Special Issue "Radar and Information Theory"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (20 December 2017).

Special Issue Editors

Prof. Dr. Arye Nehorai
E-Mail Website
Guest Editor
Preston M. Green Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
Interests: modeling; statistical signal processing; information theory; machine learning; sensor networks and fusion; sparse recovery; compressive sensing; optimal design; applications to radar, sonar and biomedicine
Dr. Satyabrata Sen
E-Mail Website
Guest Editor
Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6015, USA
Interests: statistical signal processing, information theory, network fusion, compressive sensing, optimal design, machine learning, and their applications to radar, communication, and sensor arrays
Prof. Dr. Murat Akcakaya
E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, University of Pittsburgh, Pittsburgh, PA 15213, USA
Interests: statistical signal processing, machine learning, information theory, and their applications in radar, brain computer interfaces, and health informatics

Special Issue Information

Dear Colleagues,

Information theory has been applied in radar signal processing for over a half a century now, starting from the pioneering work of Woodward and Davies. However, the research on information theory for radar applications, initially, was not as effective as it had been in communication areas, due to the inherent differences in the concept of “information” in these two fields. The sole purpose of the radar systems is to seek information about a target, which is, in general, non-cooperative, whereas communication systems aim to extract information regarding a transmitting signal/message. Then, with the seminal dissertation work by Bell, information theory regained its footing in radar signal processing to adaptively design the transmitting waveform, which can extract more target-information from the received measurements. Henceforth, information theoretic criteria, especially mutual information and relative entropy (also known as Kullback-Leibler divergence), have been at the core of adaptive radar waveform design algorithms. Additionally, with the recent emergence of cognitive radar, which learns from its experience in addition to sensing and adapting, the concept of information preservation has become even more relevant in the radar receiver processing chain.

Therefore, the aim of this Special Issue is to encourage researchers to present original and recent developments on information theory for the advanced radar systems and algorithms. Applications can include (but are not limited to) target detection, tracking, parameter estimation, target recognition and classification, synthetic/inverse-synthetic radar imaging, electronic counter/counter-counter measures, and adaptive/cognitive waveform design. Analytical development of radar performance bounds/limits, analogous to those in communication, such as Shannon’s theorem, Slepian-Wolf theorem, rate-distortion theory, etc., is also encouraged.

Prof. Dr. Arye Nehorai
Prof. Dr. Murat Akcakaya
Dr. Satyabrata Sen
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. Entropy is an international peer-reviewed open access monthly 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.

Keywords

  • radar signal processing
  • information theoretic measures and criteria
  • information preservation
  • entropy, relative entropy, mutual information
  • Shannon’s theorem, Slepian-Wolf theorem, rate-distortion theory
  • target detection and tracking
  • target recognition and classification
  • parameter estimation and feature extraction
  • adaptive and cognitive radar waveform design
  • synthetic/inverse-synthetic radar imaging
  • information theoretic radar performance bounds

Published Papers (9 papers)

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Research

Article
Vector Bundle Model of Complex Electromagnetic Space and Change Detection
Entropy 2019, 21(1), 10; https://doi.org/10.3390/e21010010 - 23 Dec 2018
Cited by 5 | Viewed by 1421
Abstract
Complex Electromagnetic Space (CEMS), which consists of physical space and the complex electromagnetic environment, plays an essential role in our daily life for supporting remote communication, wireless network, wide-range broadcast, etc. In CEMS, the electromagnetic activities might work differently from the ideal situation; [...] Read more.
Complex Electromagnetic Space (CEMS), which consists of physical space and the complex electromagnetic environment, plays an essential role in our daily life for supporting remote communication, wireless network, wide-range broadcast, etc. In CEMS, the electromagnetic activities might work differently from the ideal situation; the typical case is that undesired signal would disturb the echo of objects and overlap into it resulting in the mismatch of matched filter and the reduction of the probability of detection. The lacking mathematical description of CEMS resulting from the complexity of electromagnetic environment leads to the inappropriate design of detection method. Therefore, a mathematical model of CEMS is desired for integrating the electromagnetic signal in CEMS as a whole and considering the issues in CEMS accurately. This paper puts forward a geometric model of CEMS based on vector bundle, which is an abstract concept in differential geometry and proposes a geometric detector for change detection in CEMS under the geometric model. In the simulation, the proposed geometric detector was compared with energy detector and matched filter in two scenes: passive detection case and active detection case. The results show the proposed geometric detector is better than both energy detector and matched filter with 4∼5 dB improvements of SNR (signal-to-noise ratio) in two scenes. Full article
(This article belongs to the Special Issue Radar and Information Theory)
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Article
Information Geometry for Covariance Estimation in Heterogeneous Clutter with Total Bregman Divergence
Entropy 2018, 20(4), 258; https://doi.org/10.3390/e20040258 - 08 Apr 2018
Cited by 7 | Viewed by 1885
Abstract
This paper presents a covariance matrix estimation method based on information geometry in a heterogeneous clutter. In particular, the problem of covariance estimation is reformulated as the computation of geometric median for covariance matrices estimated by the secondary data set. A new class [...] Read more.
This paper presents a covariance matrix estimation method based on information geometry in a heterogeneous clutter. In particular, the problem of covariance estimation is reformulated as the computation of geometric median for covariance matrices estimated by the secondary data set. A new class of total Bregman divergence is presented on the Riemanian manifold of Hermitian positive-definite (HPD) matrix, which is the foundation of information geometry. On the basis of this divergence, total Bregman divergence medians are derived instead of the sample covariance matrix (SCM) of the secondary data. Unlike the SCM, resorting to the knowledge of statistical characteristics of the sample data, the geometric structure of matrix space is considered in our proposed estimators, and then the performance can be improved in a heterogeneous clutter. At the analysis stage, numerical results are given to validate the detection performance of an adaptive normalized matched filter with our estimator compared with existing alternatives. Full article
(This article belongs to the Special Issue Radar and Information Theory)
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Article
Modulation Signal Recognition Based on Information Entropy and Ensemble Learning
Entropy 2018, 20(3), 198; https://doi.org/10.3390/e20030198 - 16 Mar 2018
Cited by 23 | Viewed by 2070
Abstract
In this paper, information entropy and ensemble learning based signal recognition theory and algorithms have been proposed. We have extracted 16 kinds of entropy features out of 9 types of modulated signals. The types of information entropy used are numerous, including Rényi entropy [...] Read more.
In this paper, information entropy and ensemble learning based signal recognition theory and algorithms have been proposed. We have extracted 16 kinds of entropy features out of 9 types of modulated signals. The types of information entropy used are numerous, including Rényi entropy and energy entropy based on S Transform and Generalized S Transform. We have used three feature selection algorithms, including sequence forward selection (SFS), sequence forward floating selection (SFFS) and RELIEF-F to select the optimal feature subset from 16 entropy features. We use five classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), Adaboost, Gradient Boosting Decision Tree (GBDT) and eXtreme Gradient Boosting (XGBoost) to classify the original feature set and the feature subsets selected by different feature selection algorithms. The simulation results show that the feature subsets selected by SFS and SFFS algorithms are the best, with a 48% increase in recognition rate over the original feature set when using KNN classifier and a 34% increase when using SVM classifier. For the other three classifiers, the original feature set can achieve the best recognition performance. The XGBoost classifier has the best recognition performance, the overall recognition rate is 97.74% and the recognition rate can reach 82% when the signal to noise ratio (SNR) is −10 dB. Full article
(This article belongs to the Special Issue Radar and Information Theory)
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Article
Low Probability of Intercept-Based Radar Waveform Design for Spectral Coexistence of Distributed Multiple-Radar and Wireless Communication Systems in Clutter
Entropy 2018, 20(3), 197; https://doi.org/10.3390/e20030197 - 16 Mar 2018
Cited by 10 | Viewed by 2262
Abstract
In this paper, the problem of low probability of intercept (LPI)-based radar waveform design for distributed multiple-radar system (DMRS) is studied, which consists of multiple radars coexisting with a wireless communication system in the same frequency band. The primary objective of the multiple-radar [...] Read more.
In this paper, the problem of low probability of intercept (LPI)-based radar waveform design for distributed multiple-radar system (DMRS) is studied, which consists of multiple radars coexisting with a wireless communication system in the same frequency band. The primary objective of the multiple-radar system is to minimize the total transmitted energy by optimizing the transmission waveform of each radar with the communication signals acting as interference to the radar system, while meeting a desired target detection/characterization performance. Firstly, signal-to-clutter-plus-noise ratio (SCNR) and mutual information (MI) are used as the practical metrics to evaluate target detection and characterization performance, respectively. Then, the SCNR- and MI-based optimal radar waveform optimization methods are formulated. The resulting waveform optimization problems are solved through the well-known bisection search technique. Simulation results demonstrate utilizing various examples and scenarios that the proposed radar waveform design schemes can evidently improve the LPI performance of DMRS without interfering with friendly communications. Full article
(This article belongs to the Special Issue Radar and Information Theory)
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Article
Kullback–Leibler Divergence Based Distributed Cubature Kalman Filter and Its Application in Cooperative Space Object Tracking
Entropy 2018, 20(2), 116; https://doi.org/10.3390/e20020116 - 10 Feb 2018
Cited by 15 | Viewed by 2116
Abstract
In this paper, a distributed Bayesian filter design was studied for nonlinear dynamics and measurement mapping based on Kullback–Leibler divergence. In a distributed structure, the nonlinear filter becomes a challenging problem, since each sensor cannot access the global measurement likelihood function over the [...] Read more.
In this paper, a distributed Bayesian filter design was studied for nonlinear dynamics and measurement mapping based on Kullback–Leibler divergence. In a distributed structure, the nonlinear filter becomes a challenging problem, since each sensor cannot access the global measurement likelihood function over the whole network, and some sensors have weak observability of the state. To solve the problem in a sensor network, the distributed Bayesian filter problem was converted into an optimization problem by maximizing a posterior method. The global cost function over the whole network was decomposed into the sum of the local cost function, where the local cost function can be solved by each sensor. With the help of the Kullback–Leibler divergence, the global estimate was approximated in each sensor by communicating with its neighbors. Based on the proposed distributed Bayesian filter structure, a distributed cubature Kalman filter (DCKF) was proposed. Finally, a cooperative space object tracking problem was studied for illustration. The simulation results demonstrated that the proposed algorithm can solve the issues of varying communication topology and weak observability of some sensors. Full article
(This article belongs to the Special Issue Radar and Information Theory)
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Article
Adaptive Waveform Design for Cognitive Radar in Multiple Targets Situation
Entropy 2018, 20(2), 114; https://doi.org/10.3390/e20020114 - 09 Feb 2018
Cited by 7 | Viewed by 2280
Abstract
In this paper, the problem of cognitive radar (CR) waveform optimization design for target detection and estimation in multiple extended targets situations is investigated. This problem is analyzed in signal-dependent interference, as well as additive channel noise for extended targets with unknown target [...] Read more.
In this paper, the problem of cognitive radar (CR) waveform optimization design for target detection and estimation in multiple extended targets situations is investigated. This problem is analyzed in signal-dependent interference, as well as additive channel noise for extended targets with unknown target impulse response (TIR). To address this problem, an improved algorithm is employed for target detection by maximizing the detection probability of the received echo on the promise of ensuring the TIR estimation precision. In this algorithm, an additional weight vector is introduced to achieve a trade-off among different targets. Both the estimate of TIR and transmit waveform can be updated at each step based on the previous step. Under the same constraint on waveform energy and bandwidth, the information theoretical approach is also considered. In addition, the relationship between the waveforms that are designed based on the two criteria is discussed. Unlike most existing works that only consider single target with temporally correlated characteristics, waveform design for multiple extended targets is considered in this method. Simulation results demonstrate that compared with linear frequency modulated (LFM) signal, waveforms designed based on maximum detection probability and maximum mutual information (MI) criteria can make radar echoes contain more multiple-target information and improve radar performance as a result. Full article
(This article belongs to the Special Issue Radar and Information Theory)
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Article
Bayesian Nonlinear Filtering via Information Geometric Optimization
Entropy 2017, 19(12), 655; https://doi.org/10.3390/e19120655 - 01 Dec 2017
Cited by 6 | Viewed by 2003
Abstract
In this paper, Bayesian nonlinear filtering is considered from the viewpoint of information geometry and a novel filtering method is proposed based on information geometric optimization. Under the Bayesian filtering framework, we derive a relationship between the nonlinear characteristics of filtering and the [...] Read more.
In this paper, Bayesian nonlinear filtering is considered from the viewpoint of information geometry and a novel filtering method is proposed based on information geometric optimization. Under the Bayesian filtering framework, we derive a relationship between the nonlinear characteristics of filtering and the metric tensor of the corresponding statistical manifold. Bayesian joint distributions are used to construct the statistical manifold. In this case, nonlinear filtering can be converted to an optimization problem on the statistical manifold and the adaptive natural gradient descent method is used to seek the optimal estimate. The proposed method provides a general filtering formulation and the Kalman filter, the Extended Kalman filter (EKF) and the Iterated Extended Kalman filter (IEKF) can be seen as special cases of this formulation. The performance of the proposed method is evaluated on a passive target tracking problem and the results demonstrate the superiority of the proposed method compared to various Kalman filter methods. Full article
(This article belongs to the Special Issue Radar and Information Theory)
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Article
The Geometry of Signal Detection with Applications to Radar Signal Processing
Entropy 2016, 18(11), 381; https://doi.org/10.3390/e18110381 - 25 Oct 2016
Cited by 26 | Viewed by 3090
Abstract
The problem of hypothesis testing in the Neyman–Pearson formulation is considered from a geometric viewpoint. In particular, a concise geometric interpretation of deterministic and random signal detection in the philosophy of information geometry is presented. In such a framework, both hypotheses and detectors [...] Read more.
The problem of hypothesis testing in the Neyman–Pearson formulation is considered from a geometric viewpoint. In particular, a concise geometric interpretation of deterministic and random signal detection in the philosophy of information geometry is presented. In such a framework, both hypotheses and detectors can be treated as geometrical objects on the statistical manifold of a parameterized family of probability distributions. Both the detector and detection performance are geometrically elucidated in terms of the Kullback–Leibler divergence. Compared to the likelihood ratio test, the geometric interpretation provides a consistent but more comprehensive means to understand and deal with signal detection problems in a rather convenient manner. Example of the geometry based detector in radar constant false alarm rate (CFAR) detection is presented, which shows its advantage over the classical processing method. Full article
(This article belongs to the Special Issue Radar and Information Theory)
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Article
The Constant Information Radar
Entropy 2016, 18(9), 338; https://doi.org/10.3390/e18090338 - 19 Sep 2016
Cited by 12 | Viewed by 3593
Abstract
The constant information radar, or CIR, is a tracking radar that modulates target revisit time by maintaining a fixed mutual information measure. For highly dynamic targets that deviate significantly from the path predicted by the tracking motion model, the CIR adjusts by illuminating [...] Read more.
The constant information radar, or CIR, is a tracking radar that modulates target revisit time by maintaining a fixed mutual information measure. For highly dynamic targets that deviate significantly from the path predicted by the tracking motion model, the CIR adjusts by illuminating the target more frequently than it would for well-modeled targets. If SNR is low, the radar delays revisit to the target until the state entropy overcomes noise uncertainty. As a result, we show that the information measure is highly dependent on target entropy and target measurement covariance. A constant information measure maintains a fixed spectral efficiency to support the RF convergence of radar and communications. The result is a radar implementing a novel target scheduling algorithm based on information instead of heuristic or ad hoc methods. The CIR mathematically ensures that spectral use is justified. Full article
(This article belongs to the Special Issue Radar and Information Theory)
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