Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations

Unmanned aerial vehicles (UAVs) play a primary role in a plethora of technical and scientific fields owing to their wide range of applications. In particular, the provision of emergency services during the occurrence of a crisis event is a vital application domain where such aerial robots can contribute, sending out valuable assistance to both distressed humans and rescue teams. Bearing in mind that time constraints constitute a crucial parameter in search and rescue (SAR) missions, the punctual and precise detection of humans in peril is of paramount importance. The paper in hand deals with real-time human detection onboard a fully autonomous rescue UAV. Using deep learning techniques, the implemented embedded system was capable of detecting open water swimmers. This allowed the UAV to provide assistance accurately in a fully unsupervised manner, thus enhancing first responder operational capabilities. The novelty of the proposed system is the combination of global navigation satellite system (GNSS) techniques and computer vision algorithms for both precise human detection and rescue apparatus release. Details about hardware configuration as well as the system’s performance evaluation are fully discussed.

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Aerial and Ground Robot Collaboration for Autonomous Mapping in Search and Rescue Missions

Drones ◽

10.3390/drones4040079 ◽

2020 ◽

Vol 4 (4) ◽

pp. 79

Author(s):

Dimitrios Chatziparaschis ◽

Michail G. Lagoudakis ◽

Panagiotis Partsinevelos

Keyword(s):

Humanoid Robot ◽

Satellite System ◽

Search And Rescue ◽

Localization Algorithm ◽

Localization And Mapping ◽

Planning Algorithm ◽

Crisis Scenarios ◽

Trained Neural Network ◽

Unknown Area ◽

Global Navigation Satellite

Humanitarian Crisis scenarios typically require immediate rescue intervention. In many cases, the conditions at a scene may be prohibitive for human rescuers to provide instant aid, because of hazardous, unexpected, and human threatening situations. These scenarios are ideal for autonomous mobile robot systems to assist in searching and even rescuing individuals. In this study, we present a synchronous ground-aerial robot collaboration approach, under which an Unmanned Aerial Vehicle (UAV) and a humanoid robot solve a Search and Rescue scenario locally, without the aid of a commonly used Global Navigation Satellite System (GNSS). Specifically, the UAV uses a combination of Simultaneous Localization and Mapping and OctoMap approaches to extract a 2.5D occupancy grid map of the unknown area in relation to the humanoid robot. The humanoid robot receives a goal position in the created map and executes a path planning algorithm in order to estimate the FootStep navigation trajectory for reaching the goal. As the humanoid robot navigates, it localizes itself in the map while using an adaptive Monte-Carlo Localization algorithm by combining local odometry data with sensor observations from the UAV. Finally, the humanoid robot performs visual human body detection while using camera data through a Darknet pre-trained neural network. The proposed robot collaboration scheme has been tested under a proof of concept setting in an exterior GNSS-denied environment.

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GNSS-ISE: Instruction Set Extension for GNSS Baseband Processing

Sensors ◽

10.3390/s20020465 ◽

2020 ◽

Vol 20 (2) ◽

pp. 465 ◽

Cited By ~ 1

Author(s):

Krzysztof Marcinek ◽

Witold A. Pleskacz

Keyword(s):

Flash Memory ◽

Low Cost ◽

Satellite System ◽

Microprocessor System ◽

Instruction Set ◽

Advantages And Disadvantages ◽

Wide Range ◽

Instruction Set Extension ◽

Baseband Processing ◽

Global Navigation Satellite

This work presents the results of research toward designing an instruction set extension dedicated to Global Navigation Satellite System (GNSS) baseband processing. The paper describes the state-of-the-art techniques of GNSS receiver implementation. Their advantages and disadvantages are discussed. Against this background, a new versatile instruction set extension for GNSS baseband processing is presented. The authors introduce improved mechanisms for instruction set generation focused on multi-channel processing. The analytical approach used by the authors leads to the introduction of a GNSS-instruction set extension (ISE) for GNSS baseband processing. The developed GNSS-ISE is simulated extensively using PC software and field-programmable gate array (FPGA) emulation. Finally, the developed GNSS-ISE is incorporated into the first-in-the-world, according to the authors’ best knowledge, integrated, multi-frequency, and multi-constellation microcontroller with embedded flash memory. Additionally, this microcontroller may serve as an application processor, which is a unique feature. The presented results show the feasibility of implementing the GNSS-ISE into an embedded microprocessor system and its capability of performing baseband processing. The developed GNSS-ISE can be implemented in a wide range of applications including smart IoT (internet of things) devices or remote sensors, fostering the adaptation of multi-frequency and multi-constellation GNSS receivers to the low-cost consumer mass-market.

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The Research of Multipath and Linear Error for Pseudolites Applications

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.130-134.2890 ◽

2011 ◽

Vol 130-134 ◽

pp. 2890-2893 ◽

Cited By ~ 1

Author(s):

Xiao Guang Wan ◽

Xing Qun Zhan

Keyword(s):

Carrier Phase ◽

Indoor Environment ◽

Phase Measurement ◽

Satellite System ◽

Independent System ◽

Carrier Phase Measurement ◽

Wide Range ◽

Positioning Method ◽

Global Navigation Satellite ◽

Is Design

Pseudolites are ground-based transmitters that send global navigation satellite system like signals, such as GPS, GLONASS, or Galileo. As an independent system for indoor positioning, pseudolites technique can be explored for a wide range of positioning and navigation application where the signal of satellite GNSS can’t be received. However, with indoor environment, the positioning method of pseudolite navigation system is not entirely same as GNSS, and there are some challenging issues in research and system design. In this paper, a signal difference carrier phase measurement system with pseudolites is design. Furthermore, two major problems are studied that they are multipath error and linear errors.

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SibNet — Siberian Global Navigation Satellite System Network: Current state

Solnechno-Zemnaya Fizika ◽

10.12737/szf-44201809 ◽

2018 ◽

Vol 4 (4) ◽

pp. 82-94 ◽

Cited By ~ 2

Author(s):

Юрий Ясюкевич ◽

Yury Yasyukevich ◽

Артем Веснин ◽

Artem Vesnin ◽

Наталья Перевалова ◽

...

Keyword(s):

Global Navigation Satellite System ◽

Satellite System ◽

General Information ◽

Navigation Satellite ◽

Main Research ◽

Current State ◽

Wide Range ◽

Global Navigation ◽

Using Data ◽

Global Navigation Satellite

In 2011, ISTP SB RAS began to deploy a routinely operating network of receivers of global navigation satellite system signals. To date, eight permanent and one temporal sites in the Siberian region are operating on a regular basis. These nine sites are equipped with 12 receivers. We use nine multi-frequency multi-system receivers of Javad manufacturer, and three specialized receivers NovAtel GPStation-6 designed to measure ionospheric phase and amplitude scintillations. The deployed network allows a wide range of ionospheric studies as well as studies of the navigation system positioning quality under various heliogeophysical conditions. This article presents general information about the network, its technical characteristics, and current state, as well as the main research problems that can be solved using data from the network.

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Progress in Time Series Soil Moisture Retrievals Using the Cyclone Global Navigation Satellite System Mission

10.5194/egusphere-egu21-8572 ◽

2021 ◽

Author(s):

Mohammad Al-Khaldi ◽

Joel Johnson ◽

Scott Gleason

Keyword(s):

Time Series ◽

Soil Moisture ◽

Global Navigation Satellite System ◽

Satellite System ◽

Retrieval Algorithm ◽

Navigation Satellite ◽

Gps Satellites ◽

Wide Range ◽

Global Navigation ◽

Global Navigation Satellite

NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission has continued to provide measurements of land surface specular scattering since its launch in December 2016. CYGNSS&#8217;s operates in a GNSS-R configuration in which &#160;CYGNSS satellites together with GPS satellites form a bistatic radar geometry with GPS satellites acting as transmitters and CYGNSS satellites acting as receivers. The fundamental GNSS-R measurement obtained using the CYGNSS observatories is the delay-Doppler map (DDM), from which normalized radar cross section (NRCS) estimates are derived. The sensitivity of CYGNSS measurements to a wide range of surface properties has motivated their use for soil moisture retrievals.This presentation reports an updated analysis of soil moisture retrieval errors using a previously reported time series soil moisture retrieval algorithm that considers a &#160;multi-year CYGNSS dataset. The presentation also reports recent progress in which further simplifications to the proposed algorithm are introduced that limit its need for ancillary soil moisture data and promote use in an operational capacity. This is accomplished, in part, through the incorporation of a recently developed global Level-1 coherence detection methodology and the use of a soil moisture climatology.Soil moisture is sensed using a time-series retrieval in which NRCS ratios derived from CYGNSS measurements are used to form a system of equations that can be solved for a times series of surface reflectivities. While the NRCS exhibits a dependence on a wide range of properties such as soil moisture, soil composition, vegetation cover, and surface roughness, NRCS ratios in consecutive acquisitions, at sufficiently low latency, exhibit a direct proportionality to reflectivity ratios that are a function of soil permittivity and therefore soil moisture. The dependence of NRCS ratios on reflectivity facilitates a location dependent inversion of reflectivity to soil moisture through a dielectric mixing model. The use of NRCS ratios however results in N-1 equations for the N soil moistures in the time series, thereby necessitating the incorporation of additional information typically expressed in terms of maximum and/or minimum soil moisture (or reflectivity) values over the time series when solving the system. These values can be obtained either from ancillary data from other systems or from a soil moisture climatology as incorporated in this presentation.Retrieved moisture values from the updated algorithm are compared against observed values reported by the Soil Moisture Active Passive (SMAP) mission. The findings suggest that there exists potential for using GNSS-R systems for global soil moisture retrievals with an RMS error on the order of 0.06 cm3/cm3 over varied terrain. The dependence of the algorithm&#8217;s retrieval error on land cover class, soil texture, and moisture variability trends will be reported in detail in this presentation.

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Landslide Prediction Method Based on a Ground-Based Micro-Deformation Monitoring Radar

Remote Sensing ◽

10.3390/rs12081230 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1230

Author(s):

Lin Qi ◽

Weixian Tan ◽

Pingping Huang ◽

Wei Xu ◽

Yaolong Qi ◽

...

Keyword(s):

Prediction Method ◽

Satellite System ◽

Deformation Monitoring ◽

Data Types ◽

Monitoring Equipment ◽

Landslide Prediction ◽

Wide Range ◽

Micro Deformation ◽

The One ◽

Global Navigation Satellite

As remote sensing methods have received a lot of attention, ground-based micro- deformation monitoring radars have been widely used in recent years due to their wide range, high accuracy, and all-day monitoring capability. On the one hand, these monitoring radars break through the limitations of traditional point monitoring equipment such as the Global Navigation Satellite System (GNSS) and fissure meters in terms of monitoring scope and ease of installation. On the other hand, the data types of these monitoring radars are more varied. Therefore, it may be difficult for the data-processing method of traditional point monitoring equipment to take all advantages of this type of radar. In this paper, based on time-series monitoring data of ground-based micro-deformation monitoring radars, three parameters—extent of change (EOC), extent of stability (EOS), and extent of mutation (EOM)—are calculated according to deformation value, coherence and deformation pixels size. Then a method for landslide prediction by combining these three parameters with the inverse velocity method is proposed. The effectiveness of this method is verified by the measured data of a landslide in Yunnan Province, China. The experimental results show that the method can correctly discern deformation areas and provide more accurate monitoring results, especially when the deformation trend changes rapidly. In summary, this method can improve the response rate and prediction accuracy in extreme cases, such as rapid deformation.

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Determination of Cultivated Area, Field Boundary and Overlapping for A Plowing Operation Using ISO 11783 Communication and D-GNSS Position Data

Agriculture ◽

10.3390/agriculture9020038 ◽

2019 ◽

Vol 9 (2) ◽

pp. 38 ◽

Cited By ~ 6

Author(s):

Andreas Heiß ◽

Dimitrios Paraforos ◽

Hans Griepentrog

Keyword(s):

Crop Production ◽

Low Cost ◽

Satellite System ◽

Position Information ◽

Cost Differential ◽

Main Challenge ◽

Wide Range ◽

Position Data ◽

Machine Speed ◽

Global Navigation Satellite

Easily available and detailed area-related information is very valuable for the optimization of crop production processes in terms of, e.g., documentation and invoicing or detection of inefficiencies. The present study dealt with the development of algorithms to gain sophisticated information about different area-related parameters in a preferably automated way. Rear hitch position and wheel-based machine speed were recorded from ISO 11783 communication data during plowing with a mounted reversible moldboard plow. The data were georeferenced using the position information from a low-cost differential global navigation satellite system (D-GNSS) receiver. After the exclusion of non-work sequences from continuous data logs, single cultivated tracks were reconstructed, which represented as a whole the cultivated area of a field. Based on that, the boundary of the field and the included area were automatically detected with a slight overestimation of 1.4%. Different field parts were distinguished and single overlaps between the cultivated tracks were detected, which allowed a distinct assessment of the lateral and headland overlapping (2.05% and 3.96%, respectively). Incomplete information about the work state of the implement was identified as the main challenge to get precise results. With a few adaptions, the used methodology could be transferred to a wide range of mounted implements.

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Classifying Inundation in a Tropical Wetlands Complex with GNSS-R

Remote Sensing ◽

10.3390/rs11091053 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1053 ◽

Cited By ~ 14

Author(s):

Nereida Rodriguez-Alvarez ◽

Erika Podest ◽

Katherine Jensen ◽

Kyle C. McDonald

Keyword(s):

Global Navigation Satellite System ◽

Open Water ◽

Coherent Scattering ◽

Satellite System ◽

Wetland Classification ◽

Navigation Satellite ◽

Tropical Wetlands ◽

Ancillary Data ◽

Global Navigation ◽

Global Navigation Satellite

The use of global navigation satellite system reflectometry (GNSS-R) measurements for classification of inundated wetlands is presented. With the launch of NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission, space-borne GNSS-R measurements have become available over ocean and land. CYGNSS covers latitudes between ±38°, providing measurements over tropical ecosystems and benefiting new studies of wetland inundation dynamics. The GNSS-R signal over inundated wetlands is driven mainly by coherent scattering associated with the presence of surface water, producing strong forward scattering and a distinctive bistatic scattering signature. This paper presents a methodology used to classify inundation in tropical wetlands using observables derived from GNSS-R measurements and ancillary data. The methodology employs a multiple decision tree randomized (MDTR) algorithm for classification and wetland inundation maps derived from the phased-array L-band synthetic aperture radar (PALSAR-2) as reference for training and validation. The development of an innovative GNSS-R wetland classification methodology is aimed to advance mapping of global wetland distribution and dynamics, which is critical for improved estimates of natural methane production. The results obtained in this manuscript demonstrate the ability of GNSS-R signals to detect inundation under dense vegetation over the Pacaya-Samiria Natural Reserve, a tropical wetland complex located in the Peruvian Amazon. Classification results report an accuracy of 69% for regions of inundated vegetation, 87% for open water regions, and 99% for non-inundated areas. Misclassification of inundated vegetation, primarily as non-inundated area, is likely related to the combination of two factors: partial inundation within the GNSS-R scattering area, and signal attenuation from dense overstory vegetation, resulting in a low signal.

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Precise Single-Frequency Positioning Using Low-Cost Receiver with the Aid of Lane-Level Map Matching for Land Vehicle Navigation

Journal of Navigation ◽

10.1017/s0373463320000375 ◽

2020 ◽

pp. 1-14

Author(s):

Fei Liu ◽

Yue Liu ◽

Zhixi Nie ◽

Yang Gao

Keyword(s):

Low Cost ◽

Digital Camera ◽

Satellite System ◽

Single Frequency ◽

Map Matching ◽

Digital Map ◽

The Road ◽

Ambiguity Fixing ◽

Wide Range ◽

Global Navigation Satellite

Precise positioning with low-cost single-frequency global navigation satellite system (GNSS) receivers has great potential in a wide range of applications because of its low price and improved accuracy. However, challenges remain in achieving reliable and accurate solutions using low-cost receivers. For instance, the successful ambiguity fixing rate could be low for real-time kinematic (RTK) while large errors may occur in precise point positioning (PPP) in some scenarios (e.g., trees along the road). To solve the problems, this paper proposes a method with the aid of additional lane-level digital map information to improve the accuracy and reliability of RTK and PPP solutions. In the method, a digital camera will be applied for lane recognition and the positioning solution from a low-cost receiver will be projected to the digital map lane link. With the projected point position as a constraint, the RTK ambiguity fixing rate and PPP performance can be enhanced. A field kinematic test was conducted to verify the improvement of the RTK and PPP solutions with the aid of map matching. The results show that the RTK ambiguity fixing rate can be increased and the PPP positioning error can be reduced by map matching.

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An Aircraft Wetland Inundation Experiment Using GNSS Reflectometry

Remote Sensing ◽

10.3390/rs12030512 ◽

2020 ◽

Vol 12 (3) ◽

pp. 512

Author(s):

Stephen T. Lowe ◽

Clara Chew ◽

Jesal Shah ◽

Michael Kilzer

Keyword(s):

Fresnel Zone ◽

Open Water ◽

Spot Size ◽

Satellite System ◽

Dry Land ◽

Surface Spot ◽

Detailed Assessment ◽

Caddo Lake ◽

Giant Salvinia ◽

Global Navigation Satellite

In early May of 2017, a flight campaign was conducted over Caddo Lake, Texas, to test the ability of Global Navigation Satellite System-Reflectometry (GNSS-R) to detect water underlying vegetation canopies. This paper presents data from that campaign and compares them to Sentinel-1 data collected during the same week. The low-altitude measurement allows for a more detailed assessment of the forward-scattering GNSS-R technique, and at a much higher spatial resolution, than is possible using currently available space-based GNSS-R data. Assumptions about the scattering model are verified, as is the assumption that the surface spot size is approximately the Fresnel zone. The results of this experiment indicate GNSS signals reflected from inundated short, thick vegetation, such as the giant Salvinia observed here, results in only a 2.15 dB loss compared to an open water reflection. GNSS reflections off inundated cypress forests show a 9.4 dB loss, but still 4.25 dB above that observed over dry regions. Sentinel-1 data show a 6-dB loss over the inundated giant Salvinia, relative to open water, and are insensitive to standing water beneath the cypress forests, as there is no difference between the signal over inundated cypress forests and that over dry land. These results indicate that, at aircraft altitudes, forward-scattered GNSS signals are able to map inundated regions even in the presence of dense overlying vegetation, whether that vegetation consists of short plants or tall trees.

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