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Remote Sensing
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Advances of Noise Radar for Remote Sensing (ANR-RS)
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Advances of Noise Radar for Remote Sensing (ANR-RS)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 15448

Special Issue Editors

Prof. Dr. Vito Pascazio

E-Mail Website
Guest Editor
Dipartimento di Ingegneria, Università degli Studi di Napoli “Parthenope”, 80143 Napoli, Italy
Interests: synthetic aperture radar (SAR) image processing; SAR interferometry; SAR tomography; GB-SAR; ground penetrating radars; through-the-wall imaging, and deep learning techniques to radar and remote sensing imaging
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Konstantin Lukin

E-Mail Website
Guest Editor
Laboratory for Nonlinear Dynamics of Electronic Systems (LNDES), National Academy of Sciences of Ukraine, 61085 Kharkov, Ukraine
Interests: analogue and digital generation and processing of random/chaotic/noise signals and their applications in Noise Radar for SAR imaging; 3D imaging with MIMO Ground Noise SAR; microwave monitoring and detection of pre-catastrophic states of large constructions; remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Krzysztof Kulpa

E-Mail Website
Guest Editor
Defense and Security Research Center, Institute of Electronic Systems, Warsaw University of Technology (WUT), Nowowiejska 15/19, Warsaw, Poland
Interests: 2D and 3D maneuvering target tracking; maritime patrol radar; low RCS target detection and tracking; noise and passive radars; synthetic aperture radar and ISAR imaging; cognitive radars and EW; airborne passive radars
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays classical radar technology is mature, but classical radar sensors have many drawbacks and constrains, such as range and Doppler ambiguity, limitations in imaging due to short observation time, poor resistance against interferences, etc. Noise Radar (NR) exploits noise/random or pseudo-random signals for target illumination. The use of such waveforms provides completely new features for newly developed sensors, such as lack of range ambiguity even for periodic pulsed probing signals. More than 30 years of R&D activities in Noise Radar Technology (NRT) have revealed its great potentiality for many radar applications and, in particular, in Remote Sensing of the environment and various objects. NRT could potentially open the new fields of NR applications in remote sensing systems design.

NRT uses random noise waveforms (NW) as a sounding signal and coherent processing for NR returns reception. Basic properties of Noise Waveform have been investigated from the viewpoint of NR design. It has been shown that NRT has the potential to meet today’s requirements for advanced radar sensor design. NRT enables independent variation of range and velocity resolutions and ambiguities, which is not the case for other waveforms. Furthermore, NR systems display excellent Interference Immunity and Electro-Magnetic Compatibility (EMC) performance and potentiality for implementing of covert operation. Nowadays, an efficient signal processing in NR might be implemented on the basis of Field Programmable Gate Arrays (FPGA) technology.

Application of Fast ADCs and FPGA-based Digital Signal Processors (DSP) gives an excellent basis for the design of dedicated DSPs for real-time processing of noise radar returns and implementation of both the random signal generation and the radar return processing required for NR implementations.

Noise Radar Technology enables essential enhancements of sensor performance, such as:

- Better performance in immunity against interference;
- Better electromagnetic compatibility performance between different type radar sensors and different NR sub-units through better interference immunity.
- No range ambiguity even for pulse noise radar.
- Spectrum sharing problem may be easier solved using orthogonality of the random feeling of different probing pulses, provided high enough BT product: wide enough power spectrum bandwidth, B, along with long enough integration time, T.


All this makes application of NRT in radar system design promising for various remote sensing applications. However, there are still many challenges in implementing radar systems, based upon Noise Waveform which requires additional R&D efforts.


That is why the NRT is under intense investigations in many national laboratories in different countries and , also, via international cooperation in ad-hoc Groups, such as those affiliated with NATO Sensors and Electronic Technology (SET) Research Task Group (RTG): SET RTG-101 on “Noise Radar Technology” which was leaded by K. Lukin and held a set of joint trials in Kharkov, Ukraine, in June 2008, and, also, under the follow-on SET RTG-184 on “Capabilities of Noise Radar” in a series of trials during September and October 2013, and continued by the ensuing SET RTG-225 on “Spatial and Waveform Diverse Noise Radar” in the field trials in June and December 2018. From 2020, research activity on NRT will be continued under the aegis of the recently approved NATO/STO SET-287  TG on "Characterization of Noise Radar", co-chaired by K. Lukin and C. Wasserzier.

The proposed Special Issue on Advances of Noise Radar for Remote Sensing is devoted to many theoretical and the experimental sides of NRT advancing with special focusing on its applications for design of Ground Based, Airborne and Space-borne Remote Sensing Systems. Hence, ANR-RS Issue invites all interested authors to submit their contributions related, but not limited to, the following areas:

  1. Random/Noise Waveforms for Noise Radar Remote Sensing Systems (NR-RSS)
  2. Combination of Noise Radar Imaging and Communication systems
  3. Antenna for NR-RSS: design, modelling and experiments
  4. Theory and experiments with advanced NR: SAR, ISAR, Polarimetric, MIMO, MISO
  5. NR-RSS experiments with various antennas: rotating, steering and antenna arrays
  6. Methods and hardware for real-time generation and processing of NR waveforms
  7. Noise Radar Remote Sensing from moving platforms: ground, airborne and space borne
  8. High PRF Space Borne Noise SAR
  9. Applications of Noise SAR systems for border area monitoring
  10. Applications of Ground Noise SAR for monitoring of large civil engineering objects: Dumbs, Bridges, Hangars, buildings, TV towers, etc.
  11. Real-time 2D and 3D imaging: microwave video-cameras

Prof. Vito Pascazio
Prof. Konstantin Lukin
Prof. Krzysztof Kulpa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Noise Radar
  • Random/Noise/Chaotic and Pseudo-random Waveforms
  • Cross Ambiguity Function, Correlation Receiver
  • Remote Sensing with Noise Radar
  • MISO Imaging Noise SAR
  • Antenna Design for Noise Radar
  • Noise Radar Demonstrators for Remote Sensing
  • Noise SAR and ISAR Performance Evaluation
  • SAR imaging with Airborne and Space borne Noise Radar
  • High PRF Noise Radar
  • Ground Noise SAR

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Published Papers (5 papers)

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20 pages, 9687 KiB  
Article
A Collaborative Despeckling Method for SAR Images Based on Texture Classification
by Gongtang Wang, Fuyu Bo, Xue Chen, Wenfeng Lu, Shaohai Hu and Jing Fang
Remote Sens. 2022, 14(6), 1465; https://doi.org/10.3390/rs14061465 - 18 Mar 2022
Cited by 11 | Viewed by 2592
Abstract
Speckle is an unavoidable noise-like phenomenon in Synthetic Aperture Radar (SAR) imaging. In order to remove speckle, many despeckling methods have been proposed during the past three decades, including spatial-based methods, transform domain-based methods, and non-local filtering methods. However, SAR images usually contain [...] Read more.
Speckle is an unavoidable noise-like phenomenon in Synthetic Aperture Radar (SAR) imaging. In order to remove speckle, many despeckling methods have been proposed during the past three decades, including spatial-based methods, transform domain-based methods, and non-local filtering methods. However, SAR images usually contain many different types of regions, including homogeneous and heterogeneous regions. Some filters could despeckle effectively in homogeneous regions but could not preserve structures in heterogeneous regions. Some filters preserve structures well but do not suppress speckle effectively. Following this theory, we design a combination of two state-of-the-art despeckling tools that can overcome their respective shortcomings. In order to select the best filter output for each area in the image, the clustering and Gray Level Co-Occurrence Matrices (GLCM) are used for image classification and weighting, respectively. Clustering and GLCM use the co-registered optical images of SAR images because their structure information is consistent, and the optical images are much cleaner than SAR images. The experimental results on synthetic and real-world SAR images show that our proposed method can provide a better objective performance index under a strong noise level. Subjective visual inspection demonstrates that the proposed method has great potential in preserving structural details and suppressing speckle noise. Full article
(This article belongs to the Special Issue Advances of Noise Radar for Remote Sensing (ANR-RS))
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22 pages, 2774 KiB  
Article
Counter-Interception and Counter-Exploitation Features of Noise Radar Technology
by Gaspare Galati, Gabriele Pavan, Kubilay Savci and Christoph Wasserzier
Remote Sens. 2021, 13(22), 4509; https://doi.org/10.3390/rs13224509 - 9 Nov 2021
Cited by 14 | Viewed by 3747
Abstract
In defense applications, the main features of radars are the Low Probability of Intercept (LPI) and the Low Probability of Exploitation (LPE). The counterpart uses more and more capable intercept receivers and signal processors thanks to the ongoing technological progress. Noise Radar Technology [...] Read more.
In defense applications, the main features of radars are the Low Probability of Intercept (LPI) and the Low Probability of Exploitation (LPE). The counterpart uses more and more capable intercept receivers and signal processors thanks to the ongoing technological progress. Noise Radar Technology (NRT) is probably a very effective answer to the increasing demand for operational LPI/LPE radars. The design and selection of the radiated waveforms, while respecting the prescribed spectrum occupancy, has to comply with the contrasting requirements of LPI/LPE and of a favorable shape of the ambiguity function. Information theory seems to be a “technologically agnostic” tool to attempt to quantify the LPI/LPE capability of noise waveforms with little, or absent, a priori knowledge of the means and the strategies used by the counterpart. An information theoretical analysis can lead to practical results in the design and selection of NRT waveforms. Full article
(This article belongs to the Special Issue Advances of Noise Radar for Remote Sensing (ANR-RS))
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25 pages, 9055 KiB  
Article
Low-PAPR Waveforms with Shaped Spectrum for Enhanced Low Probability of Intercept Noise Radars
by Kubilay Savci, Gaspare Galati and Gabriele Pavan
Remote Sens. 2021, 13(12), 2372; https://doi.org/10.3390/rs13122372 - 17 Jun 2021
Cited by 12 | Viewed by 2984
Abstract
Noise radars employ random waveforms in their transmission as compared to traditional radars. Considered as enhanced Low Probability of Intercept (LPI) radars, they are resilient to interference and jamming and less vulnerable to adversarial exploitation than conventional radars. At its simplest, using a [...] Read more.
Noise radars employ random waveforms in their transmission as compared to traditional radars. Considered as enhanced Low Probability of Intercept (LPI) radars, they are resilient to interference and jamming and less vulnerable to adversarial exploitation than conventional radars. At its simplest, using a random waveform such as bandpass Gaussian noise as a probing signal provides limited radar performance. After a concise review of a particular noise radar architecture and related correlation processing, this paper justifies the rationale for having synthetic (tailored) noise waveforms and proposes the Combined Spectral Shaping and Peak-to-Average Power Reduction (COSPAR) algorithm, which can be utilized for synthesizing noise-like sequences with a Taylor-shaped spectrum under correlation sidelobe level constraints and assigned Peak-to-Average-Power-Ratio (PAPR). Additionally, the Spectral Kurtosis measure is proposed to evaluate the LPI property of waveforms, and experimental results from field trials are reported. Full article
(This article belongs to the Special Issue Advances of Noise Radar for Remote Sensing (ANR-RS))
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15 pages, 1842 KiB  
Article
Coherent Integration Loss Due to Nonstationary Phase Noise in High-Resolution Millimeter-Wave Radars
by Chagai Levy, Monika Pinchas and Yosef Pinhasi
Remote Sens. 2021, 13(9), 1755; https://doi.org/10.3390/rs13091755 - 30 Apr 2021
Cited by 2 | Viewed by 2118
Abstract
Phase noise refers to the instability of an oscillator, which is the cause of instantaneous phase and frequency deviations in the carrier wave. This unavoidable instability adversely affects the performance of range–velocity radar systems, including synthetic aperture radars (SARs) and ground-moving target indicator [...] Read more.
Phase noise refers to the instability of an oscillator, which is the cause of instantaneous phase and frequency deviations in the carrier wave. This unavoidable instability adversely affects the performance of range–velocity radar systems, including synthetic aperture radars (SARs) and ground-moving target indicator (GMTI) radars. Phase noise effects should be considered in high-resolution radar designs, operating in millimeter wavelengths and terahertz frequencies, due to their role in radar capability during the reliable identification of target location and velocity. In general, phase noise is a random process consisting of nonstationary terms. It has been shown that in order to optimize the coherent detection of stealthy, fast-moving targets with a low radar cross-section (RCS), it is required to evaluate the integration gain and to determine the incoherent noise effects for resolving target location and velocity. Here, we present an analytical expression for the coherent integration loss when a nonstationary phase noise is considered. A Wigner distribution was employed to derive the time–frequency expression for the coherent loss when nonstationary conditions were considered. Up to now, no analytical expressions have been developed for coherent integration loss when dealing with real nonstationary phase noise mathematical models. The proposed expression will help radar systems estimate the nonstationary integration loss and adjust the decision threshold value in order to maximize the probability of detection. The effect of nonstationary phase noise is demonstrated for studying coherent integration loss of high-resolution radar operating in the W-band. The investigation indicates that major degradation in the time-frequency coherent integration due to short-term, nonstationary phase noise instabilities arises for targets moving at low velocities and increases with range. Opposed to the conventional model, which assumes stationarity, a significant difference of up to 25 dB is revealed in the integration loss for radars operating in the millimeter wave regime. Moreover, for supersonic moving targets, the loss peaks at intermediate distances and then reduces as the target moves away. Full article
(This article belongs to the Special Issue Advances of Noise Radar for Remote Sensing (ANR-RS))
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16 pages, 22627 KiB  
Technical Note
Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets
by Xueyuan Tang, Sheng Dong, Kun Luo, Jingxue Guo, Lin Li and Bo Sun
Remote Sens. 2022, 14(2), 399; https://doi.org/10.3390/rs14020399 - 16 Jan 2022
Cited by 6 | Viewed by 2879
Abstract
The airborne ice-penetrating radar (IPR) is an effective method used for ice sheet exploration and is widely applied for detecting the internal structures of ice sheets and for understanding the mechanism of ice flow and the characteristics of the bottom of ice sheets. [...] Read more.
The airborne ice-penetrating radar (IPR) is an effective method used for ice sheet exploration and is widely applied for detecting the internal structures of ice sheets and for understanding the mechanism of ice flow and the characteristics of the bottom of ice sheets. However, because of the ambient influence and the limitations of the instruments, IPR data are frequently overlaid with noise and interference, which further impedes the extraction of layer features and the interpretation of the physical characteristics of the ice sheet. In this paper, we first applied conventional filtering methods to remove the feature noise and interference in IPR data. Furthermore, machine learning methods were introduced in IPR data processing for noise removal and feature extraction. Inspired by a comparison of the filtering methods and machine learning methods, we propose a fusion method combining both filtering methods and machine-learning-based methods to optimize the feature extraction in IPR data. Field data tests indicated that, under different conditions of IPR data, the application of different methods and strategies can improve the layer feature extraction. Full article
(This article belongs to the Special Issue Advances of Noise Radar for Remote Sensing (ANR-RS))
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