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15 pages, 1362 KiB

Open AccessArticle

Emitter Location Using Frequency Difference of Arrival Measurements Only

by Mohamed Khalaf-Allah

Sensors 2022, 22(24), 9642; https://doi.org/10.3390/s22249642 - 09 Dec 2022

Cited by 1 | Viewed by 2613

It is desirable to enable emitter location using frequency difference of arrival (FDoA) measurements only, since many signals are characterized by coarse range resolution and fine Doppler resolution. For instance, while using the cross-ambiguity function (CAF) to measure the time difference of arrival [...] Read more.

It is desirable to enable emitter location using frequency difference of arrival (FDoA) measurements only, since many signals are characterized by coarse range resolution and fine Doppler resolution. For instance, while using the cross-ambiguity function (CAF) to measure the time difference of arrival (TDoA) and the FDoA of a narrowband signal, it is difficult to obtain accurate TDoA measurements because the Doppler resolution is higher than the range resolution. Grid-based and sample-based algorithms are developed to solve the two-dimensional (2D) emitter location problem, where the solution space is approximated, respectively, by generating deterministic and random emitter location candidates. Simulation results corroborate the viability of both non-iterative algorithms to estimate the emitter location using a single-time snapshot of FDoA measurements only, without any prior location information or any knowledge about the distribution of measurement errors. The achieved accuracies are sufficient for early warning purposes, preparing defenses, and cueing more accurate location sensors by directing additional surveillance resources. Full article

(This article belongs to the Special Issue Multi-Radio and/or Multi-Sensor Integrated Navigation System)

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27 pages, 7444 KiB

Open AccessArticle

An Experimental Assessment of People’s Location Efficiency Using Low-Energy Communications-Based Movement Tracking

by Marius Minea

Sensors 2022, 22(22), 9025; https://doi.org/10.3390/s22229025 - 21 Nov 2022

Viewed by 1417

Abstract

(1) Background: public transport demand dynamics represents important information for fleet managers and is also a key factor in making public transport attractive to reduce the environmental footprint of urban traffic. This research presents some experimental results on the assessment of low-energy communication [...] Read more.

(1) Background: public transport demand dynamics represents important information for fleet managers and is also a key factor in making public transport attractive to reduce the environmental footprint of urban traffic. This research presents some experimental results on the assessment of low-energy communication technologies, such as Wi-Fi and Bluetooth, as support for people density and/or movement tracking sensing technologies. (2) Methods: the research is based on field measurements to determine the percentage of discoverable devices carried by people, in relation to the total number of physical persons in interest, different scenarios of mobile devices usage and evaluation of influences on radio signals’ propagation, RSSI / RX read values, and efficiency of indoor localization, or in similar GPS-denied environments. Different situations are investigated, especially public transport-related ones, such as subway stations, indoors of commuting hubs, railway stations and trains. (3) Results: diagrams and experiments are presented, and models of signal behavior are also proposed. (4) Conclusions: recommendations on the efficiency of these non-conventional traveler and passenger flow tracking solutions and models are presented at the end of the paper. Full article

(This article belongs to the Special Issue Multi-Radio and/or Multi-Sensor Integrated Navigation System)

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16 pages, 4307 KiB

Open AccessArticle

Mid-State Kalman Filter for Nonlinear Problems

by Zhengwei Liu, Ying Chen and Yaobing Lu

Sensors 2022, 22(4), 1302; https://doi.org/10.3390/s22041302 - 09 Feb 2022

Cited by 2 | Viewed by 1636

Abstract

When tracking very long-range targets, wide-band radars capable of measuring targets with high precision at ranges have severe measurement nonlinearities. The existing nonlinear filtering technology, such as the extended Kalman filter and untracked Kalman filter, will have significant consistency problems and loss in [...] Read more.

When tracking very long-range targets, wide-band radars capable of measuring targets with high precision at ranges have severe measurement nonlinearities. The existing nonlinear filtering technology, such as the extended Kalman filter and untracked Kalman filter, will have significant consistency problems and loss in tracking accuracy. A novel mid-state Kalman filter is proposed to avoid loss and preserve the filtering consistency. The observed state and its first-order state derivative are selected as the mid-state vector. The update process is transformed into the measurement space to ensure the Gaussian measurement distribution and the linearization of the measurement equation. In order to verify the filter performance in comparison, an iterative formulation of Cramér-Rao Low Bound for the nonlinear system is further derived and given in this paper. Simulation results show that the proposed method has excellent performance of high filtering accuracy and fast convergence by comparing the filter state estimation accuracy and consistency. Full article

(This article belongs to the Special Issue Multi-Radio and/or Multi-Sensor Integrated Navigation System)

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26 pages, 18088 KiB

Open AccessArticle

Real-Time Georeferencing of Fire Front Aerial Images Using Iterative Ray-Tracing and the Bearings-Range Extended Kalman Filter

by Bernardo Santana, El Khalil Cherif, Alexandre Bernardino and Ricardo Ribeiro

Sensors 2022, 22(3), 1150; https://doi.org/10.3390/s22031150 - 02 Feb 2022

Cited by 3 | Viewed by 2454

Abstract

Although Aerial Vehicle images are a viable tool for observing large-scale patterns of fires and their impacts, its application is limited by the complex optical georeferencing procedure due to the lack of distinctive visual features in forest environments. For this reason, an exploratory [...] Read more.

Although Aerial Vehicle images are a viable tool for observing large-scale patterns of fires and their impacts, its application is limited by the complex optical georeferencing procedure due to the lack of distinctive visual features in forest environments. For this reason, an exploratory study on rough and flat terrains was conducted to use and validate the Iterative Ray-Tracing method in combination with a Bearings-Range Extended Kalman Filter as a real-time forest fire georeferencing and filtering algorithm on images captured by an aerial vehicle. The Iterative Ray-Tracing method requires a vehicle equipped with a Global Positioning System (GPS), an Inertial Measurement Unit (IMU), a calibrated camera, and a Digital Elevation Map (DEM). The proposed method receives the real-time input of the GPS, IMU, and the image coordinates of the pixels to georeference (computed by a companion algorithm of fire front detection) and outputs the geographical coordinates corresponding to those pixels. The Unscented Transform B is proposed to characterize the Iterative Ray-Tracing uncertainty. A Bearings-Range filter measurement model is introduced in a sequential filtering architecture to reduce the noise in the measurements, assuming static targets. A performance comparison is done between the Bearings-Only and the Bearings-Range observation models, and between the Extended and Cubature Kalman Filters. In simulation studies with ground truth, without filtering we obtained a georeferencing Root Mean Squared Errors (RMSE) of 30.7 and 43.4 m for the rough and flat terrains respectively, while filtering with the proposed Bearings-Range Extended Kalman Filter showed the best results by reducing the previous RMSE to 11.7 and 19.8 m, respectively. In addition, the comparison of both filter algorithms showed a good performance of Bearings-Range filter which was slightly faster. Indeed, these experiments based on the real data conducted to results demonstrated the applicability of the proposed methodology for the real-time georeferencing forest fires. Full article

(This article belongs to the Special Issue Multi-Radio and/or Multi-Sensor Integrated Navigation System)

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19 pages, 3665 KiB

Open AccessArticle

A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks

by Yuh-Shyan Chen, Chih-Shun Hsu and Ren-Shao Chung

Sensors 2022, 22(3), 776; https://doi.org/10.3390/s22030776 - 20 Jan 2022

Cited by 7 | Viewed by 1718

Abstract

Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization in 2D [...] Read more.

Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization in 2D environment. However, the kernel learning has not been used in 3D environment. Hence, this paper proposes a multi-kernel learning scheme for 3D indoor localization. Based on the signals collected in the area of interest, the WiFi signals with better quality and closer to the user are selected so as to reduce the multipath effect and the external interference. Through the construction of multi-kernel, the localization accuracy can be improved as opposed to the localization based on the single kernel. We build multiple kernels to get the user’s location by collecting wireless received signal strengths (RSS) and signal-to-noise ratios (SNR). The kernel learning maps data to high dimension space and uses the optimization process to find the surface where the data are mapped. By multi-kernel training, the surface is fine-tuned and eventually converges to form the location database during the mapping process. The proposed localization scheme is verified by the real RSS and SNR collected from multiple wireless access points (AP) in a building. The experimental results verify that the proposed multi-kernel learning scheme performs better than the multi-DNN scheme and the existing kernel-based localization schemes in terms of localization accuracy and error in 3D indoor environment. Full article

(This article belongs to the Special Issue Multi-Radio and/or Multi-Sensor Integrated Navigation System)

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19 pages, 6256 KiB

Open AccessArticle

Automatic Identification System (AIS) Dynamic Data Integrity Monitoring and Trajectory Tracking Based on the Simultaneous Localization and Mapping (SLAM) Process Model

by Krzysztof Jaskólski, Łukasz Marchel, Andrzej Felski, Marcin Jaskólski and Mariusz Specht

Sensors 2021, 21(24), 8430; https://doi.org/10.3390/s21248430 - 17 Dec 2021

Cited by 8 | Viewed by 3253

Abstract

To enhance the safety of marine navigation, one needs to consider the involvement of the automatic identification system (AIS), an existing system designed for ship-to-ship and ship-to-shore communication. Previous research on the quality of AIS parameters revealed problems that the system experiences with [...] Read more.

To enhance the safety of marine navigation, one needs to consider the involvement of the automatic identification system (AIS), an existing system designed for ship-to-ship and ship-to-shore communication. Previous research on the quality of AIS parameters revealed problems that the system experiences with sensor data exchange. In coastal areas, littoral AIS does not meet the expectations of operational continuity and system availability, and there are areas not covered by the system. Therefore, in this study, process models were designed to simulate the tracking of vessel trajectories, enabling system failure detection based on integrity monitoring. Three methods for system integrity monitoring, through hypotheses testing with regard to differences between model output and actual simulated vessel positions, were implemented, i.e., a Global Positioning System (GPS) ship position model, Dead Reckoning and RADAR Extended Kalman Filter (EKF)—Simultaneous localization and mapping (SLAM) based on distance and bearing to navigational aid. The designed process models were validated on simulated AIS dynamic data, i.e., in a simulated experiment in the area of Gdańsk Bay. The integrity of AIS information was determined using stochastic methods based on Markov chains. The research outcomes confirmed the usefulness of the proposed methods. The results of the research prove the high level (~99%) of integrity of the dynamic information of the AIS system for Dead Reckoning and the GPS process model, while the level of accuracy and integrity of the position varied depending on the distance to the navigation aid for the RADAR EKF-SLAM process model. Full article

(This article belongs to the Special Issue Multi-Radio and/or Multi-Sensor Integrated Navigation System)

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16 pages, 7604 KiB

Open AccessArticle

Uncertainty Estimation of Dense Optical Flow for Robust Visual Navigation

by Yonhon Ng, Hongdong Li and Jonghyuk Kim

Sensors 2021, 21(22), 7603; https://doi.org/10.3390/s21227603 - 16 Nov 2021

Viewed by 2005

Abstract

This paper presents a novel dense optical-flow algorithm to solve the monocular simultaneous localisation and mapping (SLAM) problem for ground or aerial robots. Dense optical flow can effectively provide the ego-motion of the vehicle while enabling collision avoidance with the potential obstacles. Existing [...] Read more.

This paper presents a novel dense optical-flow algorithm to solve the monocular simultaneous localisation and mapping (SLAM) problem for ground or aerial robots. Dense optical flow can effectively provide the ego-motion of the vehicle while enabling collision avoidance with the potential obstacles. Existing research has not fully utilised the uncertainty of the optical flow—at most, an isotropic Gaussian density model has been used. We estimate the full uncertainty of the optical flow and propose a new eight-point algorithm based on the statistical Mahalanobis distance. Combined with the pose-graph optimisation, the proposed method demonstrates enhanced robustness and accuracy for the public autonomous car dataset (KITTI) and aerial monocular dataset. Full article

(This article belongs to the Special Issue Multi-Radio and/or Multi-Sensor Integrated Navigation System)

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13 pages, 3997 KiB

Open AccessArticle

Depth-Camera-Aided Inertial Navigation Utilizing Directional Constraints

by Usman Qayyum and Jonghyuk Kim

Sensors 2021, 21(17), 5913; https://doi.org/10.3390/s21175913 - 02 Sep 2021

Cited by 3 | Viewed by 1942

Abstract

This paper presents a practical yet effective solution for integrating an RGB-D camera and an inertial sensor to handle the depth dropouts that frequently happen in outdoor environments, due to the short detection range and sunlight interference. In depth drop conditions, only the [...] Read more.

This paper presents a practical yet effective solution for integrating an RGB-D camera and an inertial sensor to handle the depth dropouts that frequently happen in outdoor environments, due to the short detection range and sunlight interference. In depth drop conditions, only the partial 5-degrees-of-freedom pose information (attitude and position with an unknown scale) is available from the RGB-D sensor. To enable continuous fusion with the inertial solutions, the scale ambiguous position is cast into a directional constraint of the vehicle motion, which is, in essence, an epipolar constraint in multi-view geometry. Unlike other visual navigation approaches, this can effectively reduce the drift in the inertial solutions without delay or under small parallax motion. If a depth image is available, a window-based feature map is maintained to compute the RGB-D odometry, which is then fused with inertial outputs in an extended Kalman filter framework. Flight results from the indoor and outdoor environments, as well as public datasets, demonstrate the improved navigation performance of the proposed approach. Full article

(This article belongs to the Special Issue Multi-Radio and/or Multi-Sensor Integrated Navigation System)

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17 pages, 9271 KiB

Open AccessArticle

Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi

by Byeong-ho Lee, Kyoung-Min Park, Yong-Hwa Kim and Seong-Cheol Kim

Sensors 2021, 21(16), 5583; https://doi.org/10.3390/s21165583 - 19 Aug 2021

Cited by 5 | Viewed by 2095

Abstract

In this paper, we propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. We replaced the ranging part of the rule-based localization method with a deep regression model that uses [...] Read more.

In this paper, we propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. We replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error compared to the existing methods. In addition, we verified that the proposed method was robust to changes in the indoor structure. Full article

(This article belongs to the Special Issue Multi-Radio and/or Multi-Sensor Integrated Navigation System)

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16 pages, 5269 KiB

Open AccessArticle

Data Augmentation of Automotive LIDAR Point Clouds under Adverse Weather Situations

by Jose Roberto Vargas Rivero, Thiemo Gerbich, Boris Buschardt and Jia Chen

Sensors 2021, 21(13), 4503; https://doi.org/10.3390/s21134503 - 30 Jun 2021

Cited by 10 | Viewed by 3295

Abstract

In contrast to previous works on data augmentation using LIDAR (Light Detection and Ranging), which mostly consider point clouds under good weather conditions, this paper uses point clouds which are affected by spray. Spray water can be a cause of phantom braking and [...] Read more.

In contrast to previous works on data augmentation using LIDAR (Light Detection and Ranging), which mostly consider point clouds under good weather conditions, this paper uses point clouds which are affected by spray. Spray water can be a cause of phantom braking and understanding how to handle the extra detections caused by it is an important step in the development of ADAS (Advanced Driver Assistance Systems)/AV (Autonomous Vehicles) functions. The extra detections caused by spray cannot be safely removed without considering cases in which real solid objects may be present in the same region in which the detections caused by spray take place. As collecting real examples would be extremely difficult, the use of synthetic data is proposed. Real scenes are reconstructed virtually with an added extra object in the spray region, in a way that the detections caused by this obstacle match the characteristics a real object in the same position would have regarding intensity, echo number and occlusion. The detections generated by the obstacle are then used to augment the real data, obtaining, after occlusion effects are added, a good approximation of the desired training data. This data is used to train a classifier achieving an average F-Score of 92. The performance of the classifier is analyzed in detail based on the characteristics of the synthetic object: size, position, reflection, duration. The proposed method can be easily expanded to different kinds of obstacles and classifier types. Full article

(This article belongs to the Special Issue Multi-Radio and/or Multi-Sensor Integrated Navigation System)

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24 pages, 4649 KiB

Open AccessArticle

Emitter Location with Azimuth and Elevation Measurements Using a Single Aerial Platform for Electronic Support Missions

by Mohamed Khalaf-Allah

Sensors 2021, 21(12), 3946; https://doi.org/10.3390/s21123946 - 08 Jun 2021

Cited by 2 | Viewed by 3850

Abstract

Passive ground emitter geolocation techniques are essential to electronic warfare systems, as they provide threat warnings in hostile environments, while ensuring the electronic silence of the mission platform. Geolocation of enemy emitters indicates the position of and type of adversary troops, and allows [...] Read more.

Passive ground emitter geolocation techniques are essential to electronic warfare systems, as they provide threat warnings in hostile environments, while ensuring the electronic silence of the mission platform. Geolocation of enemy emitters indicates the position of and type of adversary troops, and allows for the use of guided munition against enemy targets. Three-dimensional geolocation solutions based on least squares and particle filter estimation, using only azimuth and elevation measurements, were considered. Three batch-processing and one instantaneous solution algorithms, i.e., using a single pulse or a single observation point, were developed and investigated. The performance of the proposed solutions was demonstrated by simulations. Results showed that the batch-processing solutions achieved acceptable accuracies with a sufficient number of observation points. The performance degraded with fewer observation points. The instantaneous geolocation solution improved performance with increasing observation points, i.e., working in the sequential mode, and therefore could approach the accuracy of the batch-processing solutions. Full article

(This article belongs to the Special Issue Multi-Radio and/or Multi-Sensor Integrated Navigation System)

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