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

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 30431

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Prof. Dr. Konrad Jędrzejewski

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Guest Editor

Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
Interests: radar signal processing; passive radars; space surveillance radars; adaptive signal processing; biomedical signal processing; machine learning; analog-to-digital converters
Special Issues, Collections and Topics in MDPI journals

Dr. Matthias Weiß

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Guest Editor

Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, 53343 Wachtberg, Germany
Interests: multistatic radar; waveform design; joint radar–communication; RadCom; synchronization; simultaneous transmit and receive (STAR)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Signal processing and radar techniques are the basis of the majority of remote sensing systems. Recent developments in both fields have allowed meeting the increasing demands of modern remote sensing systems. Hence, the advances in these two areas, especially those intended for future technology, should be monitored continuously by the remote sensing community.

This Special Issue aims to present the latest research results in the field of signal processing and radar techniques that are related to remote sensing, presented among others during the Signal Processing Symposium (SPSympo), Łódź, Poland, 2021, and also other radar and remote sensing conferences like the International Radar Symposium (IRS), Berlin, Germany 2021. Contributions from leading experts in these fields of research will be collected and presented in this Special Issue.

Topics include but are not limited to:

Signal processing in remote sensing;
Radar technologies and techniques for remote sensing;
Microwave and THz radar imaging techniques;
SAR and ISAR techniques;
Passive radar imaging;
Target detection and tracking;
Target recognition and classification;
Antennas, arrays, and beamforming;
Space applications;

Measurement methods and systems related to remote sensing.

Prof. Dr. Konrad Jędrzejewski
Dr. Matthias Weiß
Guest Editors

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Published Papers (12 papers)

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22 pages, 7445 KiB

Open AccessArticle

Determining Ionospheric Drift and Anisotropy of Irregularities from LOFAR Core Measurements: Testing Hypotheses behind Estimation

by Marcin Grzesiak, Mariusz Pożoga, Barbara Matyjasiak, Dorota Przepiórka, Katarzyna Beser, Lukasz Tomasik, Hanna Rothkaehl and Helena Ciechowska

Remote Sens. 2022, 14(18), 4655; https://doi.org/10.3390/rs14184655 - 18 Sep 2022

Viewed by 2023

Abstract

We try to assess the validity of assumptions taken when deriving drift velocity. We give simple formulas for characteristics of the spatiotemporal correlation function of the observed diffraction pattern for the frozen flow and the more general Briggs model. Using Low-Frequency Array (LOFAR) Cassiopeia intensity observation, we compare the experimental velocity scaling factor with a theoretical one to show that both models do not follow observations. We also give a qualitative comparison of our drift velocity estimates with SuperDARN convection maps. The article is essentially an extended version of the conference paper: “Determining ionospheric drift and anisotropy of irregularities from LOFAR core measurements”, Signal Processing Symposium 2021 (SPSympo 2021). Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)

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14 pages, 6126 KiB

Open AccessCommunication

A Wideband Noise Radar System Using a Phased Array with True Time Delay

by Eunhee Kim, In-kyu Kim, Seungsu Han, Jaemin Lee and Sang-jin Shin

Remote Sens. 2022, 14(18), 4489; https://doi.org/10.3390/rs14184489 - 8 Sep 2022

Cited by 2 | Viewed by 2204

Abstract

Noise radar has become attractive owing to progress in hardware technology. Aside from the low probability of exploitation, the use of noise waveform is likely to grow due to its low interference features, especially in circumstances where multiple radars operate in the same band. In this study, we developed and tested a wideband noise radar for a ground-moving vehicle. It operates in the X-band with an instantaneous bandwidth of 1.5 GHz. The true time delay (TTD) was applied to correct the distortion of the beam pattern by the wide bandwidth, and the correlators were implemented by high-speed parallel processing using a field programmable gate array (FPGA). The outdoor experimental results were presented. Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)

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28 pages, 1151 KiB

Open AccessArticle

A Ship Discrimination Method Based on High-Frequency Electromagnetic Theory

by Yaomin He, Huizhang Yang, Huafeng He, Junjun Yin and Jian Yang

Remote Sens. 2022, 14(16), 3893; https://doi.org/10.3390/rs14163893 - 11 Aug 2022

Cited by 6 | Viewed by 1636

Abstract

Ship target detection using radar has important applications in the military and civilian fields. As a decoy, the corner reflector (CR) can successfully deceive a radar by its strong radar cross-section (RCS) to protect a ship. In order to discriminate between a CR and ship, this paper proposes a discrimination method based on three-dimensional characteristics. First, we deduce the basic scattering of CR by the high-frequency electromagnetic theory, and propose a CR decomposition which can solve the problem that the Krogager decomposition has terrible errors in clutter. Then, we introduce the definition of the main scattering polarization and give the multi-dimensional characteristic of CR. Subsequently, we analyze the spatial-time characteristic of radar based on the three-dimensional proportional guidance. With the CR mean square error (MSE), a CR discrimination method is proposed based on the time-spatial-polarization (TSP) joint domains. Finally, the proposed method is analyzed and compared using the fully polarimetric data of Feko software, which can achieve 95% discrimination probability and 4.1% false alarm probability. Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)

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14 pages, 499 KiB

Open AccessCommunication

Low-Complexity One-Bit DOA Estimation for Massive ULA with a Single Snapshot

by Shaodi Ge, Chongyi Fan, Jian Wang and Xiaotao Huang

Remote Sens. 2022, 14(14), 3436; https://doi.org/10.3390/rs14143436 - 17 Jul 2022

Cited by 8 | Viewed by 1820

Abstract

Existing one-bit direction of arrival (DOA) estimate methods based on sparse recovery or subspace have issues when used for massive uniform linear arrays (MULAs), such as high computing cost, estimation accuracy depending on grid size, or high snapshot-number requirements. This paper considers the low-complexity one-bit DOA estimation problems for MULA with a single snapshot. Theoretical study and simulation results demonstrate that discrete Fourier transform (DFT) can be applied to MULA for reliable initial DOA estimation even when the received data are quantized by one-bit methods. A precise estimate is then obtained by searching within a tiny area. The resulting method is called one-bit DFT. This method is straightforward and simple to implement. High-precision DOA estimates of MULA can be obtained with a single snapshot, and the computational complexity is significantly less than that of existing one-bit DOA estimation methods. Moreover, the suggested method is easily extensible to multiple snapshot scenarios, and increasing the number of snapshots can further improve estimation precision. Simulation results show the effectiveness of the one-bit DFT method. Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)

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21 pages, 6965 KiB

Open AccessArticle

A Multi-Objective Quantum Genetic Algorithm for MIMO Radar Waveform Design

by Tianqu Liu, Jinping Sun, Guohua Wang and Yilong Lu

Remote Sens. 2022, 14(10), 2387; https://doi.org/10.3390/rs14102387 - 16 May 2022

Cited by 13 | Viewed by 2258

Abstract

Aiming at maximizing waveform diversity gain when designing a phase-coded multiple-input multiple-output (MIMO) radar waveform set, it is desirable that all waveforms are orthogonal to each other. Hence, the lowest possible peak cross-correlation ratio (PCCR) is expected. Meanwhile, low peak auto-correlation side-lobe ratio (PASR) is needed for good detection performance. However, it is difficult to obtain a closed form solution to the waveform set from the expected values of the PASR and PCCR. In this paper, the waveform set design problem is modeled as a multi-objective, NP-hard constrained optimization problem. Unlike conventional approaches that design the waveform set through optimizing a weighted sum objective function, the proposed optimization model evaluates the performance of multi-objective functions based on Pareto level and obtains a set of Pareto non-dominated solutions. That means that the MIMO radar system can trade off each objective function for different requirements. To solve this problem, this paper presents a multi-objective quantum genetic algorithm (MoQGA) based on the framework of quantum genetic algorithm. A new population update strategy for the MoQGA is designed based on the proposed model. Compared to the state-of-the-art methods, like BiST and Multi-CAN, the PASR and PCCR metrics of the waveform set are 0.95–3.91 dB lower with the parameters of the numerical simulation. The MoQGA is able to minimize PASR and PCCR of the MIMO radar waveform set simultaneously. Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)

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23 pages, 7542 KiB

Open AccessArticle

Performance of Fingerprinting-Based Indoor Positioning with Measured and Simulated RSSI Reference Maps

by Robert Kawecki, Sławomir Hausman and Piotr Korbel

Remote Sens. 2022, 14(9), 1992; https://doi.org/10.3390/rs14091992 - 21 Apr 2022

Cited by 18 | Viewed by 3479

Abstract

Numerous indoor positioning technologies and systems have been proposed to localize people and objects in large buildings. Wi-Fi and Bluetooth positioning systems using fingerprinting have gained popularity, due to the wide availability of existing infrastructure. Unfortunately, the implementation of fingerprinting-based methods requires time-consuming radio surveys to prepare databases (RSSI maps) that serve as a reference for the radio signal. These surveys must be conducted for each individual building. Here, we investigate the possibility of using simulated RSSI maps with fingerprinting-based indoor localization systems. We discuss the suitability of the two popular radio wave propagation models for the preparation of RSSI reference data: ray tracing and multiwall. Based on an analysis of several representative indoor scenarios, we evaluated the performance of RSSI distribution maps obtained from simulations versus maps obtained from measurement campaigns. An experimental positioning system developed by the authors was used in the study. Based on Bluetooth Low Energy beacons and mobile devices (smartphones), the system uses fingerprinting followed by a particle filter algorithm to estimate the user’s current position from RSSI measurements and a reference spatial RSSI distribution database for each Bluetooth beacon in the building. The novelty of our contribution is that we evaluate the performance of the positioning system with RSSI maps prepared both from measurements and using the two most representative indoor propagation methods, in three different environments in terms of structure and size. We compared not only the three RSSI maps, but also how they influence the performance of the fingerprint-based positioning algorithm. Our original findings have important implications for the development of indoor localization systems and may reduce deployment times by replacing reference measurements with computer simulations. Replacing the labor-intensive and time-consuming process of building reference maps with computer modeling may significantly increase their usefulness and ease of adaptation in real indoor environments. Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)

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19 pages, 9488 KiB

Open AccessArticle

Detection of Periodic Disturbances in LOFAR Calibration Solutions

by Katarzyna Beser, Maaijke Mevius, Marcin Grzesiak and Hanna Rothkaehl

Remote Sens. 2022, 14(7), 1719; https://doi.org/10.3390/rs14071719 - 2 Apr 2022

Cited by 4 | Viewed by 1934

Abstract

The Earth’s ionosphere is a highly variable medium on a wide range of spatio-temporal scales. The responsiveness of plasma to the geomagnetic field and its changes gives rise to anisotropy, which may introduce wave-like characteristics while scanning the ionosphere with a line-of-sight towards a radio source. Previous studies of LOw Frequency ARray (LOFAR) calibration phase solutions report that the estimated beta parameter of a structure function calculated over 6–8 h of astronomical observation timespan has a range of values from 1.6 to 2.0, with an average of 1.89. Such difference between the observations could result from transient wave-like disturbances within the data. This study aims to present a method of signal processing of ionospheric calibration datasets that allows the extraction of a transient wave-like signal and discuss its possible origin. We use complex Morlet wavelet analysis applied to two 8 h observations corresponding to very quiet geomagnetic conditions. We find a wave-like signal in the interferometric Total Electron Content data even during periods of no geomagnetic activity. We suggest it results from the relative velocity changes between the LOFAR line-of-sight and a convection pattern in the ionospheric F layer. Establishing the relationship between quiet time ionosphere, geomagnetic field changes and LOFAR’s calibration solutions may prove beneficial to determination of the dominant signals in the more disturbed conditions, which we leave for future study. Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)

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23 pages, 502 KiB

Open AccessArticle

Tensor-Based Target Parameter Estimation Algorithm for FDA-MIMO Radar with Array Gain-Phase Error

by Yuehao Guo, Xianpeng Wang, Jinmei Shi, Xiang Lan and Liangtian Wan

Remote Sens. 2022, 14(6), 1405; https://doi.org/10.3390/rs14061405 - 15 Mar 2022

Cited by 4 | Viewed by 2394

Abstract

As a new radar system, FDA-MIMO radar has recently developed rapidly, as it has broad prospects in angle-range estimation. Unfortunately, the performance of existing algorithms for FDA-MIMO radar is greatly degrading or even failing under the condition of array gain-phase error. This paper proposes an innovative solution to the joint angle and range estimation of FDA-MIMO radar under the condition of array gain-phase error and an estimation algorithm is developed. Moreover, the corresponding Cramér-Rao bound (CRB) is derived to evaluate the algorithm. The parallel factor (PARAFAC) decomposition technique can be utilized to calculate transmitter and receiver direction matrices. Taking advantage of receiver direction matrix, the angle estimation can be obtained. The range estimation can be estimated by transmitter direction matrix and angle estimation. To eliminate the error accumulation effect of array gain-phase error, the gain error and phase error are obtained separately. In this algorithm, the impact of gain-phase error on parameter estimation is removed and so is the error accumulation effect. Therefore, the proposed algorithm can provide excellent performance of angle-range and gain-phase error estimation. Numerical experiments prove the validity and advantages of the proposed method. Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)

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18 pages, 3598 KiB

Open AccessArticle

Tracking of Evasive Objects Using Bistatic Doppler Radar Operating in the Millimeter Wave Regime

by Yair Richter, Jacob Gerasimov, Nezah Balal and Yosef Pinhasi

Remote Sens. 2022, 14(4), 867; https://doi.org/10.3390/rs14040867 - 11 Feb 2022

Cited by 4 | Viewed by 2375

Abstract

In this study, we propose a range detection (RD) ability by a continuous wave (CW) bistatic Doppler radar (RDCWB) of small and fast targets with very high range resolution. The target’s range and velocity are detected simultaneously. The scheme is based on the transmission of a continuous wave (CW) at millimeter wavelength (MMW) and the measurement of the respective Doppler shifts associated with target movements in different directions. The range resolution in this method is determined by the Doppler resolution only, without the necessity to transmit the modulated waveforms as in frequency modulation continuous wave (FMCW) or pulse radars. As the Doppler resolution in CW depends only on the time window required for processing, a very highrange resolution can be obtained. Most other systems that perform target localization use the transmission of wide-band waveforms while measuring the delay of the received signal scattered from the target. In the proposed scheme, the range resolution depends on the processed integration time of the detected signal and the velocity of the target. The transmission is performed from separated antennas and received by a single antenna. The received signal is heterodyned with a sample of the transmitted signal in order to obtain the Doppler shifts associated with the target’s movement. As in a multi-in multi-out (MIMO) configuration, the presented scheme allows for the accumulation of additional information for target classification. Data on the target’s velocity, distance, direction, and instantaneous velocity can be extracted. Using digital processing, with the additional information obtained by analyzing the difference between the resulting intermediate frequencies caused by the Doppler effect, it is possible to calculate the distance between the radar and the target at high resolution in real-time. The presented method, which was tested experimentally, proved to be highly effective, as only one receiver is required for the detection, while the transmission is carried out using a fixed, single-frequency transmission. Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)

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12 pages, 1899 KiB

Open AccessCommunication

An Adaptive Lp Norm Minimization Algorithm for Direction of Arrival Estimation

by Lutao Liu and Zejing Rao

Remote Sens. 2022, 14(3), 766; https://doi.org/10.3390/rs14030766 - 7 Feb 2022

Cited by 12 | Viewed by 2536

Abstract

In this paper, we propose a new direction of arrival (DOA) estimation algorithm, in which DOA estimation is achieved by finding the sparsest support set of multiple measurement vectors (MMV) in an over-complete dictionary. The proposed algorithm is based on

ℓ_{p}

norm [...] Read more.

ℓ_{p}

norm minimization, which belongs to non-convex optimization. Therefore, the quasi-Newton method is used to converge the iterative process. There are two advantages of this algorithm: one is the higher possibility and resolution of distinguishing closely spaced sources, and the other is the adaptive regularization parameter adjustment. Moreover, an accelerating strategy is applied in the computation, and a weighted method of the proposed algorithm is also introduced to improve the accuracy. We conducted experiments to validate the effectiveness of the proposed algorithm. The performance was compared with several popular DOA estimation algorithms and the Cramer–Rao bound (CRB). Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)

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23 pages, 91752 KiB

Open AccessArticle

An Inter-Subband Processing Algorithm for Complex Clutter Suppression in Passive Bistatic Radar

by Luo Zuo, Jun Wang, Jinxin Sui and Nan Li

Remote Sens. 2021, 13(23), 4954; https://doi.org/10.3390/rs13234954 - 6 Dec 2021

Cited by 8 | Viewed by 3203

Abstract

Clutter suppression is a challenging problem for passive bistatic radar systems, given the complexity of actual clutter scenarios (stationary, time-varying and fractional-order clutter). Such complex clutter induces intense sidelobes in the entire range-Doppler plane and thus degrades target-detection performance, especially for low-observable targets. In this paper, a novel method, denominated as the batch version of the extensive cancellation algorithm (ECA) in the frequency domain (ECA-FB), is presented for the first time, to suppress stationary clutter and its sidelobes. Specifically, in this method, the received signal is first divided into short batches in the frequency domain to coarsen the range resolution, and then the clutter is removed over each batch via ECA. Further, to suppress the time-varying clutter, a Doppler-shifted version of ECA-FB (ECA-FBD) is proposed. Compared with the popular ECA and ECA-B methods, the proposed ECA-FB and ECA-FBD obtained superior complex clutter suppression and slow-moving target detection performance with lower computational complexity. A series of simulation and experimental results are provided to demonstrate the validity of the proposed methods. Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)

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14 pages, 4294 KiB

Open AccessTechnical Note

Calibration of MIMO Radar Transmitting and Receiving Array Using Scene Object Measurement

by Łukasz Maślikowski

Remote Sens. 2022, 14(15), 3573; https://doi.org/10.3390/rs14153573 - 25 Jul 2022

Viewed by 1896

Abstract

The paper describes two simple methods allowing phase offsets to be aligned between radiators of transmitting and receiving antenna arrays in a collocated MIMO (Multiple Input Multiple Output) radar. One method uses normalization with averaging, while the second applies SVD (Singular Value Decomposition) of a measurement matrix. To calibrate phase offsets, the measurement of a calibration target at a known angular position must be done. The paper shows numerical comparison to already known method based on normalization by a single element of MIMO measurement matrix and experimental results obtained through the application of the proposed methods to measurement data. Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)

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