Wide Area Search Research Articles

A Search for Faint Resolved Galaxies Beyond the Milky Way in DES Year 6: A New Faint, Diffuse Dwarf Satellite of NGC 55

We report results from a systematic wide-area search for faint dwarf galaxies at heliocentric distances from 0.3 to 2 Mpc using the full 6 yr of data from the Dark Energy Survey (DES). Unlike previous searches over the DES data, this search specifically targeted a field population of faint galaxies located beyond the Milky Way virial radius. We derive our detection efficiency for faint, resolved dwarf galaxies in the Local Volume with a set of synthetic galaxies and expect our search to be complete to M V ∼ (−7, −10) mag for galaxies at D = (0.3, 2.0) Mpc. We find no new field dwarfs in the DES footprint, but we report the discovery of one high-significance candidate dwarf galaxy at a distance of 2.2−0.12+0.05Mpc , a potential satellite of the Local Volume galaxy NGC 55, separated by 47′ (physical separation as small as 30 kpc). We estimate this dwarf galaxy to have an absolute V-band magnitude of −8.0−0.3+0.5mag and an azimuthally averaged physical half-light radius of 2.2−0.4+0.5kpc , making this one of the lowest surface brightness galaxies ever found with μ=32.3magarcsec−2 . This is the largest, most diffuse galaxy known at this luminosity, suggesting possible tidal interactions with its host.

FSSSA: A Fuzzy Squirrel Search Algorithm Based on Wide-Area Search for Numerical and Engineering Optimization Problems

The Squirrel Search Algorithm (SSA) is widely used due to its simple structure and efficient search ability. However, SSA exhibits relatively slow convergence speed and imbalanced exploration and exploitation. To address these limitations, this paper proposes a fuzzy squirrel search algorithm based on a wide-area search mechanism named FSSSA. The fuzzy inference system and sine cosine mutation are employed to enhance the convergence speed. The wide-area search mechanism is introduced to achieve a better balance between exploration and exploitation, as well as improve the convergence accuracy. To evaluate the effectiveness of the proposed strategies, FSSSA is compared with SSA on 24 diverse benchmark functions, using four evaluation indexes: convergence speed, convergence accuracy, balance and diversity, and non-parametric test. The experimental results demonstrate that FSSSA outperforms SSA in all four indexes. Furthermore, a comparison with eight metaheuristic algorithms is conducted to illustrate the optimization performance of FSSSA. The results indicate that FSSSA exhibits excellent convergence speed and overall performance. Additionally, FSSSA is applied to four engineering problems, and experimental verification confirms that it maintains superior performance in realistic optimization problems, thus demonstrating its practicality.

Wide-Area and Real-Time Object Search System of UAV

The method of collecting aerial images or videos by unmanned aerial vehicles (UAVs) for object search has the advantages of high flexibility and low cost, and has been widely used in various fields, such as pipeline inspection, disaster rescue, and forest fire prevention. However, in the case of object search in a wide area, the scanning efficiency and real-time performance of UAV are often difficult to satisfy at the same time, which may lead to missing the best time to perform the task. In this paper, we design a wide-area and real-time object search system of UAV based on deep learning for this problem. The system first solves the problem of area scanning efficiency by controlling the high-resolution camera in order to collect aerial images with a large field of view. For real-time requirements, we adopted three strategies to accelerate the system, as follows: design a parallel system, simplify the object detection algorithm, and use TensorRT on the edge device to optimize the object detection model. We selected the NVIDIA Jetson AGX Xavier edge device as the central processor and verified the feasibility and practicability of the system through the actual application of suspicious vehicle search in the grazing area of the prairie. Experiments have proved that the parallel design of the system can effectively meet the real-time requirements. For the most time-consuming image object detection link, with a slight loss of precision, most algorithms can reach the 400% inference speed of the benchmark in total, after algorithm simplification, and corresponding model’s deployment by TensorRT.

Design of integrated searching and tracking optical systems based on freeform mirrors.

Target searching and tracking photoelectric systems are widely used in civil and military fields. Achieving both efficient search and fine-observation imaging while minimizing the volume and weight of the system is the most concerning problem. In this paper, a dual-mode integrated optical system is proposed, which shares large size components between large field of view (FOV) search module and high-resolution tracking module by using the Biconic Zernike freeform mirrors. Compared with the traditional searching and tracking system utilizing multi-camera matching, this scheme significantly reduces the volume and weight of the remote sensing payload. At the orbit altitude of 500 km, this proposed system has a swath of 87.5 km with a ground resolution of 10 m in search mode. In tracking mode, it can observe an area of 19 km2 with a ground resolution of 1 m. It can meet the requirements of wide-area search and focus tracking simultaneously, providing a feasible scheme for the lightweight design of space optical cameras.

Temporal and non-temporal contextual saliency analysis for generalized wide-area search within unmanned aerial vehicle (UAV) video

Unmanned aerial vehicles (UAV) can be used to great effect for wide-area searches such as search and rescue operations. UAV enable search and rescue teams to cover large areas more efficiently and in less time. However, using UAV for this purpose involves the creation of large amounts of data, typically in video format, which must be analysed before any potential findings can be uncovered and actions taken. This is a slow and expensive process which can result in significant delays to the response time after a target is seen by the UAV. To solve this problem we propose a deep model architecture using a visual saliency approach to automatically analyse and detect anomalies in UAV video. Our Temporal Contextual Saliency (TeCS) approach is based on the state-of-the-art in visual saliency detection using deep Convolutional Neural Networks (CNN) and considers local and scene context, with novel additions in utilizing temporal information through a convolutional Long Short-Term Memory (LSTM) layer and modifications to the base model architecture. We additionally evaluate the impact of temporal vs non-temporal reasoning for this task. Our model achieves improved results on a benchmark dataset with the addition of temporal reasoning showing significantly improved results compared to the state-of-the-art in saliency detection.

Automatic Target Recognition of Millimeter-Wave Radar Based on Deep Learning

All-day, all-weather wide-area search discovery target capability makes radar become a key piece of equipment in many military and civilian fields, and plays an indispensable role in tasks such as identification and positioning. However, at the same time, the current radar equipment generally relies on complex signal processing systems, and designing artificial feature extraction algorithms based on prior knowledge is both difficult and time-consuming, and it is difficult to fine-grained identify the target. In recent years, deep learning has been widely used in the radar field with its strong adaptability and strong self-learning ability. The paper presents a radar Doppler image based on YOLO v3 algorithm. With Millimeter-Wave radar distance-doppler reflectance image as input and darknet-53 as the feature extraction network, the automatic detection of vehicle millimeter wave radar cars, bicycles, pedestrians and trucks was realized, and the average detection accuracy reached 84.3%, providing new ideas and technical support for the development of intelligent radar and radar target detection.

The future of autonomous underwater vehicle control

The future of autonomous underwater vehicle control

Parameter Design and Performance Evaluation of a Large-Swath and High-Resolution Space Camera.

A rotary-scan space camera with an area sensor can achieve large width and high-resolution imaging. Designing system parameters properly is important for the application of the rotary-scan space camera. We model the swath, resolution, and overlap rate between frames for such a camera. An optimum algorithm combining the linear weighting method and the Monte Carlo method for system parameter design is proposed based on the model. Then, the performance of the designed system is evaluated using the grid point method. The designed systems can achieve swaths of more than 1000 km and less than 1 m resolution without leakage during the imaging. In the evaluation, the designed system can cover 82.13% of the observation region at the height of 500 km in 6.5 min, and the average repeated observation frequency is 3.26 times per 118 s. The design method is simple and effective in the initial design of the rotary-scan space camera’s system parameters. The system designed can provide “no-leakage and wide coverage by quick scan” and “high-frequency repeated observation over a long visibility period.” This will greatly improve earth observation ability in wide-area search and rescue missions.

Collaborative mission optimization for ship rapid search by multiple heterogeneous remote sensing satellites

<p style='text-indent:20px;'>Multiple heterogeneous satellites mission optimization is a typical kind of non-deterministic polynomial-time hard (NP-hard) problem, and some complicated scenarios bring new challenges. A novel method of missing ship rapid search using multiple grouped heterogeneous satellites is introduced in this paper. The focus is on optimization of collaborative mission optimization for various satellites including low-earth orbit (LEO) satellite and geostationary orbit (GEO) satellites. A fast coverage of the wide sea area using imaging satellites with narrow coverage range has become the most important part to tackle this problem. However, due to different imaging mechanisms of heterogeneous satellites and other constraints, it brings a great challenge to construct the optimization model. A constrained optimization problem model considering the cooperation between LEO and GEO satellites is constructed. A solution strategy based on bi-level metaheuristic algorithm is designed. The time optimal solution of the collaborative task planning between LEO and GEO satellites can be obtained based on the optimal attitude maneuver path of GEO satellites. Thus, wide-area search for missing ships can be conducted in an effective way. The effectiveness of the proposed method is verified by an example.</p>

RETRACTED ARTICLE: Temporal and non-temporal contextual saliency analysis for generalized wide-area (GWA) search within UAV video

Unmanned aerial vehicles (UAVs) can be used to great effect for wide-area searches such as search and rescue operations. UAV enables search and rescue teams to cover large areas more efficiently and in less time. However, using UAV for this purpose involves the creation of large amounts of data, typically in video format, which must be analyzed before any potential findings can be uncovered and actions taken. This is a slow and expensive process, which can result in significant delays to the response time after a target is seen by the UAV. To solve this problem, a deep model using a visual saliency approach to automatically analyze and detect anomalies in UAV video is proposed. Temporal contextual saliency model is based on the state of the art in visual saliency detection using deep convolutional neural networks and considers local and scene context, with novel additions in utilizing temporal information through a convolutional LSTM layer and modifications to the base model. Additionally, the impact of temporal versus non-temporal reasoning for this task is evaluated. This model achieves improved results on a benchmark dataset with the addition of temporal reasoning showing significantly improved results compared to the state of the art in saliency detection.

Application of a Novel Search Method to Handheld Gamma Radiation Detectors

This work details an innovative method to search a complex enclosed area for a gamma-ray emitting material using a handheld gross-counts gamma detector. It is not a wide area search tool. It relies on triangulating a search area and using the Currie limit of detection to determine the presence of radioactive gamma-ray emitting materials. Results of experimental trials of the model indicate its effectiveness in detecting radiation sources in complex environments. This highlights its potential application in locating radiological material for nuclear security purposes. The method is currently 2-D but is being extended to 3-D.

Wide-Area Search Tracking for Siamese Region Proposal Network

With the introduction of deep learning technology into the field of visual tracking, the accuracy and robustness of visual tracking have greatly improved. Recently, trackers based on Siamese region proposal networks have attracted a great deal of attention because of their favorable performance, especially when faced with challenges of heavy target deformation and out-of-plane rotation. However, strategies of local-area search and using a fixed template degrade the performance of such networks in the long-term tracking. In this paper, we focus on a wide-area search tracking approach with adaptive template updating for a Siamese region proposal network. By embedding a correlation filter module into the Siamese region proposal networks, a structure of moving anchors distribution is designed to make the anchor regression centered around the target. Through adaptive reliability evaluation and online template update, the discriminative performance of the model improves greatly. Furthermore, in order to address the problem of being misled by false positives, a multi-trajectory tracking mechanism is joined to consistently boost the classification ability. Experiments on OTB100 and VOT short-term benchmarks and UAV123 long-term benchmark show that our tracker significantly outperforms the original algorithm and achieves comparable performance compared with other state-of-the-art trackers.

Trajectory Optimization for High-Altitude Long-Endurance UAV Maritime Radar Surveillance

For an unmanned aerial vehicle (UAV) carrying out a maritime radar surveillance mission, there is a tradeoff between maximizing information obtained from the search area and minimizing fuel consumption. This article presents an approach for the optimization of a UAV's trajectory for maritime radar wide area persistent surveillance to simultaneously minimize fuel consumption, maximize mean probability of detection, and minimize mean revisit time. Quintic polynomials are used to generate UAV trajectories due to their ability to provide complete and complex solutions while requiring few inputs. Furthermore, the UAV dynamics and surveillance mission requirements are used to ensure that a trajectory is realistic and mission compatible. A wide area search radar model is used within this article in conjunction with a discretized grid in order to determine the search area's mean probability of detection and mean revisit time. The trajectory generation method is then used in conjunction with a multiobjective particle swarm optimization algorithm to obtain a global optimum in terms of path, airspeed (and thus time), and altitude. The performance of the approach is then tested over two common maritime surveillance scenarios and compared to an industry recommended baseline.

Multi-Task Regression-Based Learning for Autonomous Unmanned Aerial Vehicle Flight Control Within Unstructured Outdoor Environments

Increased growth in the global Unmanned Aerial Vehicles (UAV) (drone) industry has expanded possibilities for fully autonomous UAV applications. A particular application which has in part motivated this research is the use of UAV in wide area search and surveillance operations in unstructured outdoor environments. The critical issue with such environments is the lack of structured features that could aid in autonomous flight, such as road lines or paths. In this paper, we propose an End-to-End Multi-Task Regression-based Learning approach capable of defining flight commands for navigation and exploration under the forest canopy, regardless of the presence of trails or additional sensors (i.e. GPS). Training and testing are performed using a software in the loop pipeline which allows for a detailed evaluation against state-of-the-art pose estimation techniques. Our extensive experiments demonstrate that our approach excels in performing dense exploration within the required search perimeter, is capable of covering wider search regions, generalises to previously unseen and unexplored environments and outperforms contemporary state-of-the-art techniques.

Plug-and-play radiation sensor components for unmanned aerial system platform

Mobile radiation sensing techniques for surge deployment and wide area search are important for nuclear security applications. Radiation sensors were developed for the use with robotic platforms of unmanned aerial systems (UAS). Ambient temperature neutron and gamma CLYC sensor and high resolution CZT gamma sensor were integrated into the UAS as plug-and-play interchangeable components allowing easy attachment to a robot in field conditions. The time and position data were added via Robot Operating System to measured intensities of photon peaks and neutron rates enabling the input for the UAS flight control. The contour mapping of a radiation area by multiple UAS was simulated.

Frequency diverse array radar signal and data processing

The frequency diverse array (FDA) radar is re-explored with a special focus on how the FDA data should be correctly and efficiently processed to maximise its full potential and capacity of search and detection. Some features, such as its extraordinary Doppler tolerance and ability of resolving range ambiguity without using multiple pulse repetition frequencies, are first demonstrated. The extension of non-linear and random frequency increments is proposed in order to suppress range sidelobes and increase range resolution. Wide-area search and detection is achieved by processing the same received data set without a need of electronic scan and repeat pulse transmission and reception. This not only greatly simplifies the radar design and operation but also makes radar quieter and stealth. The limitation of the FDA radar is its sacrificed detection range. Therefore, in comparison with the conventional phased-array radar, the detection area is fan-area-like for the former and pencil-beam-like for the latter, making two radars (or radar operation modes) complementary.

Low-illumination image denoising method for wide-area search of nighttime sea surface

In order to suppress complex mixing noise in low-illumination images for wide-area search of nighttime sea surface, a model based on total variation (TV) and split Bregman is proposed in this paper. A fidelity term based on L1 norm and a fidelity term based on L2 norm are designed considering the difference between various noise types, and the regularization mixed first-order TV and second-order TV are designed to balance the influence of details information such as texture and edge for sea surface image. The final detection result is obtained by using the high-frequency component solved from L1 norm and the low-frequency component solved from L2 norm through wavelet transform. The experimental results show that the proposed denoising model has perfect denoising performance for artificially degraded and low-illumination images, and the result of image quality assessment index for the denoising image is superior to that of the contrastive models.

Scalable gamma-ray camera for wide-area search based on silicon photomultipliers array.

Portable coded-aperture imaging systems based on scintillators and semiconductors have found use in a variety of radiological applications. For stand-off detection of weakly emitting materials, large volume detectors can facilitate the rapid localization of emitting materials. We describe a scalable coded-aperture imaging system based on 5.02 × 5.02 cm2 CsI(Tl) scintillator modules, each partitioned into 4 × 4 × 20 mm3 pixels that are optically coupled to 12 × 12 pixel silicon photo-multiplier (SiPM) arrays. The 144 pixels per module are read-out with a resistor-based charge-division circuit that reduces the readout outputs from 144 to four signals per module, from which the interaction position and total deposited energy can be extracted. All 144 CsI(Tl) pixels are readily distinguishable with an average energy resolution, at 662 keV, of 13.7% FWHM, a peak-to-valley ratio of 8.2, and a peak-to-Compton ratio of 2.9. The detector module is composed of a SiPM array coupled with a 2 cm thick scintillator and modified uniformly redundant array mask. For the image reconstruction, cross correlation and maximum likelihood expectation maximization methods are used. The system shows a field of view of 45° and an angular resolution of 4.7° FWHM.

航天相机环扫成像模式设计

In order to meet the needs of wide area search using agile satellite, the aerospace camera circular scanning imaging model was designed. Based on analyzing the principle of the circular scanning imaging, the optimal ground track was designed and the relationship between the critical spin speed of the satellite and the orbital velocity, the critical circular scanning coefficient, the overlap rate was analyzed, the circular scanning model was established. At the same time, the exposure time, frame rate and other imaging parameters were determined based on the ground track design. The imaging model by STK software was simulated, and the imaging geometric parameters were analyzed. The result shows that when the orbital height H is 500 km, the pixel size a is 4 m, the focal length f is 1 m and the axial pixel number M is 50 000, the ground sample distance and swath width increase gradually with the increase of the camera inclination , when equals to 10, 20, 30, 40, the swath width raises to 1.96, 3.10, 4.58, 6.85 times compared with nadir imaging.

Oviposition behaviour of Eucryptorrhynchus brandti (Coleoptera: Curculionidae: Cryptorrhychinae) on Ailanthus altissima (Mill.) Swingle (Sapindales: Simaroubaceae)

ABSTRACTEucryptorrhynchus brandti Harold is a trunk-boring pest of tree-of-heaven Ailanthus altissima (Mill.) Swingle in China, and is being studied in quarantine in the United States as a potential biological control agent against the tree-of-heaven. In this study, oviposition behaviour of E. brandti was observed with a macro lens camera to record and describe the successive stages of oviposition. Oviposition of E. brandti consists of nine main sequential steps, sometimes followed by grooming: wide-area search, site location, oviposition cavity excavation, excitation, turning, locating the oviposition cavity, egg deposition, egg concealment and resting. Gravid females mainly lay eggs by day, with no obvious oviposition observed by night. Excavation is the longest stage of the process, and the probability of egg laying increases if excavation time exceeds 28 min. There is no obvious preference in the post-drilling turning direction (left or right) during oviposition. This is the first complete description of oviposition behaviour of E. brandti. The positive correlation between time spent excavation and probability of oviposition means that excavation time can be used to predict the success or failure of an egg-laying female.