Spatial Target Research Articles

Target Localization Based on High Resolution Mode of MIMO Radar with Widely Separated Antennas

Coherent processing of multiple-input multiple-output (MIMO) radar with widely separated antennas has high resolution capability, but it also brings ambiguity in target localization. In view of the ambiguity problem, different from other signal processing sub-directions such as array configuration optimization or continuity of phase in space/time, this paper analyzes it from the information level, that is, the tracking method is adopted. First, by using the state equation and measurement equation, the echo data of multiple coherent processing intervals (CPI) are collected to improve the target localization accuracy as much as possible. Second, the non-coherent joint probability data association filter (JPDAF) is used to achieve stable tracking of spatial cross targets without ambiguity measurements. Third, based on the tracking results of the non-coherent JPDAF, the ambiguity of coherent measurement is resolved, that is, the coherent JPDAF is realized. By means of non-coherent and coherent alternating JPDAF (NCCAF) algorithms, high accuracy localization of multiple targets is achieved. Finally, numerical simulations are carried out to validate the effectiveness of the proposed NCCAF algorithm.

Physical model-driven deep networks for through-the-wall radar imaging

AbstractIn order to merge the advantages of the traditional compressed sensing (CS) methodology and the data-driven deep network scheme, this paper proposes a physical model-driven deep network, termed CS-Net, for solving target image reconstruction problems in through-the-wall radar imaging. The proposed method consists of two consequent steps. First, a learned convolutional neural network prior is introduced to replace the regularization term in the traditional iterative CS-based method to capture the redundancy of the radar echo signal. Moreover, the physical model of the radar signal is used in the data consistency layer to encourage consistency with the measurements. Second, the iterative CS optimization is unrolled to yield a deep learning network, where the weight, regularization parameter, and the other parameters are learnable. A quantity of training data enables the network to extract high-dimensional characteristics of the radar echo signal to reconstruct the spatial target image. Simulation results demonstrated that the proposed method can achieve accurate target image reconstruction and was superior to the traditional CS method, in terms of mean squared error and the target texture details.

Spatial targets and payment modes of win–win payments for ecosystem services and poverty reduction

Spatial targets and payment modes are the key components for solving the issue of “where to pay and how to pay” and for supporting ecosystem service providers to achieve sustainable livelihoods and end poverty through payments for ecosystem services (PES) for environmental protection and poverty reduction. Here, we present the Spatial Targets and Sustainable Livelihoods (STSL) framework and analyze regional environmental vulnerability and livelihood assets in the Longnan Municipality of China. We constructed reasonable spatial targeting and payment modes according to each actual type. We classified the present 27 poverty-stricken areas in Longnan Municipality into two levels of environmental vulnerability, four classes in combination with natural capital, eight categories with the addition of human capital, and ten subcategories considering environmental vulnerability and all five livelihood assets. Finally, considering regional development and sustainable livelihoods for poor people, we suggest spatial layout targets for implementing conservation or restoration ecological projects and 10 differentiated payment modes. This study provides an appropriate proposal for certain regions to implement the environmental policy of win–win PES and fills the research gap regarding specific payment modes for PES.

Spatial context target relearning following a target relocation event: Not mission impossible.

Our visual system relies on memory to store and retrieve goal-relevant structures and information from the environment for the purpose of optimizing the allocation of attention. This concept, referred to as contextual cueing, has been demonstrated using visual search tasks, wherein repeated visual contexts lead to reduced search times compared with random displays. Subsequently, when an unexpected change occurs in the environment, or memory fails, a cognitive expense is incurred as the mind tries to resolve the conflict with the memory of the previous environmental context. How memory resolves these conflicts and is updated is of great interest. Previous studies showed that, without extensive practice, individuals were unable to associate a secondary target location with a previously learned spatial context following the relocation of the initially learned target. Here, we explored variables that could potentially affect contextual learning and relearning, such as display size, crowding, context color, and whether the target switched to a previously occupied or unoccupied location. In a series of four experiments, we find relearning occurring in all instances. Previous research may have suffered from underpowered designs.

A Fast and Accurate Spatial Target Snapping Method for 3D Scene Modeling and Mapping in Mobile Augmented Reality

High-performance spatial target snapping is an essential function in 3D scene modeling and mapping that is widely used in mobile augmented reality (MAR). Spatial data snapping in a MAR system must be quick and accurate, while real-time human–computer interaction and drawing smoothness must also be ensured. In this paper, we analyze the advantages and disadvantages of several spatial data snapping algorithms, such as the 2D computational geometry method and the absolute distance calculation method. To address the issues that existing algorithms do not adequately support 3D data snapping and real-time snapping of high data volumes, we present a new adaptive dynamic snapping algorithm based on the spatial and graphical characteristics of augmented reality (AR) data snapping. Finally, the algorithm is experimented with by an AR modeling system, including the evaluation of snapping efficiency and snapping accuracy. Through the experimental comparison, we found that the algorithm proposed in this paper is substantially improved in terms of shortening the snapping time, enhancing the snapping stability, and improving the snapping accuracy of vector points, lines, faces, bodies, etc. The snapping efficiency of the algorithm proposed in this paper is 1.6 times higher than that of the traditional algorithm on average, while the data acquisition accuracy based on the algorithm in this paper is more than 6 times higher than that of the traditional algorithm on average under the same conditions, and its data accuracy is improved from the decimeter level to the centimeter level.

MLSS-VO: A Multi-Level Scale Stabilizer with Self-Supervised Features for Monocular Visual Odometry in Target Tracking

In this study, a multi-level scale stabilizer intended for visual odometry (MLSS-VO) combined with a self-supervised feature matching method is proposed to address the scale uncertainty and scale drift encountered in the field of monocular visual odometry. Firstly, the architecture of an instance-level recognition model is adopted to propose a feature matching model based on a Siamese neural network. Combined with the traditional approach to feature point extraction, the feature baselines on different levels are extracted, and then treated as a reference for estimating the motion scale of the camera. On this basis, the size of the target in the tracking task is taken as the top-level feature baseline, while the motion matrix parameters as obtained by the original visual odometry of the feature point method are used to solve the real motion scale of the current frame. The multi-level feature baselines are solved to update the motion scale while reducing the scale drift. Finally, the spatial target localization algorithm and the MLSS-VO are applied to propose a framework intended for the tracking of target on the mobile platform. According to the experimental results, the root mean square error (RMSE) of localization is less than 3.87 cm, and the RMSE of target tracking is less than 4.97 cm, which demonstrates that the MLSS-VO method based on the target tracking scene is effective in resolving scale uncertainty and restricting scale drift, so as to ensure the spatial positioning and tracking of the target.

Non-Contact Ultralow Rotational Speed Measurement of Real Objects Based on Rotational Doppler Velocimetry

Rotational speed measurement has increasingly become an important requirement both in the industrial and aerospace fields. This article presents a technique for rotational speed measurement based on the non-contact optical method by using superposition vortex light. To the best of our knowledge, for the first time, we realized the ultralow rotational speed measurement as low as 0.001 r/s with high accuracy based on real rotating objects. By using an optical vortex (OV) with a large topological charge up to ±65, the rotational frequency is magnified significantly. The detection system proposed in this article has a simple structure and non-contact features, including a transmitter, a receiver end, and a control and signal processing unit. Depending on the rotational speed of the target, the different sampling time is used to achieve near real-time speed acquisition. The experimental results suggest that the measurement result of the rotational speed has nothing to do with the receive angle of the receiver, and different receive distances only affect the intensity of the signal. An autocorrelation algorithm is designed which can effectively improve the signal-to-noise ratio of the detection signal. At different rotation speeds, the experimental results are in good agreement with the theoretical value. Our work fills the margin of the ultralow rotational speed measurement of RDE, and the related technique in this article may be useful in the precision mechanical testing and spatial instability targets sensing.

NOAA-20 VIIRS On-Orbit Geometric Data Quality Estimation Using the Scheduled Lunar Collections

The second Visible Infrared Imaging Radiometer Suite (VIIRS) instrument aboard the National Oceanic and Atmospheric Administration (NOAA) 20 satellite has been successfully operating since its launch on November 18, 2017. Since VIIRS does not include onboard calibrators to perform the on-orbit geometric data quality characterization for parameters such as the modulation transfer function (MTF) and band-to-band registration (BBR), the monthly scheduled lunar observations are used in this study. The radiometric property of the moon surface has demonstrated its long-term stability and it is also a suitable spatial target for the on-orbit calibration of remote sensing instruments. Using the commonly practiced methodologies, the VIIRS BBR results are derived in the scan and track directions. The initial trends show that the VIIRS BBR is very stable on-orbit within ±0.1 pixels in both scan and track directions meeting the highest requirements of within 0.2 pixels. Using the sharp edge of the moon, the scan-direction MTF at the Nyquist frequency was approximately 0.23. The MTF values are well above the specification of 0.3 in imaging (I)-bands and they are very stable over the study period, whereas the moderate-resolution (M)-bands results were slightly below the specification line as suggested by prelaunch test results. The scan-direction MTF estimations were consistently near 0.2 over four years of operations. Track-direction MTF values showed oscillations because of the annual cycle of lunar shadow angle and spatial features in the moon side. However, the track-direction MTF values met the specification with large margin.

Coprime Sensing for Airborne Array Interferometric SAR Tomography

In airborne array interferometric SAR (Array-InSAR) tomography, the measurements acquired by conventional uniform sampling array are always restricted by the number of physical baseline elements and the size of baseline aperture. It is desirable to capture new acquisitions and enlarge the aperture with virtual signal processing instead of actually adding array baselines. For this motivation, we utilize the disparity of a pair of coprime sampling sub-arrays to enlarge the baseline aperture and construct new observations virtually. The generation of virtual measurements is equal to estimating cross-correlation matrices in real SAR data. Due to the spatial target variation, we adopted an adaptive filtering method to estimate the cross-correlation matrix. We call the above-mentioned processing of generating virtual measurements as a <i>coprime sensing technique</i>. The newly generated virtual measurements have more degrees of freedom, a larger baseline aperture, and a higher signal-to-noise ratio (SNR) than the physical measurements. These advantages offer the possibility to obtain competitive three-dimensional (3-D) imaging results without increasing the hardware cost of the Array-InSAR. We demonstrate the effectiveness of the proposed method by the coprime acquisitions selected from AIRCAS Array-InSAR data.

Graph Sample and Aggregate-Attention Network for Hyperspectral Image Classification

Graph convolutional network (GCN) has shown potential in hyperspectral image (HSI) classification. However, GCN is a transductive learning method, which is difficult to aggregate the new node. The available GCN-based methods fail to understand the global and contextual information of the graph. To address this deficiency, a novel semisupervised network based on graph sample and aggregate-attention (SAGE-A) for HSIs’ classification is proposed. Different from the GCN-based method, SAGE-A adopts a multilevel graph sample and aggregate (graphSAGE) network, as it can flexibly aggregate the new neighbor node among arbitrarily structured non-Euclidean data and capture long-range contextual relations. Inspired by the convolution neural network (CNN) self-attention mechanism, the proposed network uses the graph attention mechanism to characterize the importance among spatially neighboring regions, so the deep contextual and global information of the graph can be learned automatically by focusing on important spatial targets. Extensive experimental results on different real hyperspectral data sets demonstrate the performances of our proposed method compared with the state-of-the-art methods.

Joint Spectrum, Carrier, and DOA Estimation With Beamforming MWC Sampling System

The joint estimation of the spectrum, carrier, and direction of arrival (DOA) is significant in radar, sonar, wireless communications, and cognitive radio systems. The traditional parameter measurement methods based on the Shannon&#x2013;Nyquist theorem require very high sampling rates, which put a lot of pressure on sampling, processing, and storage devices. In this article, novel beamforming modulated wideband converter (BMWC) system for joint estimation of the spectrum, carrier, and DOA is proposed to improve the robustness of estimation and reduce structural complexity. From the spatial signal model of array MWC, combined with the spatial filtering and signal enhancement characteristics of adaptive beamforming, the spatial target signals are focused and separated to enhance the robustness of the system. Due to the spatial filtering of beamforming, the MWC structure only needs to reconstruct one signal at a time. The number of channels of MWC connected to each antenna gets rid of the dependence on the number of signals. We utilize the MWC channel expansion technology, which makes it possible to reconstruct the signal well even when each receiving antenna is only connected to a single MWC channel. Compared with previous works, the proposed system enhances the sparsity property of the signals and greatly simplifies the MWC structure. Simulation and hardware experimental results demonstrate the effectiveness and robustness of the proposed method.

Comparison of Spinal Cord Stimulation vs. Dorsal Root Ganglion Stimulation vs. Association of Both in Patients with Refractory Chronic Back and/or Lower Limb Neuropathic Pain: An International, Prospective, Randomized, Double-Blinded, Crossover Trial (BOOST-DRG Study).

While spinal cord stimulation (SCS) is a well-established therapy to address refractory persistent spinal pain syndrome after spinal surgery (PSPS-T2), its lack of spatial selectivity and reported discomfort due to positional effects can be considered as significant limitations. As alternatives, new waveforms, such as burst stimulation and different spatial neural targets, such as dorsal root ganglion stimulation (DRGS), have shown promising results. Comparisons between DRGS and standard SCS, or their combination, have never been studied on the same patients. “BOOST DRG” is the first prospective, randomized, double-blinded, crossover study to compare SCS vs. DRGS vs. SCS+DRGS. Sixty-six PSPS-T2 patients will be recruited internationally in three centers. Before crossing over, patients will receive each stimulation modality for 1 month, using tonic conventional stimulation. After 3 months, stimulation will consist in switching to burst for 1 month, and patients will choose which modality/waveform they receive and will then be reassessed at 6 and 12 months. In addition to our primary outcome based on pain rating, this study is designed to assess quality of life, functional disability, psychological distress, pain surface coverage, global impression of change, medication quantification, adverse events, brain functional imaging and electroencephalography, with the objective being to provide a multidimensional insight based on composite pain assessment.

Towards low-carbon cities: Patch-based multi-objective optimization of land use allocation using an improved non-dominated sorting genetic algorithm-II

The rational land use allocation is of great significance to the construction of low-carbon cities. The optimization model of land use allocation is an important tool that helps urban planners to quantitatively trade-off among the multi-objectives and achieve optimal land use schemes. For multi-objective optimization of low-carbon land use allocation, the models conducted by existing studies generally tend to be based on gridded data, lack of comprehensive consideration of quantitative and spatial objectives, and efficient algorithms to execute the optimization process. Therefore, this paper proposed a patch-based low carbon multi-objective land use allocation (LC-MLUA) optimization model involving both quantitative and spatial optimization targets. The LC-MLUA optimization model was solved with an improved non-dominated sorting genetic algorithm-II (NSGA-II), and the weighted-sum method was used to make the final selection under different preferences. The LC-MLUA optimization model was then applied to a case study of Changxing, a county-level city in east China, and there were three key results. (1) The LC-MLUA optimization model had a remarkable outperform of the land use allocation than the original land use plan, and the optimized values of economic benefit, emission reduction, and accessibility increased by 27.0%, 6.2% and 8.3%, respectively. (2) The LC-MLUA optimization model generated a series of optimal schemes to support suggestion-making for the low-carbon adjustment of the land use structure and spatial layout. (3) The LC-MLUA optimization model based on vector land patch data was proved more efficient as the unit number was reduced by 5 times than gridded data and better reflected the land use planning practice. (4) Compared with other algorithms, the improved NSGA-II had better performance in the number of solutions, target optimization rate, and comprehensive performance. Based on these results, it suggests that the patch-based LC-MLUA optimization model method can provide good technical support for low-carbon land use planning, and can be flexibly applied to other cities.

Place names in Spanish Republican Life Stories: spatial patterns in locations and perceptions

Abstract. Places can be defined by different features including geographical coordinates, landscape elements and also subjective information like perception. The corpus collected by the Réseau des acteurs de l’histoire et la mémoire de l’immigration (RAHMI) contains oral interviews about Spanish Republicans journeys during Spanish Civil War and World War II. These life stories quote place names corresponding to proper name (Npr) or common name (Nc) and which can be qualified or associated to non-geographical nouns, adjectives and expressions. Methods from natural language processing have been used to automatically extract and characterize these pieces of informations into annotations. The objective of the presented work is to analyse the geographical aspects of places and associated features, and to propose cartographical displays illustrating life stories and supporting analysis. Distribution of quoted Npr place names was first studied. It enabled targeting relevant spatial scales and so mapping extents for the corpus. Second, features of Npr places about their type (administrative, topographic) and their spatial target based on assocations with Nc have been synthetized. Then perception were allocated to Npr, falling into the three values of polarity positive, negative or neutral. Spatial patterns of each value have been displayed and interpreted. Summary of the annotations, analysis and created maps offer insights into the corpus driven by the mentioned places. Based on individual testimonies, it may help further interpretation and researches about this historical period.

Effect of reward on electrophysiological signatures of grid cell population activity in human spatial navigation

The regular equilateral triangular periodic firing pattern of grid cells in the entorhinal cortex is considered a regular metric for the spatial world, and the grid-like representation correlates with hexadirectional modulation of theta (4–8 Hz) power in the entorhinal cortex relative to the moving direction. However, researchers have not clearly determined whether grid cells provide only simple spatial measures in human behavior-related navigation strategies or include other factors such as goal rewards to encode information in multiple patterns. By analysing the hexadirectional modulation of EEG signals in the theta band in the entorhinal cortex of patients with epilepsy performing spatial target navigation tasks, we found that this modulation presents a grid pattern that carries target-related reward information. This grid-like representation is influenced by explicit goals and is related to the local characteristics of the environment. This study provides evidence that human grid cell population activity is influenced by reward information at the level of neural oscillations.

Rotational dynamics in motor cortex are consistent with a feedback controller.

Recent studies have identified rotational dynamics in motor cortex (MC), which many assume arise from intrinsic connections in MC. However, behavioral and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory feedback and interactions with other brain areas contribute substantially to MC processing. We investigated these apparently conflicting theories by building recurrent neural networks that controlled a model arm and received sensory feedback from the limb. Networks were trained to counteract perturbations to the limb and to reach toward spatial targets. Network activities and sensory feedback signals to the network exhibited rotational structure even when the recurrent connections were removed. Furthermore, neural recordings in monkeys performing similar tasks also exhibited rotational structure not only in MC but also in somatosensory cortex. Our results argue that rotational structure may also reflect dynamics throughout the voluntary motor system involved in online control of motor actions.

A Point Cloud Fusion Method for Space Target 3D Laser Point Cloud and Visible Light Image Reconstruction Point Cloud

In this paper, a high-precision spatial target lidar for 3D reconstruction of point cloud and visible light image 3D reconstruction point cloud fusion method is proposed. This fusion method uses the solved 3D reconstruction to invert the camera pose and 3D point cloud model centroid position optimization initial value selection. The registration accuracy and registration efficiency of the ICP algorithm are improved. At the same time, according to the characteristics of the two sets of point clouds, the Euclidean distance threshold is used to delete the noise points of the 3D point cloud edge, and the high-precision 3D reconstruction point with the scale information fusion is obtained. cloud. The simulation experiment of the spatial target simulation model shows that the fusion method can effectively improve the point cloud density, fill the reconstruction vulnerability, and improve the point cloud accuracy of the spatial target 3D reconstruction.

DNA methylation atlas of the mouse brain at single-cell resolution

Mammalian brain cells show remarkable diversity in gene expression, anatomy and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. Here we carry out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single-nucleus DNA methylation sequencing1,2 to profile 103,982 nuclei (including 95,815 neurons and 8,167 non-neuronal cells) from 45 regions of the mouse cortex, hippocampus, striatum, pallidum and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, we constructed an artificial neural network model that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data3 enabled prediction of high-confidence enhancer–gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments4. By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse cerebrum.

Functional coupling between target selection and acquisition in the superior colliculus.

Survival in unpredictable environments requires that animals continuously evaluate their surroundings for behavioral targets, direct their movements toward those targets, and terminate movements once a target is reached. The ability to select, move toward, and acquire spatial targets depends on a network of brain regions, but it remains unknown how these goal-directed processes are linked by neural circuits. Within this network, common circuits in the midbrain superior colliculus (SC) mediate the selection and initiation of movements to spatial targets. However, SC activity often persists throughout movement, suggesting that the same SC circuits underlying target selection and movement initiation may also contribute to "target acquisition": stopping the movement at the selected target. Here, we examine the hypothesis that SC functional circuitry couples target selection and acquisition using a "default motor plan" generated by selection-related neuronal activity. Recordings from intermediate and deep layer SC neurons in mice performing a spatial choice task demonstrate that choice-predictive neurons, including optogenetically identified GABAergic neurons whose activity mediates target selection, exhibit increased activity during movement to the target. By recording from rostral and caudal SC in separate groups of mice, we also revealed higher activity in rostral than caudal neurons during target acquisition. Finally, we used an attractor model to examine how-invoking only SC circuitry-caudal SC activity related to selecting an eccentric target could generate higher rostral than caudal acquisition-related activity. Overall, our results suggest a functional coupling between SC circuits for target selection and acquisition, elucidating a key mechanism for goal-directed behavior.NEW & NOTEWORTHY How do neural circuits ensure that selected targets are successfully acquired? Here, we examine whether choice-related activity in the superior colliculus (SC) promotes a motor plan for target acquisition. By demonstrating that choice-predictive SC neurons-including GABAergic neurons-remain active throughout movement, while the activity of rostral SC neurons increases during acquisition, and by recapitulating these dynamics with an attractor model, our results support a role for SC circuits in coupling target selection and acquisition.

Multi-scale attentive region adaptive aggregation learning for remote sensing scene classification

ABSTRACT Remote sensing scene classification (RSSC) is an active topic in the field of remote sensing and has attracted a lot of attention due to its wide range of applications. Deep learning methods, especially Convolutional neural networks (CNN), significantly improve the performance of RSSC due to their strong feature extraction capabilities. However, the complicated spatial layout and diverse target distribution of remote sensing images make RSSC challenging. Current CNN usually tends to describe the global semantics of high-level features of images, but the extraction of local semantics, multi-scale features and anisotropic contextual features in remote sensing images needs to be enhanced to cope with the above challenges. To this end, an end-to-end hybrid structure, namely multi-scale attentive region adaptive aggregation (MARAA) learning is proposed, which makes full use of the rich semantic information of deep convolutional features and the high robustness of local adaptive aggregation. First, we extract spatial feature maps based on different layers of CNN, so that our feature extractor can learn multi-scale semantic representations. Second, an attention-enhanced local adaptive aggregation learning strategies is designed to aggregate the spatial features of each scale. Not only the dual attention is utilized to enhance the semantic features of local regions but also the local regions is divided into groups and the different orders of spatial adaptive aggregation learning based on hierarchical attention is designed to explore arbitrary contexts of local semantics. Subsequently, a context gating mechanism of sparse fusion is proposed to merge the adaptive aggregation features of local semantics of different scale spaces, so as to explore the advantages of cross-scale feature fusion. Finally, experiments on five publicly available RSSC benchmarks show that the classification performance of our MARAA significantly outperforms many state-of-the-art methods by capturing deep adaptive internal correlations of multi-scale attentive regions of the image.