 + 8 Q 1 − 5 , when estimating DOA using the received signal covariance. To facilitate the correct matching of azimuth and elevation angles, the cross-covariance between the two TSESA arrays is utilized for estimation. By comparing and analyzing performance parameters with commonly used L-shaped and other sparse arrays, it is found that the proposed L-shaped TSESA has higher degrees of freedom and array aperture, leading to improved two-dimensional (2-D) DOA estimation results. Finally, simulation experiments validate the excellent performance of the L-shaped TSESA in 2-D DOA estimation. [ABSTRACT FROM AUTHOR]

In passive localization techniques, as the scale of the array of the sensors used increases, the source distribution may be a coexistence of near-field (NF) and far-field (FF) sources. Most of the existing algorithms dedicated to the localization of mixed-field sources are based on a simplified model, which has model errors and cannot make good use of non-circular properties when non-circular signals are present in the sources. In this paper, we present a mixed-field circular and non-circular source localization algorithm based on exact spatial propagation geometry. First, we make an initial estimate of the source parameters using exact spatial geometry relations. The MUSIC algorithm is then used in combination with the non-circular properties of the signal to achieve an accurate estimate. The algorithm does not lose performance due to model mismatch and is able to make good use of the non-circular properties of the sources to improve the estimation accuracy. The simulation results show that the proposed algorithm can effectively distinguish between sources and that the algorithm performs satisfactorily. [ABSTRACT FROM AUTHOR]

BACKGROUND: Muscle strength in the upper and lower limbs is a major contributing factor to punch force and is one of the keys to success in boxing. OBJECTIVE: This study aimed to investigate the relationship between knee and shoulder strength and punching force in boxers. METHODS: Twenty-one boxers completed knee flexion, knee extension, shoulder external rotation and internal rotation isokinetic concentric contractions at slow (60 ∘ /s) and fast (180 ∘ /s) angular velocities. Maximal punch force tests included lead and rear straight arm punching were assessed using a force platform. RESULTS: Pearson's correlation coefficients revealed that knee extension (r = 0.646–0.848) and knee flexion (r = 0.470–0.646) peak torques were moderately to very strongly correlated with maximal punching force in lead and rear arms. The shoulder internal rotation (r = 0.492–0.634) and shoulder external rotation (r = 0.441–0.588) peak torques were moderate to strongly correlated with maximal punching force. Moreover, knee extension peak torques at 60 ∘ /s had higher correlation with maximal punching force. In contrast, shoulder internal rotation peak torques at 180 ∘ /s had a larger correlation with maximal punching force. CONCLUSIONS: The main findings indicated the importance of the capacity to generate maximum knee extension and rapid shoulder internal rotation strength, contributing to punch force production. [ABSTRACT FROM AUTHOR]

Joint direction of arrival (DOA) and time delay (TD) estimation of wideband signals such as WIFI and mobile communication signals has to face the challenges aroused by multipath effect in indoor environments. New methods that can deal with coherent wideband signals with high estimation accuracy are necessary. To achieve localization when signals are coherent, this paper proposes a new joint estimation method which fully utilizes the subcarriers of wideband signals. This method could greatly extend the array aperture and accomplish the joint estimation through UCA-ESPRIT and one-dimensional (1-D) search. Simulation experiments are conducted to show the validity and efficiency of this method.

At present, there are few sparse arrays used in the mixed near‐field (NF) and far‐field (FF) localization based on non‐circular (NC) signals. Inspired by the symmetric flipped nested array (SFNA) used in the existing mixed NF and FF NC source, in order to further improve the parameter estimation accuracy of the mixed NF and FF NC signal, an improved symmetric flipped nested array (ISFNA) for mixed NF and FF NC sources localization was developed. First, the uniform subarrays in the SFNA are rearranged, ⌈N22⌉−1${\lceil \frac{N_2}{2}\rceil }-1$ elements are extracted from the uniform subarrays and rearranged into ISFNA. ISFNA is more sparse, the array aperture is larger, and the array degree of freedom (DOF) is higher; second, the formula of the maximum consecutive lags of ISFNA is given; third, a special fourth‐order cumulant is used to eliminate the range parameter and then use a one‐dimensional (1‐D) spectral peak search to obtain all Directions of Arrival (DOAs). By defining the range search, the range can be obtained by bringing in estimated DOAs. Finally, the superiority of the proposed array is proved by simulation. [ABSTRACT FROM AUTHOR]

In this paper, a high-accuracy localization problem under a complex non-line-of-sight (NLOS) condition is addressed by a new method that utilizes multiple NLOS paths to improve localization accuracy, as opposed to the traditional method of suppressing them. The spatial multi-path array fusion localization model is presented and analyzed, followed by an angle-of-arrival (AOA) and time-of-arrival (TOA) algorithm based on spatial multi-information fusion that seeks to improve localization accuracy. Multi-path of spatial signals, measurement of the multi-element antenna, and geographic environment information are integrated into the proposed method for localization optimization. Simulation experiments were carried out, and the results revealed that the proposed algorithm is capable of making full use of spatial multi-location information for localization, thus improving the accuracy of localization in a the NLOS environment effectively and increasing the locatable probability of complex environment localization applications. [ABSTRACT FROM AUTHOR]

Simple Summary: Black-billed capercaillie (Tetrao parvirostris) were listed as least concern (LC) class by the International Union for Conservation of Nature (IUCN). However, black-billed capercaillie was an endangered species in China and classified as first-class state protection animal (category I). Only a few studies have focused on the feeding habits of black-billed capercaillie and there is less research content at the molecular level, such as genomics, microbiome, and molecular markers. To improve our understanding of black-billed capercaillie, the fecal microbiome was characterized. This study provides reasonable scientific data for the understanding of black-billed capercaillie and may provide important insights for protecting this endangered species in China. Black-billed capercaillie (Tetrao parvirostris) was listed as a first-class state-protected animal because it was endangered in China (Category I). This study is the first to examine the diversity and composition of T. parvirostris gut microbiome in the wild. We collected fecal samples from five black-billed capercaillie flock roosting sites (each 20 km apart) in one day. Thirty fecal samples were sequenced with 16S rRNA gene amplicons on the Illumina HiSeq platform. This study is the first to analyze the fecal microbiome composition and diversity of black-billed capercaillie in the wild. At the phylum level, Camplyobacterota, Bacillota, Cyanobacteria, Actinomycetota, and Bacteroidota were the most abundant in the fecal microbiome of black-billed capercaillie. At the genus level, unidentified Chloroplast, Escherichia−Shigella, Faecalitalea, Bifidobacterium, and Halomonas were the dominant genera. Based on alpha and beta diversity analyses, we found no significant differences in the fecal microbiome between five flocks of black-billed capercaillie. Protein families: genetic information processing; protein families: signaling and cellular processes, carbohydrate metabolism; protein families: metabolism and energy metabolism are the main predicted functions of the black-billed capercaillie gut microbiome through the PICRUSt2 method. This study reveals the composition and structure of the fecal microbiome of the black-billed capercaillie under wild survival conditions, and this study provides scientific data for the comprehensive conservation of the black-billed capercaillie. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation of mixed near‐field (NF) and far‐field (FF) signals is one of the key issues in the study of array signal processing. For more precise localisation performance, sparse arrays have drawn the attention of an increasing number of academics. Excitingly, sparse arrays enable larger array apertures and higher degrees of freedom (DOFs) than traditionally used uniform linear arrays. Consequently, this study proposes a transformed symmetric nested array (TSNA) for the passive localisation application regarding mixed NF and FF. There are three processes that go into building the proposed array. In the first step, we swap the positions of the uniform linear arrays and the sparse linear arrays in the nested array. In the second step, we place the corresponding array sensors extracted from the uniform linear array behind the uniform linear array to form a new subarray. In the last step, the TSNA is constructed by folding the formed array in half. Furthermore, we give the formula of the largest consecutive lags of the TSNA and its corresponding constructor. Finally, through theoretical analysis and computer performance simulation, this study illustrates the proposed array's numerous advantages over currently used symmetric sparse arrays. [ABSTRACT FROM AUTHOR]

Large‐scale arrays are widely used due to their advantages of high gain, high resolution, and strong beam control capability. The large number of antennas leads to a sharp increase in computational complexity, and thus, it is necessary to reduce the complexity of signal processing. A signal separation method is proposed based on subarray beam synthesis. This method can improve signal reception and separation performance through zero‐forcing calculation and realise system gain and phase error corrections in the front end of an antenna. Compared with back‐end processing, it has the advantage of low implementation difficulty. The feasibility and performance of the method are verified by simulations. [ABSTRACT FROM AUTHOR]

Based on the EBM-DEA (Explainable Boosting Machine-Data Envelopment Analysis) model, this paper constructs an evaluation model of urban industrial carbon efficiency (UICE), measures and analyzes the spatial evolution characteristics of China's UICE from 2003 to 2016, and analyzes the influencing factors of UICE using the Tobit model. The research draws the following conclusions: (1) China's UICE improved from 2003 to 2016, and the distribution showed a spatial pattern decreasing from the east, central, west, and northeast regions. (2) The UICE, by region, was at an initial low stable level in 2003 and was in the process of moving towards a highly-efficient stable state up until 2016. The differences between regions have been the main aspect which affects the overall variation in UICE in China. (3) There is a logistic curve relationship between the economic development level and UICE. (4) Nationally, the factors that are significantly and positively correlated with UICE are: industrial agglomeration, local fiscal decentralisation, level of economic development, technological progress, industrial enterprises' average size, and industrial diversification. Factors that are significantly negatively correlated with UICE are the level of industrialization, the share of output value of state-owned enterprises in total output value, industrial openness, and environmental regulation. The factors influencing UICE differ depending on the stage of industrialization. [ABSTRACT FROM AUTHOR]

In order to solve the problems of high complexity and absence of adaptability with the joint estimation for time delay (TD) and angle of arrival (AOA) in coherent multipath environments, a fast estimation algorithm based on orthogonal frequency division multiplexing (OFDM) technique is proposed. First, we combine OFDM signal characteristics with array features to obtain extended arrays. Then, we obtain the channel frequency domain response covariance matrices for TD and AOA estimation separately by smoothing preprocessing in the spatial and frequency domains, respectively. Finally, we estimate the TD values by a one‐dimensional (1‐D) spectral peak search, as well as determine the closed‐form solution for AOA by Unitary‐ESPRIT. In comparison with previous work, the proposed algorithm not only improves the adaptability in correlated or mixed multipath environments but also significantly reduces the complexity. The simulation results show the effectiveness and robustness of the proposed joint estimation algorithm. [ABSTRACT FROM AUTHOR]

As it is known, existing mixed near‐field and far‐field non‐circular source localisation rely on the uniform linear array (ULA). Compared with the ULA, the sparse array can obtain more consecutive lags and a larger physical array aperture. So a novel symmetric flipped nested array (SFNA) is proposed for mixed near‐field and far‐field non‐circular source localisation. At the same time, according to the special geometry of the array, the non‐circular phase information can be removed and the existing algorithm can be used to solve the problem of sparse array estimating the mixed near‐field and far‐field non‐circular signals. First, by exchanging the positions of the subarrays in the traditional nested array and then flipping them, a new SFNA is formed; second, the authors present the maximum consecutive lags and composition method of SFNA. For example, 4MN +4M −3 consecutive lags can be obtained by using a 2M + 2N −1 array element; third, in the estimation of mixed near‐field and far‐field non‐circular sources, a special cumulant is used to eliminate the range of the mixed near‐field and far‐field non‐circular sources and a one‐dimensional (1‐D) spectral search is used to obtain all the directions of arrival, and the range can be obtained by defining the range spectrum; finally, numerical simulation results demonstrate the superiority of the proposed array. [ABSTRACT FROM AUTHOR]

Owing to increased degrees of freedom (DOFs) and reduced mutual coupling effect, sparse linear arrays can improve the performance of direction‐of‐arrival estimation compared with uniform linear arrays. In this study, a novel sparse linear array is presented based on fourth‐order cumulants, which consists of five sparse sub‐arrays. The sensor locations of the five sub‐arrays are determined by a closed‐form expression. Closed‐form expressions of DOFs for the proposed array with any number of sensors are also derived. By optimising the allocation of sensors in each sub‐array, the DOFs of the proposed array with M sensors achieve OM4 $\mathcal{O}\left({M}^{4}\right)$. Because each sub‐array is sparse, the number of pairwise sensors with small separation is significantly alleviated. Theoretical analysis shows that the proposed array can provide a higher number of DOFs and is more robust in scenarios with heavy levels of mutual coupling than most sparse linear arrays. Numerical simulations are conducted to demonstrate the merits of the proposed array over other sparse linear arrays. [ABSTRACT FROM AUTHOR]

Nowadays, the joint estimation of time delay (TD) and angle of arrival (AOA) using conventional vector structure suffers from the considerable complexity of multidimensional spectrum search. Therefore, a fast estimation method using orthogonal frequency division multiplexing (OFDM) technology and uniform planar array (UPA) is proposed in this paper, which adopts low-complexity tensor-based operations and spatial-frequency features to reconfigure the channel frequency response. To begin with, the array response is integrated with the OFDM signal characteristics to build an extended array in tensor form. Afterwards, we process the covariance matrix of the tensor structure by CANDECOMP/PARAFAC decomposition (CPD) to separate the respective signal subspaces of TD and AOA estimates. Finally, we conduct a one-dimensional (1-D) spectrum search to locate the TD estimates and a two-dimensional (2-D) spectrum search to locate the AOA estimates. The simulated performance demonstrates that the proposed algorithm offers precise estimates at low signal-to-noise ratios in a multipath environment and outperforms traditional vector-based algorithms with respect to computational complexity. [ABSTRACT FROM AUTHOR]

Recently, the joint estimation for time delay (TD) and direction of arrival (DOA) has suffered from the high complexity of processing multi-dimensional signal models and the ineffectiveness of correlated/coherent signals. In order to improve this situation, a joint estimation method using orthogonal frequency division multiplexing (OFDM) and a uniform planar array composed of reconfigurable intelligent surface (RIS) is proposed. First, the time-domain coding function of the RIS is combined with the multi-carrier characteristic of the OFDM signal to construct the coded channel frequency response in tensor form. Then, the coded channel frequency response covariance matrix is decomposed by CANDECOMP/PARAFAC (CPD) to separate the signal subspaces of TD and DOA. Finally, we perform a one-dimensional (1D) spectral search for TD values and a two-dimensional (2D) spectral search for DOA values. Compared to previous efforts, this algorithm not only enhances the adaptability of coherent signals, but also greatly decreases the complexity. Simulation results indicate the robustness and effectiveness for the proposed algorithm in independent, coherent, and mixed multipath environments and low signal-to-noise ratio (SNR) conditions. [ABSTRACT FROM AUTHOR]

In order to solve the problem of severely decreased performance under the situation of rapid moving sources and unstable array platforms, a null broadening robust adaptive beamforming algorithm based on power estimation is proposed in this paper. First of all, we estimate the interference signal power according to the characteristic subspace theory. Then, the correspondence between the signal power and steering vector (SV) is obtained based on the orthogonal property, and the interference covariance matrix (ICM) is reconstructed. Finally, with the aim of setting virtual interference sources, null broadening can be carried out. The proposed algorithm results in a deeper null, lower side lobes and higher tolerance of the desired signal steering vector mismatch under the condition of low complexity. The simulation results show that the algorithm also has stronger robustness. [ABSTRACT FROM AUTHOR]

Non-invasive contactless simultaneous sensing and heating of individual droplets would allow droplet microfluidics to empower a wide range of applications. However, it is challenging to realize simultaneous sensing and heating of individual droplets as the resonance frequency of the droplet fluid, which is decided by its permittivity, must be known so that energy is only supplied at this frequency for droplet heating with one resonator. To tailor the energy transfer in real-life heating applications, the droplet has to be sensed first to identify its corresponding resonance frequency, which is used to dynamically tune the frequency for supplying the required energy for heating this particular droplet. To achieve this goal, two resonators are needed, with one for sensing and one for heating. Integrating multiple resonators into one typical microfluidic device limits placement of the resonators to be as close as possible, which would raise the concern of crosstalk between them. The crosstalk would result in inaccurate sensing and heating. This study focuses on numerically and experimentally investigating the effect of influencing parameters on the crosstalk between two adjacent resonators with the ultimate goal of providing guidance for multiplexing the resonators in a typical microfluidic device. ANSYS HFSS is used to perform the electromagnetic analysis based on the finite element method. Experimental studies are conducted on a microfluidic chip integrated with two resonators to validate the numerical results. An optimal distance between two resonators is suggested, with the recommendation for the resonator size and heating power towards simultaneous sensing and heating of individual droplets. [ABSTRACT FROM AUTHOR]

Summary: The general Global Navigation Satellite System (GNSS) receiver faces several challenges because of jamming signals, spoofing signals, and multipath signals, which severely influence its safety. In this paper, a receiver scheme with an interference recognition function is designed. In the latter, the correlation peak with different shapes is produced according to different interferences. The machine learning method is then applied to recognize and classify these feature maps. This transforms the interference recognition problem into a machine learning‐based classification problem. In order to reduce the complexity of the machine learning network, only the finite‐length correlation peak region of interest (ROI) is extracted as network input, endowing the shallow neural network with the interference recognition function. Afterward, five data acquisition environments are designed: authentic, spoofing, jamming, non‐line‐of‐sight (NLOS) multipath, and line‐of‐sight (LOS) multipath. Moreover, several experimental data are acquired, followed by the production of the correlation peak maps dataset, that are then learned and tested using two machine learning networks: one‐dimensional convolutional neural network (1D‐CNN) and bidirectional long short‐term memory neural network (BiLSTM‐NN). The results demonstrate that a recognition accuracy rate of over 98% can be reached using the shallow machine learning network. [ABSTRACT FROM AUTHOR]

In the coprime planar array (CPA), the existing tensor DOA estimation has the problem that the statistics are not fully utilized. We propose a two-dimensional DOA estimation method based on tensor self-correlation, which realizes the high-resolution and high-precision joint estimation of elevation angle and azimuth angle. Firstly, we represent the received signals of two subarrays with tensors and then obtain the self-correlation covariance tensor of the subarrays themselves and the cross-correlation covariance tensor of the two subarrays. Then, we extract the covariance tensor corresponding to the maximum continuous virtual array and prove the expression of the maximum continuous virtual array aperture of the proposed method. Compared with the existing methods, the proposed method effectively improves the maximum aperture of the continuous virtual array. Finally, the signal subspace is solved by tensor expansion and tensor decomposition. Simulation results show that under the same conditions, the proposed method has higher estimation accuracy and degree of freedom than the cross-correlation tensor method, and the resolution is also improved significantly. [ABSTRACT FROM AUTHOR]

As we all know, the non‐uniform array is widely used in the mixed near‐field (NF) and far‐field sources localization. The nested array contains a uniform linear array, it is easy to be affected by mutual coupling, so we propose a symmetric thinned coprime array (STCA) to reduce the mutual coupling effect of mixed source localization. In this paper, the STCA configuration consists of two sparse uniform linear arrays, the first subarray is composed of 2N − 1 sensors, and the sensor element spacing is Md. The second subarray is composed of 2M+2⌊M2⌋ $2M+2\lfloor \frac{M}{2}\rfloor $ sensors, and the sensor element spacing are Nd and Md respectively. The two subarrays form the symmetric coprime array, which shares a reference sensor. Under the same number of physical array sensors, compared with the latest symmetric nested array, although STCA has lower consecutive lags, the array sensors have larger inter‐sensor spacing and larger physical array aperture, which can significantly reduce the mutual coupling effect between physical sensors, and better estimation performance of direction‐of‐arrival (DOA) estimation can be achieved. Finally, simulation results show that STCA can achieve better performance than other symmetric nested arrays under the same array sensors and mutual coupling effects. [ABSTRACT FROM AUTHOR]

Aiming at the problems that the chaotic sequence is periodic due to the influence of calculation accuracy and the pseudorandomness of the sequence is not ideal, this paper proposes a dynamic multimapping compound (DMMC) chaotic sequence based on time of days (TOD) and several improved logistic mappings, which not only increases the key space, flexibility, and complexity of sequence generation but also improves pseudorandom performance. The sequence is composed of two new logistic maps with different initial values, which are then scrambled by the 2D-LICM sequence. In the whole process, the TOD is used to determine the system parameter and the selection of the scrambling sequence. After certain processing, it is connected in series with the original generated sequence to obtain the target chaotic sequence. The simulation analysis proves that the sequence generated by the algorithm proposed in this paper has good run length, correlation, and ideal balance and greatly improves the confidentiality of the system, which proves the effectiveness and reliability of the designed spread spectrum sequence. [ABSTRACT FROM AUTHOR]

The global COVID-19 pandemic caused by SARS-CoV-2 has resulted in an unprecedented economic and societal impact. Developing simple and accurate testing methods for point-of-care (POC) diagnosis is crucial not only for the control of COVID-19, but also for better response to similar outbreaks in the future. In this work, we present a novel proof-of-concept of a microfluidic microwave sensing method for POC diagnosis of the SARS-CoV-2 virus. This method relies on the antibody immobilized on the microwave sensor to selectively capture and concentrate the SARS-CoV-2 antigen or virus present in a buffer solution flowing through the sensor region in a microchannel. The capturing of the SARS-CoV-2 antigen or virus results in a change in the permittivity of the medium near the sensor region reflected by the resonance frequency shift which is used for detection. The use of microchannels offers precise control of the sample volume and the continuous flow nature also offers the potential to monitor the dynamic capturing process. The microwave-microfluidic device shows a good sensitivity of 0.1 ng ml−1 for the SARS-CoV-2 antigen and 4000 copies per ml for the SARS-CoV-2 virus. The resonance frequency shift presents a linear relationship with the logarithm of antigen or virus concentration, respectively. This detection method is able to distinguish SARS-CoV-2 from the antigen of human CD4 and two human coronaviruses (MERS and HKU1), which presents a new pathway towards POC diagnosis of the COVID-19 at the community level. It presents the potential to detect other viruses by functionalizing the microwave sensor with respective antibodies. [ABSTRACT FROM AUTHOR]

Recently, sparse linear arrays have aroused significant attention because they can provide increased degrees of freedom (DOFs) and enlarged inter‐element spacing, which are the critical factors to improve the accuracy of direction of arrival (DOA) estimation. In this paper, to alleviate the high mutual coupling caused by the dense part of transformed nested arrays (TNAs), we present a super transformed nested array (STNA) for the DOA estimation of non‐circular signals. It can be obtained by removing some sensors from the dense part of transformed nested arrays and relocating them appropriately. The proposed array structure possesses a higher DOF and a sparser physical structure, resulting in less mutual coupling compared to TNA. For the proposed array structure, we provide theoretical proof of the DOFs and deduce the weight functions. Numerical simulations are conducted to demonstrate the advantages of the STNA over existing sparse arrays. [ABSTRACT FROM AUTHOR]

Compared with the uniform linear array (ULA) under the same physical array element, the coprime array can obtain a higher degree of freedom and a larger array aperture, so it has received extensive attention from scholars in recent years. Based on the idea of interference plus noise covariance matrix (INCM) reconstruction, this study proposed a robust adaptive beamforming algorithm using the coprime array. In the virtual array generated by the coprime array, the authors prove from the perspective of virtual signals that the constructed Toeplitz matrix is equivalent to a virtual ULA with more array elements. Subsequently, a high‐resolution subspace algorithm is used to estimate the direction of arrival (DOA) of the signals, and the estimated DOA is used to reconstruct the direction vector (DV) of the coprime array. Through the eigenvalue decomposition of the reconstructed Toeplitz matrix, the corresponding relationship between the eigenvalue and the power is deduced, and the signals and noise power are estimated using the eigenvalue. Finally, combine the DV of the desired signal and the reconstructed INCM to calculate the weight vector of the proposed algorithm. The simulation results prove the superiority and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

When the array structure of the sparse arrays (SA) cannot be determined, the existing beamforming algorithms designed according to specific formations such as coprime arrays (CA), nested arrays (NA), etc. will fail. To solve this problem, we propose two algorithms that are suitable for a variety of SA. In the first method, assuming that the desired signal is a non-Gaussian signal, the desired signal direction vector (DSDV) is estimated using the fourth-order cumulant, and then the interference plus noise covariance matrix (INCM) is reconstructed by integrating the area outside the desired signal. When the desired signal is a Gaussian signal, we propose the second method. The second method estimates the power and direction of arrival (DOA) of the signals by performing eigenvalue decomposition on the sampled covariance matrix (SCM) and finally calculates the weight vector. However, this method needs to estimate the DOA of the signals, so it has certain requirements for the SA structure design. The simulation results show that the proposed method has good performance and strong robustness under different SA. [ABSTRACT FROM AUTHOR]

As we all know, nested array can obtain a larger array aperture and more degrees of freedom using fewer sensors. In this study, we not only designed an enhanced symmetric nested array (ESNA), which achieved more consecutive lags and more unique lags compared with a generalized nested array but also developed a special cumulant matrix, in the case of a given number of sensors, which can automatically generate the largest consecutive lags of the array. First, the direction-of-arrivals (DOAs) of mixed sources are estimated using the special cumulant matrix. Then, we can estimate the range of the near-field source in the mixed source using a one-dimensional spectral search through estimated DOAs, and in the mixed sources, the near-field and far-field sources are classified by bringing in the range parameter. The largest consecutive lags and composition method of ESNA are also given, under a given number of sensors.Our algorithm has moderate computation complexity, which provides a higher resolution and improves the parameters' estimation accuracy. Numerical simulation results demonstrate that the proposed array showed an outstanding performance under estimation accuracy and resolution ability for both DOA and range estimation compared with existing arrays of the same physical array sensors. [ABSTRACT FROM AUTHOR]

As we all know, the model mismatch, primarily when the desired signal exists in the training data, or when the sample data is used for training, will seriously affect algorithm performance. This paper combines the subspace algorithm based on direction of arrival (DOA) estimation with the adaptive beamforming. It proposes a reconstruction algorithm based on the interference plus noise covariance matrix (INCM). Firstly, the eigenvector of the desired signal is obtained according to the eigenvalue decomposition of the subspace algorithm, and the eigenvector is used as the estimated value of the desired signal steering vector (SV). Then the INCM is reconstructed according to the estimated parameters to remove the adverse effect of the desired signal component on the beamformer. Finally, the estimated desired signal SV and the reconstructed INCM are used to calculate the weight. Compared with the previous work, the proposed algorithm not only improves the performance of the adaptive beamformer but also dramatically reduces the complexity. Simulation experiment results show the effectiveness and robustness of the proposed beamforming algorithm. [ABSTRACT FROM AUTHOR]

When there is coexistence of uncorrelated and coherent signals in sparse arrays, the conventional algorithms for direction-of-arrival (DOA) estimation using difference coarray fail. In order to solve the problems, this paper analyzes the feasibility of using spatial smoothing in sparse arrays. Firstly, we summarize the two types of sparse arrays, one consisting of identical sparse subarrays and the other consisting of several uniform linear subarrays. Then, we give the feasibility analysis and the processes of applying spatial smoothing. At last, we discuss the performance of the number of detectable coherent signals in different sparse arrays. Numerical experiments prove the conclusions proposed by the paper. [ABSTRACT FROM AUTHOR]

The sparse arrays using two uniform linear arrays have attracted considerable interest due to the capability of giving analytical expression of sensor location and owning robust direction-of-arrival (DOA) facing strong mutual coupling and sensor failure. In order to achieve the maximum consecutive virtual uniform linear array in difference coarray, in this paper, a design method of a novel sparse array using two uniform linear arrays (NSA-U2) is proposed. We first analyze the relationship between the values of displacement of two subarrays and difference coarray, and then we give the analytical expressions of the displacement and the number of consecutive lags. By discussing the selection of number of subarray sensors, the design of NSA-U2 is completed. Moreover, through choosing a proper compressed interelement spacing, NSA-U2 can be robust to mutual coupling effect. Numerical experiments prove the effectiveness and favorable performance of DOA estimation with mutual coupling. [ABSTRACT FROM AUTHOR]

Recently, the design of sparse linear array for direction of arrival (DOA) estimation of non-Gaussian signals has attracted considerable interest due to the fact that the fourth-order difference coarray offered by non-Gaussian significantly increases the aperture of a virtual linear array, which improves the performance of DOA estimation. In this paper, a super four-level nested array (S-FL-NA) configuration based on fourth-order cumulants (FOC) is proposed. The S-FL-NA consists of uniform linear arrays which have different interelement spacing. The proposed array configuration is designed based on interelement spacing, which, for a given number of sensors, is uniquely determined by a closed-form expression. We also derive the closed-form expression for the degrees of freedom (DOFs) of the proposed array. The optimal distribution of the number of sensors in each uniform linear array of the proposed array is given for an arbitrary number of sensors. Compared with the existing sparse arrays, the proposed array can provide a higher number of degrees of freedom and a larger physical array aperture. In addition, to improve the calculation speed of the fourth-order cumulant matrix, we simplify the FOC matrix by removing some redundancy. Numerical simulations are conducted to verify the superiority of the S-FL-NA over other sparse arrays. [ABSTRACT FROM AUTHOR]

The optical properties of a layered turbid material were associated with the reflectance on the surface in the context of the diffusion theory. Photon flux paths were used to describe the escape of photons through the diffusive medium. Results of the study show that internal optically differentiable structure can be assessed by photon flux measurements on the surface of the medium. This method separates the effect of the top layerfrom the effect of the bottom layer of tissue on the measured photons.