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In this paper, we present a novel four-dimensional (4D) parameter estimation method to localize the mixed far-field (FF) and near-field (NF) targets using bistatic MIMO arrays and higher-order singular value decomposition (HOSVD). The estimated four parameters include the angle-of-departure (AOD), angle-of-arrival (AOA), range-of-departure (ROD), and range-of-arrival (ROA). In the method, we store array data in a tensor form to preserve the inherent multidimensional properties of the array data. First, the observation data are arranged into a third-order tensor and its covariance tensor is calculated. Then, the HOSVD of the covariance tensor is performed. From the left singular vector matrices of the corresponding module expansion of the covariance tensor, the subspaces with respect to transmit and receive arrays are obtained, respectively. The AOD and AOA of the mixed FF and NF targets are estimated with signal-subspace, and the ROD and ROA of the NF targets are achieved using noise-subspace. Finally, the estimated four parameters are matched via a pairing method. The Cramér–Rao lower bound (CRLB) of the mixed target parameters is also derived. The numerical simulations demonstrate the superiority of the tensor-based method. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of bearing-only target localization with sensor bias contamination. To enhance the system's observability, inspired by plant phototropism, we propose a control barrier function (CBF)-based method for UAV motion planning. The rank criterion provides only qualitative observability results. We employ the condition number for a quantitative analysis, identifying key influencing factors. After that, a multi-objective, nonlinear optimization problem for UAV trajectory planning is formulated and solved using the proposed Nonlinear Constrained Multi-Objective Gray Wolf Optimization Algorithm (NCMOGWOA). Simulations validate our approach, showing a threefold reduction in the condition number, significantly enhancing observability. The algorithm outperforms others in terms of localization accuracy and convergence, achieving the lowest Generational Distance (GD) (7.3442) and Inverted Generational Distance (IGD) (8.4577) metrics. Additionally, we explore the effects of the CBF attenuation rates and initial flight path angles. [ABSTRACT FROM AUTHOR]

Doppler through-wall radar faces two challenges when locating targets concealed behind walls: (1) precisely determining the instantaneous frequency of the target within the frequency aliasing region and (2) reducing the impact of the wall on positioning by determining accurate wall parameters. To address these issues, this paper introduces a target localization algorithm that combines the Hough transform and support vector regression-BP neural network. First, a multiview fusion model framework is proposed for through-wall target detection, which enables the auxiliary estimation of wall parameter information by acquiring target positions from different perspectives. Second, a high-precision extraction and estimation algorithm for the instantaneous frequency curve of the target is proposed by combining the differential evolutionary algorithm and Chebyshev interpolation polynomials. Finally, a target motion trajectory compensation algorithm based on the Back Propagation (BP) neural network is proposed using the estimated wall parameter information, which suppresses the distorting effect of obstacles on target localization results and achieves the accurate localization of the target behind a wall. Experimental results indicate that compared with the conventional short-time Fourier method, the developed algorithm can accurately extract target instantaneous frequency curves within the time-frequency aliasing region. Moreover, it successfully reduces the impact caused by walls, facilitating the precise localization of multiple targets behind walls, and the overall localization accuracy is improved ～85%.

Simple Summary: Breast cancer typically originates and metastasizes from the mammary fat pad. The tumor within the fat pad is supported by enriched lymphatic vessels, to a greater degree than that of blood vessels. Recently, we have successfully co-formulated two physically disparate cancer drugs, water-soluble gemcitabine (G) and water-insoluble paclitaxel (T), into a drug combination nanoparticle (DcNP) referred to as GT-in-DcNP. This GT-in-DcNP dosage form is stable and scalable. When it is given to mice, it enables synchronized delivery of GT into tumors via enriched lymph vessels. A single dose of GT-in-DcNP in mice has been found to suppress breast tumor growth in the mammary fat pad more effectively than an equivalent dose of the free GT combination. We observed tumor regression and restoration of mammary fat tissue at higher doses of GT-in-DcNP. This concept has been extended to demonstrate the ability to suppress human breast cancer in a xenograft model. With these observations, GT-in-DcNP may be considered for clinical development to treat breast cancer. Early diagnosis, intervention, and therapeutic advancements have extended the lives of breast cancer patients; however, even with molecularly targeted therapies, many patients eventually progress to metastatic cancer. Recent data suggest that residual breast cancer cells often reside in the lymphatic system before rapidly spreading through the bloodstream. To address this challenge, an effective drug combination composed of gemcitabine (G) and paclitaxel (T) is administered intravenously in sequence at the metastatic stage, but intravenous GT infusion may limit lymphatic GT drug accessibility and asynchronous drug exposure in cancer cells within the lymph. To determine whether co-localization of intracellular gemcitabine and paclitaxel (referred to as GT) could overcome these limitations and enhance the efficacy of GT, we have evaluated a previously reported GT drug-combination formulated in nanoparticle (referred to as GT-in-DcNP) evaluated in an orthotopic breast tumor model. Previously, with indocyanine green-labeled nanoparticles, we reported that GT-in-DcNP particles after subcutaneous dosing were taken up rapidly and preferentially into the lymph instead of blood vessels. The pharmacokinetic study showed enhanced co-localization of GT within the tumors and likely through lymphatic access, before drug apparency in the plasma leading to apparent long-acting plasma time-course. The mechanisms may be related to significantly greater inhibitions of tumor growth—by 100 to 140 times—in both sub-iliac and axillary regions compared to the equivalent dosing with free-and-soluble GT formulation. Furthermore, GT-in-DcNP exhibited dose-dependent effects with significant tumor regression. In contrast, even at the highest dose of free GT combination, only a modest tumor growth reduction was notable. Preliminary studies with MDA-231-HM human breast cancer in an orthotopic xenograft model indicated that GT-in-DcNP may be effective in suppressing human breast tumor growth. Taken together, the synchronized delivery of GT-in-DcNP to mammary tumors through the lymphatic system offers enhanced cellular retention and greater efficacy. [ABSTRACT FROM AUTHOR]

The measurement of the target azimuth angle using forward-looking radar (FLR) is widely applied in unmanned systems, such as obstacle avoidance and tracking applications. This paper proposes a semi-supervised support vector regression (SVR) method to solve the problem of small sample learning of the target angle with FLR. This method utilizes function approximation to solve the problem of estimating the target angle. First, SVR is used to construct the function mapping relationship between the echo and the target angle in beamspace. Next, by adding manifold constraints to the loss function, supervised learning is extended to semi-supervised learning, aiming to improve the small sample adaptation ability. This framework supports updating the angle estimating function with continuously increasing unlabeled samples during the FLR scanning process. The numerical simulation results show that the new technology has better performance than model-based methods and fully supervised methods, especially under limited conditions such as signal-to-noise ratio and number of training samples. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A doppler radar holds promising prospects in the field of indoor target localization. However, traditional Doppler radar systems suffer from high power consumption, large size, and noticeable radio frequency interference issues when transmitting carriers of different frequencies. Therefore, an ISABO-based (improved subtraction-average-based optimizer) target localization algorithm for a single-frequency Doppler radar is proposed in this paper. Firstly, a mathematical model for target localization is established according to the spatial geometric relationships during the target movement and the Doppler frequency-shift information in the single-frequency echo signal. Then, the optimization function is constructed with the target motion error as the optimization goal. Finally, an improved subtraction-average-based optimizer algorithm is proposed to solve the optimal parameters and realize the target positioning. The experimental results show that the proposed method can achieve centimeter-level localization accuracy with a cost-effective system structure. [ABSTRACT FROM AUTHOR]

Purpose: The overall distribution pattern of intramuscular nerves and the regions with the highest spindle abundance in deep cervical muscles have not been revealed. This study aimed to reveal neuromuscular compartmentalization and localize the body surface position and depth of the center of the region of highest muscle spindle abundance (CRHMSA) in the deep cervical muscles. Methods: This study included 36 adult cadavers (57.7 ± 11.5 years). The curved line joining the lowest point of the jugular notch and chin tip was designated as the longitudinal reference line (line L), and the curved line connecting the lowest point of the jugular notch and acromion was designated as the horizontal reference line (line H). Modified Sihler's staining, hematoxylin-eosin staining and computed tomography scanning were employed to determine the projection points (P) of the CRHMSAs on the anterior surfaces of the neck. The positions (PH and PL) of point P projected onto the H and L lines, and the depth of each CRHMSA, and puncture angle were determined using the Syngo system. Results: The scalenus posterior and longus capitis muscles were divided into two neuromuscular compartments, while the scalenus anterior and longus colli muscles were divided into three neuromuscular compartments. The scalenus medius muscle can be divided into five neuromuscular compartments. The PH of the CRHMSA of the scalenus muscles (anterior, medius, and posterior), and longus capitis and longus colli muscles, were located at 36.27, 39.18, 47.31, 35.67, and 42.71% of the H line, respectively. The PL positions were at 26.53, 32.65, 32.73, 68.32, and 51.15% of the L line, respectively. The depths of the CRHMSAs were 2.47 cm, 2.96 cm, 2.99 cm, 3.93 cm, and 3.17 cm, respectively, and the puncture angles were 87.13°, 85.92°, 88.21°, 58.08°, and 77.75°, respectively. Conclusion: Present research suggests that the deep cervical muscles can be divided into neuromuscular compartments; we recommend the locations of these CRHMSA as the optimal target for administering botulinum toxin A injections to treat deep cervical muscle dystonia. [ABSTRACT FROM AUTHOR]

Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions. [ABSTRACT FROM AUTHOR]

The application of non-invasive brain stimulation (NIBS) in non-human primates (NHPs) is critical for advancing understanding of brain networks and developing treatments for neurological diseases. Improving the precision of targeting can significantly enhance the efficacy of these interventions. Here, we introduce a 3D-printed helmet designed to achieve repeatable and precise neuromodulation targeting in awake rhesus monkeys, eliminating the need of head fixation. Imaging studies confirmed that the helmet consistently targets the primary motor cortex (M1) with a margin of error less than 1 mm. Evaluations of stimulation efficacy revealed high resolution and stability. Additionally, physiological evaluations under propofol anesthesia showed that the helmet effectively facilitated the generation of recruitment curves for motor area, confirming successful neuromodulation. Collectively, our findings present a straightforward and effective method for achieving consistent and precise NIBS targeting in awake NHPs, potentially advancing both basic neuroscience research and the development of clinical neuromodulation therapies.

Reverberation in real environments is an important factor affecting the high resolution of target sound source localization (SSL) methods. Broadband low-frequency signals are common in real environments. This study focuses on the localization of this type of signal in reverberant environments. Because the time reversal (TR) method can overcome multipath effects and realize adaptive focusing, it is particularly suitable for SSL in a reverberant environment. On the basis of the significant advantages of the sparse Bayesian learning algorithm in the estimation of wave direction, a novel SSL is proposed in reverberant environments. First, the sound propagation model in a reverberant environment is studied and the TR focusing signal is obtained. We then use the sparse Bayesian framework to locate the broadband low-frequency sound source. To validate the effectiveness of the proposed method for broadband low-frequency targeting in a reverberant environment, simulations and real data experiments were performed. The localization performance under different bandwidths, different numbers of microphones, signal-to-noise ratios, reverberation times, and off-grid conditions was studied in the simulation experiments. The practical experiment was conducted in a reverberation chamber. Simulation and experimental results indicate that the proposed method can achieve satisfactory spatial resolution in reverberant environments and is robust. [ABSTRACT FROM AUTHOR]

The centre of the highest region of muscle spindle abundance (CHRMSA) in the intramuscular nerve‐dense region has been suggested as the optimal target location for injecting botulinum toxin A to block muscle spasms. The anterior forearm muscles have a high incidence of spasticity. However, the CHRMSA in the intramuscular nerve‐dense region of the forearm anterior muscle group has not been defined. This study aimed to accurately define the body surface position and the depth of CHRMSA in an intramuscular nerve‐dense region of the anterior forearm muscles. Twenty‐four adult cadavers (57.7 ± 11.5 years) were included in this study. The curved line close to the skin connecting the medial and lateral epicondyles of the humerus was designated as the horizontal reference line (H line), and the line connecting the medial epicondyle of the humerus and the ulnar styloid was defined as the longitudinal reference line (L line). Modified Sihler's staining, haematoxylin–eosin staining and computed tomography scanning were employed to determine the projection points (P and P′) of the CHRMSAs on the anterior and posterior surfaces of the forearm. The positions (PH and PL) of point P projected onto the H and L lines, and the depth of each CHRMSA, were determined using the Syngo system. The PH of the CHRMSA of the ulnar head of pronator teres, humeral head of pronator teres, flexor carpi radialis, palmaris longus, flexor carpi ulnaris, ulnar part of flexor digitorum superficialis, radial part of flexor digitorum superficialis, flexor pollicis longus, ulnar part of flexor digitorum profundus, radial portion of flexor digitorum profundus and pronator quadratus muscles were located at 42.48%, 45.52%, 41.20%, 19.70%, 7.77%, 25.65%, 47.42%, 53.47%, 12.28%, 38.41% and 51.68% of the H line, respectively; the PL were located at 18.38%, 12.54%, 28.83%, 13.43%, 17.65%, 32.76%, 57.32%, 64.12%, 20.05%, 45.94% and 88.71% of the L line, respectively; the puncture depths were located at 21.92%, 27.25%, 23.76%, 18.04%, 15.49%, 31.36%, 26.59%, 41.28%, 38.72%, 45.14% and 53.58% of the PP' line, respectively. The percentage values are the means of individual values. We recommend that the body surface puncture position and depth of the CHRMSA are the preferred locations for the intramuscular injection of botulinum toxin A to block anterior forearm muscle spasms. [ABSTRACT FROM AUTHOR]

This paper presents a simple and fast closed-form solution approach for two-dimensional (2D) target localization using range difference (RD) measurements. The formulation of the localization problem is derived using a pentagram array. The target position is determined using passive radar measurements (RDs) between the target and the ( N + 1 = 10 ) receivers' locations. The method facilitates the problem of target position and can be used as a counter-parallel method for spherical interpolation (SI) and spherical intersection (SX) methods in time difference of arrival (TDOA) and radar systems. The performance of the method is examined in 2D target localization using numerical analysis under the distribution of receivers in the pentagram array. The simulations are conducted using four different far-distance targets and comparatively large-area distributed receivers. The RD measurements were distorted by two different values of Gaussian errors based on ionosphedriec time delays of 20 and 50 nsec owing to the different receivers' positions. The findings highly verified the validity of the method for addressing the problem of target localization. Additionally, a theoretical accuracy study of the method is given, which solely relies on the RD measurements. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel four-dimensional (4D) parameter estimation method to localize the mixed far-field (FF) and near-field (NF) targets using bistatic MIMO arrays and higher-order singular value decomposition (HOSVD). The estimated four parameters include the angle-of-departure (AOD), angle-of-arrival (AOA), range-of-departure (ROD), and range-of-arrival (ROA). In the method, we store array data in a tensor form to preserve the inherent multidimensional properties of the array data. First, the observation data are arranged into a third-order tensor and its covariance tensor is calculated. Then, the HOSVD of the covariance tensor is performed. From the left singular vector matrices of the corresponding module expansion of the covariance tensor, the subspaces with respect to transmit and receive arrays are obtained, respectively. The AOD and AOA of the mixed FF and NF targets are estimated with signal-subspace, and the ROD and ROA of the NF targets are achieved using noise-subspace. Finally, the estimated four parameters are matched via a pairing method. The Cramér–Rao lower bound (CRLB) of the mixed target parameters is also derived. The numerical simulations demonstrate the superiority of the tensor-based method.

In recent years, the elderly population has increased, leading to a labor shortage and the increasing cost of training experienced labor. Owing to the continuous optimization of machine vision, multi-sensor technologies, control methods, and end-effector structures, harvesting robots have experienced rapid development. However, most harvesting robots still require intelligent solutions, and the lack of integration with artificial intelligence limits them to small-scale applications without mass production. This paper reviews key technologies for vision-based sensing and control of harvesting robots, focusing on potential applications of vision for target recognition and localization in complex agricultural environments, describing improved solutions for different target detection and localization algorithms, and comparing their detection results. The challenges and future trends of applying these key vision sensing and control techniques in harvesting robots are also described and discussed in this review. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Target detection on the surface of water using wireless sensor networks (WSNs) should be able to collect information as much as possible. The previous studies are only able to collect speed and direction of the target. In addition to previous studies, we collect not only speed and direction but also real time location of the target. In this paper, we propose a target localization algorithm based on Time Different of Arrival (TDoA). The proposed algorithm takes into account grid topology of sensors because it is more tractable. The algorithm is validated using experiments to compute the mean average error (MAE); in these experiments, the estimated and real location coordinates were compared. The experimental results achieved the smallest MAE at 0.25 m. The experimental results indicated that the MAE is inversely proportional to the distance between the sensors. Moreover, the MAE of the x-axis is always greater than that of the y-axis is well explained using the principle of the Abbe error. [ABSTRACT FROM AUTHOR]

Target localization is influenced by the presence of additionally presented nontargets, termed landmarks. In both the visual and tactile modality, these landmarks led to systematic distortions of target localizations often resulting in a shift toward the landmark. This shift has been attributed to averaging the spatial memory of both stimuli. Crucially, everyday experiences often rely on multiple modalities, and multisensory research suggests that inputs from different senses are optimally integrated, not averaged, for accurate perception, resulting in more reliable perception of cross-modal compared with uni-modal stimuli. As this could also lead to a reduced influence of the landmark, we wanted to test whether landmark distortions would be reduced when presented in a different modality or whether landmark distortions were unaffected by the modalities presented. In two experiments (each n = 30) tactile or visual targets were paired with tactile or visual landmarks. Experiment 1 showed that targets were less shifted toward landmarks from the different than the same modality, which was more pronounced for tactile than for visual targets. Experiment 2 aimed to replicate this pattern with increased visual uncertainty to rule out that smaller localization shifts of visual targets due to low uncertainty had led to the results. Still, landmark modality influenced localization shifts for tactile but not visual targets. The data pattern for tactile targets is not in line with memory averaging but seems to reflect the effects of multisensory integration, whereas visual targets were less prone to landmark distortions and do not appear to benefit from multisensory integration. NEW & NOTEWORTHY In the present study, we directly tested the predictions of two different accounts, namely, spatial memory averaging and multisensory integration, concerning the degree of landmark distortions of targets across modalities. We showed that landmark distortions were reduced across modalities compared to distortions within modalities, which is in line with multisensory integration. Crucially, this pattern was more pronounced for tactile than for visual targets. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of bearing-only target localization with sensor bias contamination. To enhance the system’s observability, inspired by plant phototropism, we propose a control barrier function (CBF)-based method for UAV motion planning. The rank criterion provides only qualitative observability results. We employ the condition number for a quantitative analysis, identifying key influencing factors. After that, a multi-objective, nonlinear optimization problem for UAV trajectory planning is formulated and solved using the proposed Nonlinear Constrained Multi-Objective Gray Wolf Optimization Algorithm (NCMOGWOA). Simulations validate our approach, showing a threefold reduction in the condition number, significantly enhancing observability. The algorithm outperforms others in terms of localization accuracy and convergence, achieving the lowest Generational Distance (GD) (7.3442) and Inverted Generational Distance (IGD) (8.4577) metrics. Additionally, we explore the effects of the CBF attenuation rates and initial flight path angles.

The measurement of the target azimuth angle using forward-looking radar (FLR) is widely applied in unmanned systems, such as obstacle avoidance and tracking applications. This paper proposes a semi-supervised support vector regression (SVR) method to solve the problem of small sample learning of the target angle with FLR. This method utilizes function approximation to solve the problem of estimating the target angle. First, SVR is used to construct the function mapping relationship between the echo and the target angle in beamspace. Next, by adding manifold constraints to the loss function, supervised learning is extended to semi-supervised learning, aiming to improve the small sample adaptation ability. This framework supports updating the angle estimating function with continuously increasing unlabeled samples during the FLR scanning process. The numerical simulation results show that the new technology has better performance than model-based methods and fully supervised methods, especially under limited conditions such as signal-to-noise ratio and number of training samples.

Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions.

A doppler radar holds promising prospects in the field of indoor target localization. However, traditional Doppler radar systems suffer from high power consumption, large size, and noticeable radio frequency interference issues when transmitting carriers of different frequencies. Therefore, an ISABO-based (improved subtraction-average-based optimizer) target localization algorithm for a single-frequency Doppler radar is proposed in this paper. Firstly, a mathematical model for target localization is established according to the spatial geometric relationships during the target movement and the Doppler frequency-shift information in the single-frequency echo signal. Then, the optimization function is constructed with the target motion error as the optimization goal. Finally, an improved subtraction-average-based optimizer algorithm is proposed to solve the optimal parameters and realize the target positioning. The experimental results show that the proposed method can achieve centimeter-level localization accuracy with a cost-effective system structure.

Current sensor technologies facilitate device-free and non-invasive monitoring of target activities and infrastructures to ensure a safe and inhabitable environment. Device-free techniques for sensing the surrounding environment are an emerging area of research where a target does not need to carry or attach any device to provide information about its motion or the surrounding environment. Consequently, there has been an increasing interest in device-free sensing. Seismic sensors are extremely effective tools for gathering target motion information. In this paper, we provide a comprehensive overview of the seismic sensor-based devicefree sensing process and highlight the key techniques within the research field.We classify the existing literature into three categories, viz., (i) target detection, (ii) target localization, and (iii) target identification, and activity recognition. The techniques in each category are divided into multiple subcategories in a structured manner to comprehensively discuss the details.We also discuss the challenges associated with contemporary cutting-edge research and suggest potential solutions. [ABSTRACT FROM AUTHOR]

Onboard visual object tracking in unmanned aerial vehicles (UAVs) has attractedmuch interest due to its versatility. Meanwhile, due to high precision, Siamese networks are becoming hot spots in visual object tracking. However, most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs. To meet the tracking precision and real-time requirements, this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL. Specifically, the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network, then performs correlationmatching to obtain the candidate regionwith high similarity. To improve the matching effect of template and search features, this paper designs a dense pixel-level feature fusion module to enhance the matching ability by pixel-wise correlation and enrich the feature diversity by dense connection. An attention module composed of self-attention and channel attention is introduced to learn global context information and selectively emphasize the target feature region in the spatial and channel dimensions. In addition, a target localization module is designed to improve target location accuracy. Compared with other advanced trackers, experiments on two public benchmarks, which are UAV123@10fps and UAV20L fromthe unmanned air vehicle123 (UAV123) dataset, show that SiamDPL can achieve superior performance and low complexity with a running speed of 100.1 fps on NVIDIA TITAN RTX. [ABSTRACT FROM AUTHOR]

The phenotypic characteristics of soybean leaves are of great significance for studying the growth status, physiological traits, and response to the environment of soybeans. The segmentation model for soybean leaves plays a crucial role in morphological analysis. However, current baseline segmentation models are unable to accurately segment leaves in soybean leaf images due to issues like leaf overlap. In this paper, we propose a target leaf segmentation model based on leaf localization and guided segmentation. The segmentation model adopts a two-stage segmentation framework. The first stage involves leaf detection and target leaf localization. Based on the idea that a target leaf is close to the center of the image and has a relatively large area, we propose a target leaf localization algorithm. We also design an experimental scheme to provide optimal localization parameters to ensure precise target leaf localization. The second stage utilizes the target leaf localization information obtained from the first stage to guide the segmentation of the target leaf. To reduce the dependency of the segmentation results on the localization information, we propose a solution called guidance offset strategy to improve segmentation accuracy. We design multiple guided model experiments and select the one with the highest segmentation accuracy. Experimental results demonstrate that the proposed model exhibits strong segmentation capabilities, with the highest average precision (AP) and average recall (AR) reaching 0.976 and 0.981, respectively. We also compare our segmentation results with current baseline segmentation models, and multiple quantitative indicators and qualitative analysis indicate that our segmentation results are better. [ABSTRACT FROM AUTHOR]

Marine search and rescue (MSR) is considered the last line of defense for human life at sea. Recently, a prospective MSR strategy based on wireless sensor networks (WSNs) has been developed, and distress-stricken individuals can be located utilizing various localization methods. Nevertheless, the accuracy cannot satisfy the requirement of related departments, especially when employing a single measurement localization technique, such as received signal strength (RSS)-based technology, in a dynamic and complicated ocean environment. To this end, a scheme inspired by information fusion is developed, which incorporates RSS and time of arrival (TOA) information. The maximum likelihood (ML)-based localization problem is then converted into a hybrid measurement alternative nonnegative constrained least squares (HM-ANCLS) framework. Moreover, the paper develops a two-step linearization localization approach (TLLA) to determine the target location. The first step proposes a slight computation method (SCM) that relies on an active set approach to address the framework. In the second step, the paper presents an error correction approach based on the first-order Taylor series expansion to refine the solution. In addition, the paper conducts the Cramér-Rao low bound (CRLB) and the computational complexity of the hybrid scheme. Simulations reveal that TLLA outperforms other state-of-the-art approaches in various situations.

Reverberation in real environments is an important factor affecting the high resolution of target sound source localization (SSL) methods. Broadband low-frequency signals are common in real environments. This study focuses on the localization of this type of signal in reverberant environments. Because the time reversal (TR) method can overcome multipath effects and realize adaptive focusing, it is particularly suitable for SSL in a reverberant environment. On the basis of the significant advantages of the sparse Bayesian learning algorithm in the estimation of wave direction, a novel SSL is proposed in reverberant environments. First, the sound propagation model in a reverberant environment is studied and the TR focusing signal is obtained. We then use the sparse Bayesian framework to locate the broadband low-frequency sound source. To validate the effectiveness of the proposed method for broadband low-frequency targeting in a reverberant environment, simulations and real data experiments were performed. The localization performance under different bandwidths, different numbers of microphones, signal-to-noise ratios, reverberation times, and off-grid conditions was studied in the simulation experiments. The practical experiment was conducted in a reverberation chamber. Simulation and experimental results indicate that the proposed method can achieve satisfactory spatial resolution in reverberant environments and is robust.

This paper presents a simple and fast closed-form solution approach for two-dimensional (2D) target localization using range difference (RD) measurements. The formulation of the localization problem is derived using a pentagram array. The target position is determined using passive radar measurements (RDs) between the target and the (N+1=10) receivers’ locations. The method facilitates the problem of target position and can be used as a counter-parallel method for spherical interpolation (SI) and spherical intersection (SX) methods in time difference of arrival (TDOA) and radar systems. The performance of the method is examined in 2D target localization using numerical analysis under the distribution of receivers in the pentagram array. The simulations are conducted using four different far-distance targets and comparatively large-area distributed receivers. The RD measurements were distorted by two different values of Gaussian errors based on ionosphedriec time delays of 20 and 50 nsec owing to the different receivers’ positions. The findings highly verified the validity of the method for addressing the problem of target localization. Additionally, a theoretical accuracy study of the method is given, which solely relies on the RD measurements.

Underwater wireless sensor networks (UWSNs) are the primary enabling technology for the Internet of underwater things (IoUT), with which all underwater objects can interact and communicate. In UWSNs, localization is vital for military or civilized applications since data collected without location are meaningless. However, accurate localization using acoustic signals in UWSNs is challenging, especially for received signal strength (RSS)-based techniques. The adverse effect of hybrid loss (path and absorption loss) and stratified propagation may severely impact localization accuracy. Even though some schemes have been proposed in the literature, the accuracy is unsatisfactory. To this end, this study proposes a coarse-to-fine localization method (CFLM). The problem is reformed into an alternating nonnegative constrained least squares (ANCLS) framework, where a constrained ellipse adjustment (CEA) using block principal pivoting is proposed to obtain the coarse estimation. A refined step using a Taylor series expansion is then further presented, in which a corrected solution is acquired by iteration. Additionally, this study derives the Cramér-Rao lower bound (CRLB) to evaluate the proposed method. Simulation results show that the proposed CFLM improves the localization accuracy by up to 66 percent compared with weighted least squares (WLS), privacy-preserving localization (PPSL), two-step linearization localization approach (TLLA), particle swarm optimization-based (PSO) localization, and differential evolution-based (DE) localization under different scenarios.

In this paper, we study the problem of circumnavigating an unknown target with only distance measurements. Because the target is unknown, it is necessary to locate the target before circumnavigation. In order to locate the target, we propose an angle estimator instead of estimating the position of the target directly, therefore the algorithm is simpler and with faster convergence. In order to perform the circumnavigation task, we develop a control protocol with a first-order filter to facilitate the practical application. We give strict theoretical analysis of the proposed theory, and verify the effectiveness of the algorithm through simulations. [ABSTRACT FROM AUTHOR]

Distributed radar systems promise to significantly enhance target localization by virtue of the superiority of multi-view observations from widely separated radars, compared to their monostatic counterparts. Nevertheless, when the radar number is limited, performing target localization bears the brunt of the parameter identifiability requirement that the parameter number must be no less than the number of independent measurements. In this way, the canonical two-stage target localization method, as well as its developments, is no longer appropriate for direct application. Hence, in this paper, we propose a novel target localization method using time-difference-of-arrival (TDOA) measurements with the minimum number of radars under platform position uncertainties. The referred distributed system is a bistatic multi-receiver system, where the primary signal is transmitted by a geostationary Earth orbit (GEO) satellite while receivers are equipped on several unmanned aerial vehicles (UAVs). In the first stage, the reference range from the reference radar to the target is estimated by a quadratic function, and then the weighted least squares (WLS) solution of the target location is updated by substituting the range estimate back into it. In the second stage, we invoke the Taylor series approximation to further refine the target localization obtained by the first stage. It can be foreseen that the developed method is beneficial for scenarios with a limited number of radars, including engineering projects such as fire control, surveillance, and guidance, to support high-accuracy target localization. The simulation results show the superiority of the localization performance of the proposed method over other existing methods. [ABSTRACT FROM AUTHOR]

This paper presents a Track-Before-Detect (TBD) approach to search and localize a radioemitted target in a wide marine environment using just the received signal strength (RSS) measurements. In this problem, the lost target transmits radio signals, and the unmanned aerial vehicle (UAV), guided on a search path, receives the target transmitted signal strength by its mounted antenna. The guidance law directs the UAV to the best detection points where the probability of target detection is maximum. At the same time, the estimation module evolves the posterior distribution of the radio target states, including the target position, heading, and transmitter power. The best detection points are calculated based on this evolved target's states' posterior. The superiority of the proposed method is due to the consideration of the antenna radiation pattern, which is accurately modeled in this paper and ensures the strength of the filter against the uncertainties of the measurement model and the target model. The simulation results validate the performance of the proposed method in the autonomous localization of a lost moving target. [ABSTRACT FROM AUTHOR]

Marine search and rescue (MSR) is considered the last line of defense for human life at sea. Recently, a prospective MSR strategy based on wireless sensor networks (WSNs) has been developed, and distress-stricken individuals can be located utilizing various localization methods. Nevertheless, the accuracy cannot satisfy the requirement of related departments, especially when employing a single measurement localization technique, such as received signal strength (RSS)-based technology, in a dynamic and complicated ocean environment. To this end, a scheme inspired by information fusion is developed, which incorporates RSS and time of arrival (TOA) information. The maximum likelihood (ML)-based localization problem is then converted into a hybrid measurement alternative nonnegative constrained least squares (HM-ANCLS) framework. Moreover, the paper develops a two-step linearization localization approach (TLLA) to determine the target location. The first step proposes a slight computation method (SCM) that relies on an active set approach to address the framework. In the second step, the paper presents an error correction approach based on the first-order Taylor series expansion to refine the solution. In addition, the paper conducts the Cram é r-Rao low bound (CRLB) and the computational complexity of the hybrid scheme. Simulations reveal that TLLA outperforms other state-of-the-art approaches in various situations. [ABSTRACT FROM AUTHOR]

In order to locate the mobile robots in three‐dimensional indoor environment, mostly global navigation satellite system‐denied space, a monocular visual space positioning algorithm based on a deep neural network is proposed. First, the authors employ the lightweight YOLOv5 algorithm for target detection, and the LibTorch deep learning framework is used for model deployment to improve the inference speed. Moreover, a multi‐layer perceptron (MLP) neural network with four inputs and two outputs is constructed, which regress the site coordinates of the robot to complete the target localization, and this method is compared with the mathematical model solving algorithm to reflect the accuracy and superiority of positioning algorithm based on the deep neural network. The proposed positioning and tracking system has been successfully applied to international conference on robotics and automation (ICRA) robot competition, and the results show that the positioning mean error estimated by the authors' method is within 10 cm whilst having good real‐time performance. [ABSTRACT FROM AUTHOR]