Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS).

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Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

There has been a great deal of material in the area of discrete-time transforms that has been published in recent years. This book does an excellent job of presenting important aspects of such material in a clear manner. The book has 11 chapters and a very useful appendix. Seven of these chapters are essentially devoted to the Fourier series/transform, discrete Fourier transform, fast Fourier transform (FFT), and applications of the FFT in the area of spectral estimation. Chapters 8 through 10 deal with many other discrete-time transforms and algorithms to compute them. Of these transforms, the KarhunenLoeve, the discrete cosine, and the Walsh-Hadamard transform are perhaps the most well-known. A lucid discussion of number theoretic transforms i5 presented in Chapter 11. This reviewer feels that the authors have done a fine job of compiling the pertinent material and presenting it in a concise and clear manner. There are a number of problems at the end of each chapter, an appreciable number of which are challenging. The authors have included a comprehensive set of references at the end of the book. In brief, this book is a valuable contribution to the general areas of signal processing and communications. It can be used for a graduate level course in perhaps two ways. One would be to cover the first seven chapters in great detail. The other would be to cover the whole book by focussing on different topics in a selective manner. This book  by  Elliott  and Rao  is extremely  useful to researchers/engineers who are working  in the areas of signal processing and communications. It i s also an excellent reference book, and hence a valuable addition to one’s library

http://ieeexplore.ieee.org/iel5/5/31347/01457444.pdf

Multirate digital signal processing

http://www2.math.umd.edu/~rvbalan/PAPERS/MyPapers/AbsValFrame_ACHAarticle.pdf

On signal reconstruction without phase

Many methods have been proposed to improve the performance of synthetic aperture radar (SAR) target recognition but seldom consider the issues in real-world recognition systems, such as the invariance under target translation, the invariance under speckle variation in different observations, and the tolerance of pose missing in training data. In this letter, we investigate the capability of a deep convolutional neural network (CNN) combined with three types of data augmentation operations in SAR target recognition. Experimental results demonstrate the effectiveness and efficiency of the proposed method. The best performance is obtained by using the CNN trained by all types of augmentation operations, showing that it is a practical approach for target recognition in challenging conditions of target translation, random speckle noise, and missing pose.

Convolutional Neural Network With Data Augmentation for SAR Target Recognition

This is a book review of 'The Micro-Doppler Effect in Radar' by V. C. Chen. Artech House, 16 Sussex Street, London, SW1V 4RW, UK. 2011. 290pp + diskette. Illustrated. £90. ISBN 978-1-60807-057-2.

'The Micro-Doppler Effect in Radar' by V.C. Chen

1. Introduction. Characterization of Signals. Characterization of Linear Time-Invariant Systems. Sampling of Signals. Linear Filtering Methods Based on the DFT. The Cepstrum. Summary and References. Problems. 2. Algorithms for Convolution and DFT. Modulo Polynomials. Circular Convolution as Polynomial Multiplication mod un- 1. A Continued Fraction of Polynomials. Chinese Remainder Theorem for Polynomials. Algorithms for Short Circular Convolutions. How We Count Multiplications. Cyclotomic Polynomials. Elementary Number Theory. Convolution Length and Dimension. The DFT as a Circular Convolution. Winograd's DFT Algorithm. Number-Theoretic Analogy of DFT. Number-Theoretic Transform. Split-Radix FFT. Autogen Technique. Summary and References. Problems. 3. Linear Prediction and Optimum Linear Filters. Innovations Representation of a Stationary Random Process. Forward and Backward Linear Prediction. Solution of the Normal Equations. Properties of the Linear Prediction-Error Filters. AR Lattice and ARMA Lattice-Ladder Filters. Wiener Filters for Filtering and Prediction. Summary and References. Problems. 4. Least-Squares Methods for System Modeling and Filter Design. System Modeling and Identification. Lease-Squares Filter Design for Prediction and Deconvolution. Solution of Least-Squares Estimation Problems. Summary and References. Problems. 5. Adaptive Filters. Applications of Adaptive Filters. Adaptive Direct-Form FIR Filters. Adaptive Lattice-Ladder Filters. Summary and References. Problems. 6. Recursive Least-Squares Algorithms for Array Signal Processing. QR Decomposition for Least-Squares Estimation. Gram-Schmidt Orthogonalization for Least-Squares Estimation. Givens Algorithm for Time-Recursive Least-Squares Estimation. Recursive Least-Squares Estimation Based on the Householder Transformation. Order-Recursive Least-Squares Estimation Algorithms. Summary and References. Problems. 7. QRD-Based Fast Adaptive Filter Algorithms. Background. QRD Lattice. Multichannel Lattice. Fast QR Algorithm. Multichannel Fast QR Algorithm. Summary and References. Problems. 8. Power Spectrum Estimation. Estimation of Spectra from Finite-Duration Observations of Signals. Nonparametric Methods for Power Spectrum Estimation. Parametric Methods for Power Spectrum Estimation. Minimum-Variance Spectral Estimation. Eigenanalysis Algorithms for Spectrum Estimation. Summary and References. Problems. 9. Signal Analysis with Higher-Order Spectra. Use of Higher-Order Spectra in Signal Processing. Definition and Properties of Higher-Order Spectra. Conventional Estimators for Higher-Order Spectra. Parametric Methods for Higher-Order Spectrum Estimation. Cepstra of Higher-Order Spectra. Phase and Magnitude Retrieval from the Bispectrum. Summary and References. Problems. References. Index.

Algorithms for Statistical Signal Processing

The ability to discriminate between ballistic missile warheads and confusing objects is an important topic from different points of view. In particular, the high cost of the interceptors with respect to tactical missiles may lead to an ammunition problem. Moreover, since the time interval in which the defense system can intercept the missile is very short with respect to target velocities, it is fundamental to minimize the number of shoots per kill. For this reason, a reliable technique to classify warheads and confusing objects is required. In the efficient warhead classification system presented in this paper, a model and a robust framework is developed, which incorporates different micro-Doppler-based classification techniques. The reliability of the proposed framework is tested on both simulated and real data.

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On Model, Algorithms, and Experiment for Micro-Doppler-Based Recognition of Ballistic Targets

A polynomial eigenvalue decomposition of paraher-mitian matrices can be calculated approximately using iterative approaches such as the sequential matrix diagonalisation (SMD) algorithm. In this paper, we present an improved SMD algorithm which, compared to existing SMD approaches, eliminates more off-diagonal energy per step. This leads to faster convergence while incurring only a marginal increase in complexity. We motivate the approach, prove its convergence, and demonstrate some results that underline the algorithm's performance.

/pdf/multiple-shift-maximum-element-sequential-matrix-1qdu0q1xuq.pdf

Multiple shift maximum element sequential matrix diagonalisation for parahermitian matrices

This paper presents initial progress on formulating minimum variance distortionless response (MVDR) broadband beam-forming using a generalised sidelobe canceller (GSC) in the context of polynomial matrix techniques. The quiescent vector is defined as a broadband steering vector, and we propose a blocking matrix design obtained by paraunitary matrix completion. The polynomial approach decouples the spatial and temporal orders of the filters in the blocking matrix, and decouples the adaptive filter order from the construction of the blocking matrix. For off-broadside constraints the polynomial approach is simple, and more accurate and considerably less costly than a standard time domain broadband GSC.

/pdf/mvdr-broadband-beamforming-using-polynomial-matrix-4mf4dstoog.pdf

MVDR broadband beamforming using polynomial matrix techniques

This paper addresses the extension of the factorization of a Hermitian matrix by an eigenvalue decomposition (EVD) to the case of a parahermitian matrix that is analytic at least on an annulus containing the unit circle. Such parahermitian matrices contain polynomials or rational functions in the complex variable $z$ and arise, e.g., as cross spectral density matrices in broadband array problems. Specifically, conditions for the existence and uniqueness of eigenvalues and eigenvectors of a parahermitian matrix EVD are given, such that these can be represented by a power or Laurent series that is absolutely convergent, at least on the unit circle, permitting a direct realization in the time domain. Based on an analysis of the unit circle, we prove that eigenvalues exist as unique and convergent but likely infinite-length Laurent series. The eigenvectors can have an arbitrary phase response and are shown to exist as convergent Laurent series if eigenvalues are selected as analytic functions on the unit circle, and if the phase response is selected such that the eigenvectors are Holder continuous with $\alpha >\frac{1}{2}$ on the unit circle. In the case of a discontinuous phase response or if spectral majorisation is enforced for intersecting eigenvalues, an absolutely convergent Laurent series solution for the eigenvectors of a parahermitian EVD does not exist. We provide some examples, comment on the approximation of a parahermitian matrix EVD by Laurent polynomial factors, and compare our findings to the solutions provided by polynomial matrix EVD algorithms.

/pdf/on-the-existence-and-uniqueness-of-the-eigenvalue-2b6m8fahr7.pdf

Ian K. Proudler

Papers

Algorithms for Statistical Signal Processing

On Model, Algorithms, and Experiment for Micro-Doppler-Based Recognition of Ballistic Targets

Multiple shift maximum element sequential matrix diagonalisation for parahermitian matrices

MVDR broadband beamforming using polynomial matrix techniques

On the Existence and Uniqueness of the Eigenvalue Decomposition of a Parahermitian Matrix