Multiple-input Multiple-output Waveform Research Articles

Spatial–Temporal Joint Design and Optimization of Phase-Coded Waveform for MIMO Radar

By simultaneously transmitting multiple different waveform signals, a multiple-input multiple-output (MIMO) radar possesses higher degrees of freedom and potential in many aspects compared to a traditional phased-array radar. The spatial–temporal characteristics of waveforms are the key to determining their performance. In this paper, a transmitting waveform design method based on spatial–temporal joint (STJ) optimization for a MIMO radar is proposed, where waveforms are designed not only for beam-pattern matching (BPM) but also for minimizing the autocorrelation sidelobes (ACSLs) of the spatial synthesis signals (SSSs) in the directions of interest. Firstly, the STJ model is established, where the two-step strategy and least squares method are utilized for BPM, and the L2p-Norm of the ACSL is constructed as the criterion for temporal characteristics optimization. Secondly, by transforming it into an unconstrained optimization problem about the waveform phase and using the gradient descent (GD) algorithm, the hard, non-convex, high-dimensional, nonlinear optimization problem is solved efficiently. Finally, the method’s effectiveness is verified through numerical simulation. The results show that our method is suitable for both orthogonal and partial-correlation MIMO waveform designs and efficiently achieves better spatial–temporal characteristic performances simultaneously in comparison with existing methods.

Linear Frequency Modulation and Orthogonal Code Modulation for Co-Located Multiple-Input Multiple-Output High-Frequency Surface Wave Radar

A high-frequency surface wave radar (HFSWR) is the only sensor that provides inexpensive surveillance for up to 200 nautical miles (NM) of the exclusive economic zone in the 3–5 MHz band. However, because of its long wavelength, its angular resolution is low. Multiple-input multiple-output (MIMO) technology is an attractive method to improve angular resolution. This paper proposes MIMO waveforms and their processing that can be used in HFSWR systems. This dual modulation method applies linear frequency modulation to each pulse and orthogonal polyphase codes for a few consecutive pulses to enable MIMO processing. The proposed method can effectively remove the correlation of mutual interference and exhibits excellent performance in removing multiple-time-around echoes.

MIMO waveforms design with good correlation properties under bound constraints

We develop a new fast and efficient algorithm for designing multiple-input multiple-output (MIMO) waveforms with low auto- and cross-correlation sidelobes. Unlike the existing methods that consider unimodular signals, we design both signal amplitudes and phases to minimize the weighted integrated sidelobe level (WISL) of signals. Bound constraints on signal amplitudes are forced into the optimization to balance the transmitter efficiency and the degrees of optimization freedom, which also can provide a feasible set that facilitates efficient optimization. The amplitude-bounded signals design problem is formulated as a quartic optimization problem with bound constraints and equality (constant energy) constraints. We propose a novel algorithm based on the limited-memory Broyden-Fletcher-Goldfarb-Shanno with bound constraints (L-BFGS-B) method to solve the resultant problem. An algorithmic enhancement via nonlinear conjugate gradient (NCG) method is also developed to improve the proposed method. In addition, we derive the gradients of the cost function with respect to (w.r.t.) the signal amplitudes and phases and calculate them with the help of fast Fourier transformation (FFT), which makes the proposed method very computationally efficient. Numerical simulations illustrate that the proposed algorithm outperforms the state-of-the-art methods in both optimization performance and computational efficiency.

Nonorthogonal waveform assisted DOA estimation for joint MIMO sensing and communication

Conventional direction-of-arrival (DOA) estimation methods for multiple-input multiple-output (MIMO) joint sensing and communication system normally pursue high estimation accuracy and resolution by imposing orthogonal waveforms. However, such operation results in a deterioration of communication performance. In this paper, we propose a nonorthogonal waveform assisted DOA estimation algorithm, where an augmented virtual array is derived by exploiting the nonorthogonal MIMO communication waveforms, while a high communication rate can still be maintained. To estimate the round-trip sensing channels of each subcarrier, we utilize the transmitted symbols as pilot symbols, and obtain all the channel coefficients with a minimum mean square error solver. A virtual channel matrix can be formulated with these channel coefficients, which can be regarded as the samples of an augmented virtual array. Based on that, the subspace processing can be conducted for DOA estimation with fully nonorthogonal waveforms. Furthermore, the rank deficiency property of the equivalent signal matrix of the virtual array is analyzed when the distance of targets are identical. To address the problem, a Toeplitz reconstruction method is proposed to restore the rank of the rank-deficient equivalent signal matrix for DOA estimation. Simulations show that the proposed nonorthogonal waveform assisted DOA estimation algorithm outperforms the conventional methods in terms of resolution and accuracy, while maintaining a satisfactory computational efficiency.

MIMO Waveform Design for Dual Functions of Radar and Communication With Space-Time Coding

Sharing a multiple-input multiple-output (MIMO) radar, the single platform can achieve dual functions of radar and communication (DFRC) within the same frequency spectrum, via the same transmit waveforms. In this paper, a space-time coding scheme is developed for transmit beamforming of DFRC and embedding communication information, without their cross-interference. For transmit beampattern design of DFRC, the shape approximation and integrated power approximation criteria are adopted respectively for waveforms optimization with the constant-envelope constraints of transmit waveforms and the equivalent signal in the communication direction. Based on the space-time coding scheme, the direct constellation mapping (DCM) and phase-rotation constellation mapping (PRCM) methods are proposed to embed information symbols. It turns out that the proposed space-time coding scheme for information constellation mapping can prevent missing information symbols and have better performance in bit-error rate (BER), compared to the existing information-embedding techniques. Moreover, the scheme can reduce the dependence of the communication data rate on radar pulse repetition frequency (PRF). Simulation results are presented to demonstrate the effectiveness of the proposed methods.

Adaptive chaotic MIMO radar based on DPMM clustering and Kalman filtering technique.

A novel scheme to optimize the adaptive transmit waveform of chaotic multiple-input multiple-output (MIMO) radar is developed. The main objective of this work is to achieve high ability in target discrimination by using a Dirichlet process mixture model (DPMM)-based clustering method based on nonparametric Bayesian theory and to improve the capability of target detection by minimizing the mean square error of radar channel response via Kalman filtering (KF) technique. The two stages are the discrimination of multiple range-extended targets and the optimization of the adaptive chaos-based waveform for transmission. The adaptive chaotic MIMO waveform optimization scheme overcomes the problem of target discrimination and detection in an intelligent transportation system, where there is a need for extracting the feature of target information achieved from vehicle-mounted sensor. As the number of iterations increases, simulation experiments demonstrate better target discrimination capability provided by the proposed DPMM-KF technique as compared with the traditional waveform design method. In addition, the proposed DPMM-KF technique leads to improved target detection probability and receiver operating characteristics in the interference environment.

Doppler sensitivity of MIMO radar waveforms

Doppler sensitivity of multiple-input multiple-output (MIMO) radar waveforms is complicated but rarely is of concern in most existing MIMO waveform designs. For both distributed and colocated MIMO radar, this paper studies the impact of Doppler sensitivity on both autocorrelation and mutual correlation sidelobes of both constant-modulus MIMO transmit waveforms and angular waveforms in theory and via numerical results, finding that Doppler sensitivity deserves consideration in future MIMO radar waveform designs.

MIMO Radar Beampattern Design Via PSL/ISL Optimization

We deal with the robust design of multiple-input multiple-output (MIMO) waveform covariance matrices that optimize the worst case (over steering mismatches) transmit beampattern assuming either the peak sidelobe level (PSL) or the integrated sidelobe level (ISL) as figure of merit. To this end, we model the uncertainty set associated with each steering vector through two double-sided, potentially non-convex, quadratic constraints. In addition, we force two suitable constraints on the optimization variable. The former accounts for the width of the mainbeam, whereas the latter is either a uniform or a relaxed elemental power requirement allowing to control the amount of transmitted power. We prove that both the mentioned waveform covariance designs lead to non-convex optimization problems which remarkably share some hidden convexity properties. Hence, we devise polynomial time procedures aimed at synthesizing the desired optimal MIMO waveform covariance matrices. Finally, at the analysis stage, we assess the performance of the proposed techniques showing their capability to ensuring improved worst case performance than some counterparts available in open literature.

Wideband MIMO Radar Waveform Design for Multiple Target Imaging

This paper focuses on the design of wideband multiple-input multiple-output (MIMO) radar waveform and its application to multi-target imaging. In a multiple targets scenario, the imaging performances are affected by the transmit beampattern and the power spectral density (PSD) illuminating on targets. Taking into account these factors, we have established an unconstrained optimization model based on jointly minimizing the matching errors of the beampattern and PSDs. To solve the large-scale optimization problem, a conjugate gradient-based wideband MIMO waveform design method is proposed. For the purpose of improving the computational efficiency, the Chirp-Z transform is employed in evaluating the discrete frequency spectrum. Numerical results show that the proposed method can approximately realize the desired multibeam and PSDs. By utilizing a sparse representation method, the designed waveform with randomly distributed PSDs can be applied to multi-target imaging.

On singularity attacks in MIMO channels

AbstractAll multiple‐input multiple‐output (MIMO) systems need a method to estimate and equalise their channel, whether through channel reciprocity or sounding, and most modern orthogonal frequency division multiplexing (OFDM)‐based MIMO waveforms use sounding via OFDM pilot tones. Previous research has focused on jamming data transmissions. We instead focus on jamming channel sounding symbols and introduce the MIMO singularity attack, which attempts to reduce the rank of the channel gain matrix estimate by the receiver through transmission of specific jamming signals. More specifically, we introduce and analyse the MIMO singularity attack, in which a multi‐antenna jammer tries to manipulate pilot tones to skew the channel state information obtained at the receiver. We prove singularity jamming can be more destructive than data jamming attacks such as barrage or pilot jamming by studying its effects on channel bit error rate and capacity. We develop the constraints associated with jamming MIMO sounding channels and further describe how these attacks specifically impact data symbol estimates for OFDM pilot‐based sounding systems. Through simulation, we demonstrate efficiency gains over barrage jamming. Copyright © 2013 John Wiley & Sons, Ltd.

MIMO Radar Waveforms and Cancellation Ratio

The performance of adaptive multiple-input multiple-output (MIMO) radar waveforms is examined. We begin by defining a metric, called cancellation ratio, for numerically screening MIMO waveforms so as to assess their clutter cancellation performance. Next, we evaluate the MISO cancellation ratio levels provided by a number of MIMO waveform classes. Finally, we describe a new MIMO waveform approach, called dithered DDMA (Doppler division multiple access), which enables high performance clutter cancellation over a large range-Doppler region without introducing ambiguous ranges or blind speeds, and without increasing the computational load on the MIMO processor.

Non-adaptive multiple-input, multiple-output radar techniques for reducing clutter

Multiple-input, multiple-output (MIMO) radars enhance performance by transmitting and receiving coded waveforms from multiple locations. This paper describes MIMO techniques that can be used to improve radar performance, especially in airborne Ground Moving Target Indicator (GMTI) applications. The authors begin by showing how MIMO techniques can lower airborne radar clutter to noise ratios (CNRs). This results in smaller losses when observing stationary or low-velocity targets. Next, the authors consider the implementation of MIMO radar modes using electronically scanned arrays (ESAs). Specifically, the authors show how MIMO techniques, applied to subarray-based ESAs, can cause high grating lobes and/or reduced search rates. To address this problem, the authors describe new space–time waveform coding techniques that can be used to improve performance. Two space–time waveform encoding approaches are proposed: (i) an overlapped virtual transmit subarray approach, and (ii) a beamspace MIMO approach. A third approach, involving conventional MIMO waveforms and irregular subarrays, is also briefly considered.