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Special Issue "Sensors In Target Detection"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors, Control, and Telemetry".

Deadline for manuscript submissions: closed (15 May 2019).

Special Issue Editors

Prof. Thierry Bouwmans
E-Mail Website
Guest Editor
Universitè la Rochelle 17000 La Rochelle, France
Interests: Data and Sensor Fusion; Low rank plus sparse decomposition; Robust principal component analysis; Image processing; Radar Signal processing
Special Issues and Collections in MDPI journals
Dr. Filippo Biondi
E-Mail Website
Guest Editor
Joint Satellite Remote Sensing Center, Italian MoD, Rome RM, Italy
Interests: Synthetic Aperture Radar (SAR) Spotlight and Stripmap focusing; SAR Interferometry and Differential SAR interferometry; Coherent filtering of SAR images; Precise atmospheric phase error estimation; 3-D SAR and polarimetric SAR Tomography; Polarimetric SAR assisted by Multi Chromatic Analysis (MCA) for target future extraction; Advanced algorithms development for Oil-Spill detection; SAR video production using the single SAR image by MCA; SAR refocusing for maritime surveillance; Low rank plus sparse decomposition for automatic future extraction; Super Resolution by spectrum extrapolation; Polarimetric Permanent Scatterers (PS) for precise 3D reconstruction and precise geolocation (Polarimetric-3-D Targeting); Multidimensional subsidence estimation by multiple look-angles of satellite observations and precise velocity/acceleration estimation
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

We invite manuscripts for this forthcoming Special Issue on all aspects of research and development related to these scientific and technical areas and in all possible domains of applications. Original research articles that focus on sensors in target detection and experimental validation are welcome to highlight novel approaches, recent advancements, and new application areas or that solve an important problem. Potential topics include (but are not limited to):

  • Visible cameras, IR cameras, multi-spectral sensors
  • Radar and sonar sensors
  • Sensor fusion
  •  Sensor placement, Sensor coverage
  • Wireless sensor networks
  • Synthetic-aperture radar (SAR) imaging, polarimetric (PolSAR), Interferometric SAR (InSAR), Polarimetric interferometric SAR (PolInSAR), Differential interferometric SAR (DinSAR), Persistent scatterer interferometry (PSInSAR), Multi-chromatic analysis (MCA) for SAR (MCA-SAR), SAR tomography
  • Pixel tracking in synthetic aperture radar for glacier applications
  • Pixel tracking in synthetic aperture radars for maritime surveillance
  • Along-track interferometry (ATI) for sea, lake and river current velocity estimation (distributed targets) and for maritime surveillance (coherent targets)
  • High-resolution, wide-swath strategies for synthetic aperture radars
  • Geosynchronous synthetic aperture radars, high-resolution methods and strategies
  • Hyperspectral satellite imaging classification and super-resolution techniques
  • Ground/marine moving target indication (MTI)
  • Direction-of-arrival tracking
  •  Deep-learning applied to remote sensing

Authors are invited to contact the Guest Editors prior to submission if they are uncertain whether their work falls within the general scope of this Special Issue.

Prof. Dr. Thierry Bouwmans
Dr. Filippo Biondi

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 papers will be 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. Sensors 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 1800 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.

Published Papers (17 papers)

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Research

Open AccessArticle
Subdiffusive Source Sensing by a Regional Detection Method
Sensors 2019, 19(16), 3504; https://doi.org/10.3390/s19163504 - 10 Aug 2019
Abstract
Motivated by the fact that the danger may increase if the source of pollution problem remains unknown, in this paper, we study the source sensing problem for subdiffusion processes governed by time fractional diffusion systems based on a limited number of sensor measurements. [...] Read more.
Motivated by the fact that the danger may increase if the source of pollution problem remains unknown, in this paper, we study the source sensing problem for subdiffusion processes governed by time fractional diffusion systems based on a limited number of sensor measurements. For this, we first give some preliminary notions such as source, detection and regional spy sensors, etc. Secondly, we investigate the characterizations of regional strategic sensors and regional spy sensors. A regional detection approach on how to solve the source sensing problem of the considered system is then presented by using the Hilbert uniqueness method (HUM). This is to identify the unknown source only in a subregion of the whole domain, which is easier to be implemented and could save a lot of energy resources. Numerical examples are finally included to test our results. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices
Sensors 2019, 19(15), 3371; https://doi.org/10.3390/s19153371 - 31 Jul 2019
Abstract
In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-the-art embedded hardware system [...] Read more.
In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-the-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one was designed using a Jetson TX or AGX Xavier, and the other was based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-the-art deep learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates, as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep learning-based association metric approach with simple online and real-time tracking (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
Low-Slow-Small (LSS) Target Detection Based on Micro Doppler Analysis in Forward Scattering Radar Geometry
Sensors 2019, 19(15), 3332; https://doi.org/10.3390/s19153332 - 29 Jul 2019
Abstract
The increase in drone misuse by civilian apart from military applications is alarming and need to be addressed. This drone is characterized as a low altitude, slow speed, and small radar cross-section (RCS) (LSS) target and is considered difficult to be detected and [...] Read more.
The increase in drone misuse by civilian apart from military applications is alarming and need to be addressed. This drone is characterized as a low altitude, slow speed, and small radar cross-section (RCS) (LSS) target and is considered difficult to be detected and classified among other biological targets, such as insects and birds existing in the same surveillance volume. Although several attempts reported the successful drone detection on radio frequency-based (RF), thermal, acoustic, video imaging, and other non-technical methods, however, there are also many limitations. Thus, this paper investigated a micro-Doppler analysis from drone rotating blades for detection in a special Forward Scattering Radar (FSR) geometry. The paper leveraged the identified benefits of FSR mode over conventional radars, such as improved radar cross-section (RCS) value irrespective of radar absorbing material (RAM), direct signal perturbation, and high resolutions. To prove the concept, a received signal model for micro-Doppler analysis, a simulation work, and experimental validation are elaborated and explained in the paper. Two rotating blades aspect angle scenarios were considered, which are (i) when drone makes a turn, the blade cross-sectional area faces the receiver and (ii) when drone maneuvers normally, the cross-sectional blade faces up. The FSR system successfully detected a commercial drone and extracted the micro features of a rotating blade. It further verified the feasibility of using a parabolic dish antenna as a receiver in FSR geometry; this marked an appreciable achievement towards the FSR system performance, which in future could be implemented as either active or passive FSR system. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
Hough Transform-Based Large Dynamic Reflection Coefficient Micro-Motion Target Detection in SAR
Sensors 2019, 19(14), 3227; https://doi.org/10.3390/s19143227 - 22 Jul 2019
Abstract
Special phase modulation of SAR echoes resulted from target rotation or vibration, is a phenomenon called the micro-Doppler (m-D) effect. Such an effect offers favorable information for micro-motion (MM) target detection, thereby improving the performance of the synthetic aperture radar (SAR) system. However, [...] Read more.
Special phase modulation of SAR echoes resulted from target rotation or vibration, is a phenomenon called the micro-Doppler (m-D) effect. Such an effect offers favorable information for micro-motion (MM) target detection, thereby improving the performance of the synthetic aperture radar (SAR) system. However, when there are MM targets with large differences in reflection coefficient, the weak reflection components will be difficult to be detected. To find a solution to this problem, we propose a novel algorithm. First, we extract and detect the strongest reflection component. By removing the strongest reflection component from the original azimuth echo one by one, we realize the detection of reflection components sequentially, from the strongest to the weakest. Our algorithm applies to detecting MM targets with different reflection coefficients and has high precision of parameter estimation. The results of simulation and field experiments verify the advantages of the algorithm. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
An Efficient Extended Targets Detection Framework Based on Sampling and Spatio-Temporal Detection
Sensors 2019, 19(13), 2912; https://doi.org/10.3390/s19132912 - 01 Jul 2019
Abstract
Excellent performance, real-time and low memory requirement are three vital requirements for target detection in high resolution marine radar system. Unfortunately, many current state-of-the-art methods merely achieve excellent performance when coping with highly complex scenes. In fact, a common problem is that real-time [...] Read more.
Excellent performance, real-time and low memory requirement are three vital requirements for target detection in high resolution marine radar system. Unfortunately, many current state-of-the-art methods merely achieve excellent performance when coping with highly complex scenes. In fact, a common problem is that real-time processing, low memory requirement and remarkable detection ability are difficult to coordinate. To address this issue, we propose a novel detection framework which bases its principle on sampling and spatiotemporal detection. The framework consists of two stages, coarse detection and fine detection. Sampling-based coarse detection is designed to guarantee the real-time processing and low memory requirements by locating the area where targets may exist in advance. Different from former detection methods, multi-scan video data are utilized. In the stage of fine detection, the candidate areas are grouped into three categories: single target, dense targets and sea clutter. Different approaches for processing the different categories are implemented to achieve excellent performance. The superiority of the proposed framework beyond state-of-the-art baselines is well substantiated in this work. Low memory requirement of the proposed framework was verified by theoretical analysis. Real-time processing capability was verified by the video data of two real scenarios. Synthetic data were tested to show the improvement in tracking performance by using the proposed detection framework. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
Impact of Thermal Control Measures on the Imaging Quality of an Aerial Optoelectronic Sensor
Sensors 2019, 19(12), 2753; https://doi.org/10.3390/s19122753 - 19 Jun 2019
Abstract
The image resolution is the most important performance parameter for an aerial optoelectronic sensor. Existing thermal control methods cannot eliminate the sensor’s temperature gradient, making the image resolution difficult to further improve. This article analyzes the different impacts of temperature changes on the [...] Read more.
The image resolution is the most important performance parameter for an aerial optoelectronic sensor. Existing thermal control methods cannot eliminate the sensor’s temperature gradient, making the image resolution difficult to further improve. This article analyzes the different impacts of temperature changes on the imaging resolution and proposes modifications. Firstly, the sensor was subjected to thermo-optical simulation by means of finite element analysis, and the different impacts of temperature changes on the imaging quality were analyzed. According to the simulation results, an active thermal control method is suggested to enhance the temperature uniformity of the sensor. Considering the impacts of active and passive thermal control measures, thermal optical analysis was carried out to predict the performance of the sensor. The results of the analysis show that the imaging quality of the sensor has been significantly improved. The experimental results show that the image resolution of the optoelectronic sensor improved from 47 to 59 lp/mm, which demonstrates that the sensor can produce a high image quality in a low-temperature environment. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
Convolutional Recurrent Neural Network-Based Event Detection in Tunnels Using Multiple Microphones
Sensors 2019, 19(12), 2695; https://doi.org/10.3390/s19122695 - 14 Jun 2019
Abstract
This paper proposes a sound event detection (SED) method in tunnels to prevent further uncontrollable accidents. Tunnel accidents are accompanied by crashes and tire skids, which usually produce abnormal sounds. Since the tunnel environment always has a severe level of noise, the detection [...] Read more.
This paper proposes a sound event detection (SED) method in tunnels to prevent further uncontrollable accidents. Tunnel accidents are accompanied by crashes and tire skids, which usually produce abnormal sounds. Since the tunnel environment always has a severe level of noise, the detection accuracy can be greatly reduced in the existing methods. To deal with the noise issue in the tunnel environment, the proposed method involves the preprocessing of tunnel acoustic signals and a classifier for detecting acoustic events in tunnels. For preprocessing, a non-negative tensor factorization (NTF) technique is used to separate the acoustic event signal from the noisy signal in the tunnel. In particular, the NTF technique developed in this paper consists of source separation and online noise learning. In other words, the noise basis is adapted by an online noise learning technique for enhancement in adverse noise conditions. Next, a convolutional recurrent neural network (CRNN) is extended to accommodate the contributions of the separated event signal and noise to the event detection; thus, the proposed CRNN is composed of event convolution layers and noise convolution layers in parallel followed by recurrent layers and the output layer. Here, a set of mel-filterbank feature parameters is used as the input features. Evaluations of the proposed method are conducted on two datasets: a publicly available road audio events dataset and a tunnel audio dataset recorded in a real traffic tunnel for six months. In the first evaluation where the background noise is low, the proposed CRNN-based SED method with online noise learning reduces the relative recognition error rate by 56.25% when compared to the conventional CRNN-based method with noise. In the second evaluation, where the tunnel background noise is more severe than in the first evaluation, the proposed CRNN-based SED method yields superior performance when compared to the conventional methods. In particular, it is shown that among all of the compared methods, the proposed method with the online noise learning provides the best recognition rate of 91.07% and reduces the recognition error rates by 47.40% and 28.56% when compared to the Gaussian mixture model (GMM)–hidden Markov model (HMM)-based and conventional CRNN-based SED methods, respectively. The computational complexity measurements also show that the proposed CRNN-based SED method requires a processing time of 599 ms for both the NTF-based source separation with online noise learning and CRNN classification when the tunnel noisy signal is one second long, which implies that the proposed method detects events in real-time. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
A Target Identification Method for the Millimeter Wave Seeker via Correlation Matching and Beam Pointing
Sensors 2019, 19(11), 2530; https://doi.org/10.3390/s19112530 - 03 Jun 2019
Abstract
Target identification is a challenging task under land backgrounds for the millimeter wave (MMW) seeker, especially under complex backgrounds. Focusing on the problem, an effective method combining correlation matching and beam pointing is proposed in this paper. In the beginning, seeker scanning for [...] Read more.
Target identification is a challenging task under land backgrounds for the millimeter wave (MMW) seeker, especially under complex backgrounds. Focusing on the problem, an effective method combining correlation matching and beam pointing is proposed in this paper. In the beginning, seeker scanning for target detection is conducted in two rounds, and target information of the detected targets is stored for correlation matching. Point or body feature judgment is implemented by using high resolution range profile (HRRP). Then, the error distribution zone is constructed with the beam pointing as the origin. In the end, we identify the target by searching the one which lies in the closest error distribution from the beam pointing center. The effectiveness of the proposed method is verified by using mooring test-fly and real flight data. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
Energy-Efficient Spatial Query-Centric Geographic Routing Protocol in Wireless Sensor Networks
Sensors 2019, 19(10), 2363; https://doi.org/10.3390/s19102363 - 22 May 2019
Cited by 1
Abstract
In data-centric wireless sensor networks (WSNs), sensing data have a high time–space correlation. Most queries are spatial and used to obtain data in a defined region. Geographic routing (GR) protocols are the optimal choice for routing spatial queries. However, several drawbacks still exist [...] Read more.
In data-centric wireless sensor networks (WSNs), sensing data have a high time–space correlation. Most queries are spatial and used to obtain data in a defined region. Geographic routing (GR) protocols are the optimal choice for routing spatial queries. However, several drawbacks still exist in GRs, and these the include premature death of nodes and communication latency, which result in reduced network life and query efficiency. A new clustering GR protocol called quadtree grid (QTGrid) was proposed in this study to save energy and improve spatial query efficiency. First, the monitoring area was logically divided into clusters by a quadtree structure, and each grid’s location was encoded to reduce the memory overhead. Second, cluster head (CH) nodes were selected based on several metrics, such as distance from the candidate node to the grid center and adjacent CHs and residual energy. Third, the next-hop routing node was selected depending on the residual energy of the candidate node and its distance to the sink node. Lastly, a lossless data aggregation algorithm and a flexible spatial query algorithm were adopted to reduce the transmission of redundant data and meet the application requirements, respectively. Simulation results showed that compared with three related protocols, QTGrid has lower energy consumption and higher spatial query efficiency and is more suitable for large-scale WSN spatial query application scenarios. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
Wi-Alarm: Low-Cost Passive Intrusion Detection Using WiFi
Sensors 2019, 19(10), 2335; https://doi.org/10.3390/s19102335 - 21 May 2019
Abstract
In this paper, we present a WiFi-based intrusion detection system called Wi-Alarm. Motivated by our observations and analysis that raw channel state information (CSI) of WiFi is sensitive enough to monitor human motion, Wi-Alarm omits data preprocessing. The mean and variance of the [...] Read more.
In this paper, we present a WiFi-based intrusion detection system called Wi-Alarm. Motivated by our observations and analysis that raw channel state information (CSI) of WiFi is sensitive enough to monitor human motion, Wi-Alarm omits data preprocessing. The mean and variance of the amplitudes of raw CSI data are used for feature extraction. Then, a support vector machine (SVM) algorithm is applied to determine detection results. We prototype Wi-Alarm on commercial WiFi devices and evaluate it in a typical indoor scenario. Results show that Wi-Alarm reduces much computational expense without losing accuracy and robustness. Moreover, different influence factors are also discussed in this paper. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
Active Hyperspectral Sensor Based on MEMS Fabry-Pérot Interferometer
Sensors 2019, 19(9), 2192; https://doi.org/10.3390/s19092192 - 12 May 2019
Abstract
An active hyperspectral sensor (AHS) was developed for target detection and classification applications. AHS measures light scattered from a target, illuminated by a broadband near-infrared supercontinuum (SC) light source. Spectral discrimination is based on a voltage-tunable MEMS Fabry-Pérot Interferometer (FPI). The broadband light [...] Read more.
An active hyperspectral sensor (AHS) was developed for target detection and classification applications. AHS measures light scattered from a target, illuminated by a broadband near-infrared supercontinuum (SC) light source. Spectral discrimination is based on a voltage-tunable MEMS Fabry-Pérot Interferometer (FPI). The broadband light is filtered by the FPI prior to transmitting, allowing for a high spectral-power density within the eye-safety limits. The approach also allows for a cost-efficient correction of the SC instability, employing a non-dispersive reference detector. A precision of 0.1% and long-term stability better than 0.5% were demonstrated in laboratory tests. The prototype was mounted on a car for field measurements. Several road types and objects were distinguished based on the spectral response of the sensor targeted in front of the car. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
Performance Bound for Joint Multiple Parameter Target Estimation in Sparse Stepped-Frequency Radar: A Comparison Analysis
Sensors 2019, 19(9), 2002; https://doi.org/10.3390/s19092002 - 29 Apr 2019
Abstract
A performance bound—Cramér-Rao lower bound (CRLB) for target estimation and detection in sparse stepped frequency radars is presented. The vector formulation of this CRLB is used to obtain a lower bound on the estimation error. The estimation performance can be transformed into different [...] Read more.
A performance bound—Cramér-Rao lower bound (CRLB) for target estimation and detection in sparse stepped frequency radars is presented. The vector formulation of this CRLB is used to obtain a lower bound on the estimation error. The estimation performance can be transformed into different types of CRLB structures. Therefore, the expressions of bounds under three equivalent models are derived separately: time delay and Doppler stretch estimator, joint multiple parameter estimator, and sparse-based estimator. The variables to be estimated include the variances of unknown noise, range, velocity, and the real and imaginary parts of the amplitude. A general performance expression is proposed by considering the echo of the target in the line-of-sight. When the relationship between CRLB and various parameters are discussed in detail, the specific effect of waveform parameters on a single CRLB is compared and analyzed. Numerical simulations demonstrated that the resulting CRLB exhibits considerable theoretical and practical significance for the selection of optimal waveform parameters. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
A Doppler Range Compensation for Step-Frequency Continuous-Wave Radar for Detecting Small UAV
Sensors 2019, 19(6), 1331; https://doi.org/10.3390/s19061331 - 16 Mar 2019
Abstract
Step-frequency continuous-wave (SFCW) modulation can have a role in the detection of small unmanned aerial vehicles (UAV) at short range (less than 1–2 km). In this paper, the theory of SFCW range detection is reviewed, and a specific method for correcting the possible [...] Read more.
Step-frequency continuous-wave (SFCW) modulation can have a role in the detection of small unmanned aerial vehicles (UAV) at short range (less than 1–2 km). In this paper, the theory of SFCW range detection is reviewed, and a specific method for correcting the possible range shift due to the Doppler effect is devised. The proposed method was tested in a controlled experimental set-up, where a free-falling target (i.e., a corner reflector) was correctly detected by an SFCW radar. This method was finally applied in field for short-range detection of a small UAV. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
A High-Multi Target Resolution Focal Plane Array-Based Laser Detection and Ranging Sensor
Sensors 2019, 19(5), 1210; https://doi.org/10.3390/s19051210 - 09 Mar 2019
Cited by 2
Abstract
This paper introduces a digital-assisted multiple echo detection scheme, which utilizes the waste time of the full serial data readout period in a focal plane array (FPA)-based laser detection and ranging (LADAR) receiver. With the support of an external digital signal processor (DSP) [...] Read more.
This paper introduces a digital-assisted multiple echo detection scheme, which utilizes the waste time of the full serial data readout period in a focal plane array (FPA)-based laser detection and ranging (LADAR) receiver. With the support of an external digital signal processor (DSP) and additional analog memory inserted into the receiver, the proposed readout scheme can effectively enhance multi-target resolution (MTR) three times higher than the conventional FPA-based LADAR, while maintaining low power consumption and a small area. A prototype chip was fabricated in a 0.18-μm CMOS process with an 8 × 8 FPA configuration, where each single receiver pixel occupied an area of 100 μm × 100 μm. The single receiver achieved an MTR of 20 ns with 7.47 mW power dissipation, an input referred noise current of 4.48 pA/√Hz with a bandwidth 530 MHz, a minimum detectable signal (MDS) of 340 nA, a maximum walk error of 2.2 ns, and a maximum non-linearity of 0.05% among the captured multiple echo images. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
A Two-Dimensional Adaptive Target Detection Algorithm in the Compressive Domain
Sensors 2019, 19(3), 567; https://doi.org/10.3390/s19030567 - 29 Jan 2019
Cited by 1
Abstract
By applying compressive sensing to infrared imaging systems, the sampling and transmitting time can be remarkably reduced. Therefore, in order to meet the real-time requirements of infrared small target detection tasks in the remote sensing field, many approaches based on compressive sensing have [...] Read more.
By applying compressive sensing to infrared imaging systems, the sampling and transmitting time can be remarkably reduced. Therefore, in order to meet the real-time requirements of infrared small target detection tasks in the remote sensing field, many approaches based on compressive sensing have been proposed. However, these approaches need to reconstruct the image from the compressive domain before detecting targets, which is inefficient due to the complex recovery algorithms. To overcome this drawback, in this paper, we propose a two-dimensional adaptive threshold algorithm based on compressive sensing for infrared small target detection. Instead of processing the reconstructed image, our algorithm focuses on directly detecting the target in the compressive domain, which reduces both the time and memory requirements for image recovery. First, we directly subtract the spatial background image in the compressive domain of the original image sampled by the two-dimensional measurement model. Then, we use the properties of the Gram matrix to decode the subtracted image for further processing. Finally, we detect the targets by employing the advanced adaptive threshold method to the decoded image. Experiments show that our algorithm can achieve an average 100% detection rate, with a false alarm rate lower than 0.4%, and the computational time is within 0.3 s, on average. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
A Clutter-Analysis-Based STAP for Moving FOD Detection on Runways
Sensors 2019, 19(3), 549; https://doi.org/10.3390/s19030549 - 29 Jan 2019
Abstract
Security risks and economic losses of civil aviation caused by Foreign Object Debris (FOD) have increased rapidly. Synthetic Aperture Radars (SARs) with high resolutions potentially have the capability to detect FODs on the runways, but the target echo is hard to be distinguished [...] Read more.
Security risks and economic losses of civil aviation caused by Foreign Object Debris (FOD) have increased rapidly. Synthetic Aperture Radars (SARs) with high resolutions potentially have the capability to detect FODs on the runways, but the target echo is hard to be distinguished from strong clutter. This paper proposes a clutter-analysis-based Space-time Adaptive Processing (STAP) method in order to obtain effective clutter suppression and moving FOD indication, under inhomogeneous clutter background. Specifically, we first divide the radar coverage into equal scattering cells in the rectangular coordinates system rather than the polar ones. We then measure normalized RCSs within the X-band and employ the acquired results to modify the parameters of traditional models. Finally, we describe the clutter expressions as responses of the scattering cells in space and time domain to obtain the theoretical clutter covariance. Experimental results at 10 GHz show that FODs with a reflection higher than −30 dBsm can be effectively detected by a Linear Constraint Minimum Variance (LCMV) filter in azimuth when the noise is −60 dBm. It is also validated to indicate a −40 dBsm target in Doppler. Our approach can obtain effective clutter suppression 60dB deeper than the training-sample-coupled STAP under the same conditions. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
An Improved Inverse Beamforming Method: Azimuth Resolution Analysis for Weak Target Detection
Sensors 2018, 18(12), 4160; https://doi.org/10.3390/s18124160 - 27 Nov 2018
Cited by 1
Abstract
The inverse beamforming (IBF) is a mature method to improve azimuth resolution. However, for weak targets it is not applicable as IBF enhances side lobes. In this paper, an improved IBF algorithm is proposed to raise the azimuth resolution under the premise of [...] Read more.
The inverse beamforming (IBF) is a mature method to improve azimuth resolution. However, for weak targets it is not applicable as IBF enhances side lobes. In this paper, an improved IBF algorithm is proposed to raise the azimuth resolution under the premise of ensuring the detection ability for weak targets. Firstly, from the point of phase compensation, we analyze the cause of side lobes when IBF is applied. Then the improved IBF algorithm recorded as GIBF (the improved inverse beamforming) is proposed by changing the Toeplitz average into the phase construction. The theoretical derivation and simulation data processing show the proposed method can improve the resolution of the N sensors to the standard of 2N − 1 sensors under different signal-to-noise ratios. Compared with IBF, GIBF has great advantages in detecting weak targets. Passive sonar data are used to further verify the advantages of GIBF; the trajectories on azimuth history diagrams become clear, the azimuth resolution is improved, and the detection ability for weak targets is still robust. In addition, GIBF is combined with the common DOA (direction of arrival) estimation algorithms, such as conventional beamforming and minimum variance distortionless signal response, which proves the applicability of the algorithm. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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