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Radar, Sonar and Navigation

A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Radar Sensors".

Viewed by 9345

Editor

Prof. Dr. Andrzej Stateczny

E-Mail Website
Collection Editor
Department of Geodesy, Faculty of Civil and Environmental Engineering, Gdansk Technical University, Narutowicza St. 11/12, Gdansk, Poland
Interests: radar navigation; comparative (terrain-based) navigation; multi-sensor data fusion; radar and sonar target tracking; sonar imaging and understanding; MBES bathymetry; ASV; artificial neural networks; geoinformatics
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

In recent years, navigation, especially autonomous navigation, has become one of the most important research areas in the world. Navigation, including autonomous navigation, is rapidly developing in air, land, surface water and underwater environments. The electronic eye of the navigator was and is remote sensing sensors—radar and sonar. They provide data in the full measurement sphere in the air and in the water. The rapidly developing new technologies, such as multi-sensor data fusion, Big Data processing and deep learning, are opening up new areas of application, improving sensors and applied navigation systems. The impact of artificial intelligence on the processing and understanding of sensor data is particularly pronounced. Radar, sonar, and vision sensors, and other sensors mounted onboard smart and flexible platforms, are bringing new qualities to the field of navigation applications, and on different types of unmanned vehicles in all kinds of environments. These technologies, focusing on autonomous unmanned navigation, represent contemporary scientific challenges.

In this Topic Collection, we will collect articles on many aspects of radar, sonar measurement technology and advanced navigation problems, mainly realized based on these sensors including applications to autonomous unmanned vehicles, such as autonomous navigation, multi-sensor fusion, processing of large amounts of radar and sonar data for in-vehicle navigation, synergy of radar and sonar sensors in the navigation guidance process, object detection and tracking algorithms, non-GNNS navigation, multi-sensor data fusion and artificial intelligence methods for navigation. Topics in this TC include, but are not limited to, the following keywords:

  • Radar and sonar surveillance systems.
  • Radar and sonar target detection, tracking and anti-collision algorithms and methods.
  • Radar and sonar data processing, data reduction, feature extraction, and image understanding.
  • Radar and sonar technology for autonomous vehicles.
  • Synergy between radar, sonar, and other sensors.
  • 3D radar and 3D sonar for navigation.
  • Multi-sensor data fusion for navigation.
  • Sensors based autonomous navigation.
  • Comparative (terrain reference) navigation.
  • Underwater navigation.
  • Non GNSS autonomous navigation.
  • Artificial Intelligence for navigation and remote sensors data processing.
  • Big data processing for navigation.
  • Path-planning methods for autonomous vehicle navigation.
  • Deep learning algorithms for navigation.

Prof. Dr. Andrzej Stateczny
Collection Editor

Manuscript Submission Information

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Keywords

  • radar and sonar surveillance systems
  • radar and sonar target detection, tracking and anti-collision algorithms and methods
  • radar and sonar data processing, data reduction, feature extraction, and image understanding
  • radar and sonar technology for autonomous vehicles
  • synergy between radar, sonar, and other sensors
  • 3D radar and 3D sonar for navigation
  • multi-sensor data fusion for navigation
  • sensors based autonomous navigation
  • comparative (terrain reference) navigation
  • underwater navigation
  • non GNSS autonomous navigation
  • artificial intelligence for navigation and remote sensors data processing
  • big data processing for navigation
  • path-planning methods for autonomous vehicle navigation
  • deep learning algorithms for navigation

Published Papers (5 papers)

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2023

Jump to: 2022

15 pages, 7449 KiB  
Article
Colocated MIMO Radar Waveform-Array Joint Optimization for Sparse Array
by Jinrong Yin, Rui Ma, Mingcong Lin and Shenghua Zhou
Sensors 2023, 23(9), 4375; https://doi.org/10.3390/s23094375 - 28 Apr 2023
Cited by 1 | Viewed by 892
Abstract
Colocated multiple-input multiple-output (MIMO) radar can transmit a group of distinct waveforms via its colocated transmit antennas and the waveform diversity leads to several advantages in contrast to conventional phased-array radar. The performance depends highly on the degrees available, and element spacing can [...] Read more.
Colocated multiple-input multiple-output (MIMO) radar can transmit a group of distinct waveforms via its colocated transmit antennas and the waveform diversity leads to several advantages in contrast to conventional phased-array radar. The performance depends highly on the degrees available, and element spacing can be deemed as another source of degrees of freedom. In this paper, we study the joint waveform and element spacing optimization problem. A joint waveform and array optimization criterion is proposed to match the transmit beampattern, the suppression range, and the angular sidelobes, under the constraints of minimal element spacing and total array aperture. Meanwhile, the effect of receive beamforming on suppressing mutual correlation between returns from different spatial directions is also incorporated into the optimization criterion. The optimization problem is solved by the sequential quadratic programming algorithm. Numerical results indicate that with more degrees of freedom from array spacings, colocated MIMO radar achieves a better transmit beampattern matching performance and a lower sidelobe level, compared with a fixed half-wavelength spaced array, but the benefits from additional degrees of freedom from array spacing optimization have a limit. Full article
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13 pages, 2136 KiB  
Article
Analysis of GPS/EGNOS Positioning Quality Using Different Ionospheric Models in UAV Navigation
by Grzegorz Grunwald, Adam Ciećko, Tomasz Kozakiewicz and Kamil Krasuski
Sensors 2023, 23(3), 1112; https://doi.org/10.3390/s23031112 - 18 Jan 2023
Cited by 5 | Viewed by 1465
Abstract
Unmanned aerial vehicles (UAVs) have become very popular tools for geoinformation acquisition in recent years. They have also been applied in many other areas of life. Their navigation is highly dependent on global navigation satellite systems (GNSS). The European Geostationary Navigation Overlay Service [...] Read more.
Unmanned aerial vehicles (UAVs) have become very popular tools for geoinformation acquisition in recent years. They have also been applied in many other areas of life. Their navigation is highly dependent on global navigation satellite systems (GNSS). The European Geostationary Navigation Overlay Service (EGNOS) is intended to support GNSSs during positioning, mainly for aeronautical applications. The research presented in this paper concerns the analysis of the positioning quality of a modified GPS/EGNOS algorithm. The calculations focus on the source of ionospheric delay data as well as on the aspect of smoothing code observations with phase measurements. The modifications to the algorithm concerned the application of different ionospheric models for position calculation. Consideration was given to the EGNOS ionospheric model, the Klobuchar model applied to the GPS system, the Klobuchar model applied to the BeiDou system, and the NeQuick model applied to the Galileo system. The effect of removing ionospherical corrections from GPS/EGNOS positioning on the results of the determination of positioning quality was also analysed. The results showed that the original EGNOS ionospheric model maintains the best accuracy results and a better correlation between horizontal and vertical results than the other models examined. The additional use of phase-smoothing of code observations resulted in maximum horizontal errors of approximately 1.3 m and vertical errors of approximately 2.2 m. It should be noted that the results obtained have local characteristics related to the area of north-eastern Poland. Full article
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2022

Jump to: 2023

14 pages, 1351 KiB  
Article
Optimization Method for Wide Beam Sonar Transmit Beamforming
by Louise Rixon Fuchs, Atsuto Maki and Andreas Gällström
Sensors 2022, 22(19), 7526; https://doi.org/10.3390/s22197526 - 04 Oct 2022
Cited by 3 | Viewed by 1530
Abstract
Imaging and mapping sonars such as forward-looking sonars (FLS) and side-scan sonars (SSS) are sensors frequently used onboard autonomous underwater vehicles. To acquire information from around the vehicle, it is desirable for these sonar systems to insonify a large area; thus, the sonar [...] Read more.
Imaging and mapping sonars such as forward-looking sonars (FLS) and side-scan sonars (SSS) are sensors frequently used onboard autonomous underwater vehicles. To acquire information from around the vehicle, it is desirable for these sonar systems to insonify a large area; thus, the sonar transmit beampattern should have a wide field of view. In this work, we study the problem of the optimization of wide transmission beampatterns. We consider the conventional phased-array beampattern design problem where all array elements transmit an identical waveform. The complex weight vector is adjusted to create the desired beampattern shape. In our experiments, we consider wide transmission beampatterns (≥20∘) with uniform output power. In this paper, we introduce a new iterative-convex optimization method for narrowband linear phased arrays and compare it to existing approaches for convex and concave–convex optimization. In the iterative-convex method, the phase of the weight parameters is allowed to be complex as in disciplined convex–concave programming (DCCP). Comparing the iterative-convex optimization method and DCCP to the standard convex optimization, we see that the former methods archive optimized beampatterns closer to the desired beampatterns. Furthermore, for the same number of iterations, the proposed iterative-convex method achieves optimized beampatterns, which are closer to the desired beampattern than the beampatterns achieved by optimization with DCCP. Full article
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28 pages, 3194 KiB  
Article
Comparison of DVB-T Passive Radar Simulated and Measured Bistatic RCS Values for a Pilatus PC-12 Aircraft
by Peter J. Speirs, Martin Ummenhofer, Christof Schüpbach, Matthias Renker, Peter Wellig, Diego Cristallini, Daniel W. O’Hagan, Michael Kohler and Axel Murk
Sensors 2022, 22(7), 2766; https://doi.org/10.3390/s22072766 - 03 Apr 2022
Cited by 1 | Viewed by 2085
Abstract
Passive radar is a technology that has huge potential for airspace monitoring, taking advantage of existing transmissions. However, to predict whether particular targets can be measured in a particular scenario, it is necessary to be able to model the received signal. In this [...] Read more.
Passive radar is a technology that has huge potential for airspace monitoring, taking advantage of existing transmissions. However, to predict whether particular targets can be measured in a particular scenario, it is necessary to be able to model the received signal. In this paper, we present the results of a campaign in which a Pilatus PC-12 single-engine aircraft was measured with a passive radar system relying on DVB-T transmission from a single transmitter. We then present our work to simulate the bistatic RCS of the aircraft along its flight track, using both the method of moments and the shooting and bouncing ray solvers, assess the uncertainty in the simulations, and compare against the measurements. We find that our simulated RCS values are useful in predicting whether or not detection occurs. However, we see poor agreement between simulated and measured RCS values where measurements are available, which we attribute primarily to the difficulties in extracting RCS measurements from the data and to unmodeled transmission and received path effects. Full article
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19 pages, 6985 KiB  
Article
Parallel Optimisation and Implementation of a Real-Time Back Projection (BP) Algorithm for SAR Based on FPGA
by Yue Cao, Shuchen Guo, Shuai Jiang, Xuan Zhou, Xiaobei Wang, Yunhua Luo, Zhongjun Yu, Zhimin Zhang and Yunkai Deng
Sensors 2022, 22(6), 2292; https://doi.org/10.3390/s22062292 - 16 Mar 2022
Cited by 10 | Viewed by 2345
Abstract
This study conducts an in-depth evaluation of imaging algorithms and software and hardware architectures to meet the capability requirements of real-time image acquisition systems, such as spaceborne and airborne synthetic aperture radar (SAR) systems. By analysing the principles and models of SAR imaging, [...] Read more.
This study conducts an in-depth evaluation of imaging algorithms and software and hardware architectures to meet the capability requirements of real-time image acquisition systems, such as spaceborne and airborne synthetic aperture radar (SAR) systems. By analysing the principles and models of SAR imaging, this research creatively puts forward the fully parallel processing architecture for the back projection (BP) algorithm based on Field-Programmable Gate Array (FPGA). The processing time consumption has significant advantages compared with existing methods. This article describes the BP imaging algorithm, which stands out with its high processing accuracy and two-dimensional decoupling of distance and azimuth, and analyses the algorithmic flow, operation, and storage requirements. The algorithm is divided into five core operations: range pulse compression, upsampling, oblique distance calculation, data reading, and phase accumulation. The architecture and optimisation of the algorithm are presented, and the optimisation methods are described in detail from the perspective of algorithm flow, fixed-point operation, parallel processing, and distributed storage. Next, the maximum resource utilisation rate of the hardware platform in this study is found to be more than 80%, the system power consumption is 21.073 W, and the processing time efficiency is better than designs with other FPGA, DSP, GPU, and CPU. Finally, the correctness of the processing results is verified using actual data. The experimental results showed that 1.1 s were required to generate an image with a size of 900 × 900 pixels at a 200 MHz clock rate. This technology can solve the multi-mode, multi-resolution, and multi-geometry signal processing problems in an integrated manner, thus laying a foundation for the development of a new, high-performance, SAR system for real-time imaging processing. Full article
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