Transmit Waveform Design Research Articles

Transmit Waveform Design for Integrated Wideband MIMO Radar and Bi-Directional Communications

Transmit Waveform Design for Integrated Wideband MIMO Radar and Bi-Directional Communications

Optimization algorithms for transmit waveform design in radar-centric dual-function radar-communication systems

We study the transmit waveform optimization problem in multiple-input-multiple-output (MIMO) dual-function radar-communication (DFRC) systems. A penalty-based formulation is adopted where engineering concerns on radar and communication are integrated in a weighted manner. We transform the original problem into an unconstrained formulation and propose a novel waveform synthesis algorithm via combination of multiple algorithm updates. The combination operation experimentally reaches a better objective value than without. We develop two combination strategies and invent a shrinkage-expansion line search method for monotonicity maintenance. Numerical experiments show that the proposed algorithm suffices to generate constant modulus waveforms which achieve radar and communication purposes.

Integrated Transmit Waveform and RIS Phase Shift Design for LPI Detection and Communication

Integrated Transmit Waveform and RIS Phase Shift Design for LPI Detection and Communication

Joint Transmit Waveform and Receive Filter Design for the DFRC System With Imperfect CSI

Joint Transmit Waveform and Receive Filter Design for the DFRC System With Imperfect CSI

Joint Design of Transmit Waveforms and Receive Filters for MIMO-STAP Radar with Two Sequential Constraints

Joint Design of Transmit Waveforms and Receive Filters for MIMO-STAP Radar with Two Sequential Constraints

Transmit Waveform Design Based on Ambiguity Function Shaping for Distributed Coherent Aperture Radar in MIMO Mode

Transmit Waveform Design Based on Ambiguity Function Shaping for Distributed Coherent Aperture Radar in MIMO Mode

MIMO Radar Transmit Waveform Design for Beampattern Matching via Complex Circle Optimization

In this paper, we study the multiple-input multiple-output (MIMO) radar transmit waveform design method for beampattern matching. The purpose is to design the beampattern to approximate the actual one and minimize the cross-correlation sidelobes under the constant modulus constraint (CMC). Due to the CMC, the problem is non-convex, and the existing methods solve it with relaxation, resulting in performance degradation. Different from these methods, we notice that the CMC is the product of complex circles (CC). Based on this physical characteristic, the direct beampattern matching without relaxation (DBMWR) method is proposed. To be specific, we first formulate the original problem as an unconstrained quartic problem over the CC and then solve it by the proposed method without relaxation. Simulation results show that the proposed method can achieve a balance in terms of accuracy and computation complexity compared with other methods.

False Targets Suppression of Integral Power Frequency Modulated Waveform for Countering Interrupted Sampling Repeater Jamming

The false targets generated by interrupted sampling repeater jamming (ISRJ) bring serious threats to radar target detection and imaging. Transmitted waveform design with joint processing has become a promising way to deal with them. In this letter, the ambiguity function (AF) of integral power frequency modulated (PFM) waveform is derived. Meanwhile, the time-frequency characteristic of ISRJ signal received by the radar is analyzed on the range-Doppler plane. In consideration of the range-Doppler coupling characteristic, an improved Doppler filtering processing method is proposed to suppress the jamming false targets. Simulation results demonstrate that the selected PFM waveform with proposed joint processing method can effectively suppress the false targets resulting from ISRJ.

Enhanced Automotive Sensing Assisted by Joint Communication and Cognitive Sparse MIMO Radar

Automotive radar is mandated to provide high accuracy direction of arrival (DOA) estimation for safe driving, while remaining a low cost device for feasible mass production. Sparse multiple input multiple output (MIMO) arrays emerge as a primary candidate to meet these requirements. As DOA estimation accuracy is a main indicator of tracking performance, Cramer Rao Bound (CRB) is chosen as the goodness measurement for sparse MIMO array optimization, but its application requires prior information of the road environment. We propose a cognitive sparse MIMO array automotive radar, which “perceives” the road environment via automotive sensing supplemented by co-existed communication from roadside unit to vehicle (R2V). This information is used for co-designing a sparse MIMO array for enhanced automotive sensing and vehicle to roadside unit (V2R) communication. Note that both static RSU and dynamic RSU are usually deployed in Internet of Vehicles (IoV), which can provide continuous transmission coverage and permanent connectivity. The bi-directional communications are integrated into the automotive radar. This is achieved by joint transmit waveform design with spectral nulls for communications and with shared sparse MIMO array co-design for both sensing and high quality V2R communications. Simulation results validate the enhanced automotive sensing performance assisted by the integrated bi-directional communications in the cycle of cognitive-driven optimization.

Waveform Design for Wireless Power Transfer With Power Amplifier and Energy Harvester Non-Linearities

Waveform optimization has shown its great potential to boost the performance of far-field wireless power transfer (WPT). Current research has optimized transmit waveform, adaptive to channel state information, to maximize the harvested power in WPT while accounting for the energy harvester (EH)'s non-linearity. However, the existing transmit waveform design disregards the non-linear high power amplifiers (HPA) at the transmitter. Driven by this, this paper optimizes a multi-carrier waveform at the input of HPA to maximize the harvested DC power considering both HPA's and EH's non-linearities. Two optimization models are formulated based on whether the frequencies of the input waveform are concentrated within the transmit pass band or not. Analysis and simulations show that, while EH's non-linearity boosts the power harvesting performance, HPA's non-linearity degrades the harvested power. Hence, the optimal waveform shifts from multi-carrier that exploits EH's non-linearity to single-carrier that reduces HPA's detrimental non-linear distortion as the operational regime of WPT becomes more sensitive to HPA's non-linearity and less sensitive to EH's non-linearity (and inversely). Simultaneously, operating towards HPA's non-linear regime by increasing the input signal power benefits the harvested power since HPA's DC power supply is better exploited, whereas the end-to-end power transfer efficiency might decrease because of HPA's increasing non-linear degradation. Throughout the simulations, the proposed waveforms show significant gain over those not accounting for HPA's non-linearity, especially in frequency-flat channels. We also compare the two proposed waveforms and show that the severity of HPA's non-linearity dictates which of the two proposed waveforms is more beneficial.

SINR- and MI-Based Double-Robust Waveform Design.

Owing to cognitive radar breaking the open-loop receiving-transmitting mode of traditional radar, adaptive waveform design for cognitive radar has become a central issue in radar system research. In this paper, the method of radar transmitted waveform design in the presence of clutter is studied. Since exact characterizations of the target and clutter spectra are uncommon in practice, a single-robust transmitted waveform design method is introduced to solve the problem of the imprecise target spectrum or the imprecise clutter spectrum. Furthermore, considering that radar cannot simultaneously obtain precise target and clutter spectra, a novel double-robust transmitted waveform design method is proposed. In this method, the signal-to-interference-plus-noise ratio and mutual information are used as the objective functions, and the optimization models for the double-robust waveform are established under the transmitted energy constraint. The Lagrange multiplier method was used to solve the optimal double-robust transmitted waveform. The simulation results show that the double-robust transmitted waveform can maximize SINR and MI in the worst case; the performance of SINR and MI will degrade if other transmitted waveforms are employed in the radar system.

Integrated Sensing and Communication: Enabling Techniques, Applications, Tools and Data Sets, Standardization, and Future Directions.

The design of integrated sensing and communication (ISAC) systems has drawn recent attention for its capacity to solve a number of challenges. Indeed, ISAC can enable numerous benefits, such as the sharing of spectrum resources, hardware, and software, and improving the interoperability of sensing and communication. In this article, we seek to provide a thorough investigation of ISAC. We begin by reviewing the paradigms of sensing-centric design, communication-centric design, and co-design of sensing and communication. We then explore the enabling techniques that are viable for ISAC (i.e., transmit waveform design, environment modeling, sensing source, signal processing, and data processing). We also present some emergent smart-world applications that could benefit from ISAC. Furthermore, we describe some prominent tools used to collect sensing data and publicly available sensing data sets for research and development, as well as some standardization efforts. Finally, we highlight some challenges and new areas of research in ISAC, providing a helpful reference for ISAC researchers and practitioners, as well as the broader research and industry communities.

Waveform optimization with SINR criteria for FDA radar in the presence of signal-dependent mainlobe interference

In this paper, we focus on the design of the transmit waveforms of a frequency diverse array (FDA) in order to improve the output signal-to-interference-plus-noise ratio (SINR) in the presence of signal-dependent mainlobe interference. Since the classical multi-carrier matched filtering-based FDA receiver cannot effectively utilize the waveform diversity of FDA, a novel FDA receiver framework based on multi-channel mixing and low pass filtering is developed to keep the separation of the transmit waveform at the receiver side, while preserving the FDA range-controllable degrees of freedom. Furthermore, a range-angle minimum variance distortionless response beamforming technique is introduced to synthesize receiver filter weights with the ability to suppress a possible signal-dependent mainlobe interference. The resulting FDA transmit waveform design problem is initially formulated as an optimization problem consisting of a non-convex objective function and multiple non-convex constraints. To efficiently solve this, we introduce two algorithms, one based on a signal relaxation technique, and the other based on the majorization minimization technique. The preferable performance of the proposed multi-channel low pass filtering receiver and the optimized transmit waveforms is illustrated using numerical simulations, indicating that the resulting FDA system is not only able to effectively suppress mainlobe interference, but also to yield estimates with a higher SINR than the FDA system without waveform optimization.

Beamforming and Waveform Designing for Spectrum Coexistence System Based on Constructive Interference

In this letter, a joint communication beamforming and radar transmit waveform design method is proposed for radar and communication coexistence (RCC) systems. The optimization problem is designed to maximize the radar signal-to-interference-plus-noise ratio (SINR), under the constraints of communication constructive interference (CI), radar waveform similarity and constant modulus. A novel block fading (BF) algorithm is developed for tackling the non-convexity of the problem constructed, where the closed-form sub-optimal solutions are obtained by transforming the original problem into an element-by-element phase angle problem. Finally, the numerical simulation results verify the superiority of the proposed algorithm on the performance of radar detection and multi-user interference (MUI) elimination.

Adaptive transmit waveform design for active cognitive sonar using multi-tone sinusoidal frequency modulation

Cognitive sonar systems leverage information gathered from earlier sensing interactions with the acoustic environment to adapt its system parameters for optimal performance. Of the many system parameters, an active cognitive sonar system could adapt, the acoustic signal projected into the medium, also known as the transmit waveform, has a profound impact on system performance as many of the physical characteristics of the acoustic environment are contained in the return echo signal. This research examines utilizing multi-tone sinusoidal frequency modulated (MTSFM) waveforms as an adaptive transmit waveform model for use in active cognitive sonar systems. The MTSFM waveform’s frequency and phase modulation functions are composed of a finite set of weighted sinusoidal harmonics. The weights for each harmonic are utilized as a discrete set of design coefficients. Adjusting these coefficients results in constant amplitude, spectrally compact FM waveforms that possess a wide variety of performance characteristics, which in the past required a diverse set of waveform types to achieve. The adaptability of the MTSFM waveform model enables a cognitive sonar system to generate waveforms that are finely tuned for the novel scenarios and environments that it may encounter.

Direct transmit waveform design to match a desired beampattern under the constant modulus constraint

In this paper, we study waveform optimization methods for transmit beampattern matching with a multiple-input-multiple-output (MIMO) radar. The aim is to minimize the squared error between the desired beampattern and the actual one. We propose two algorithms, which are based on cyclic optimization and majorization-minimization (MM), to tackle the highly nonlinear and non-convex optimization problem. The proposed algorithms have simple closed-form solutions at every iteration, which reduce the computational complexity significantly. Moreover, the convergence of the proposed methods can be guaranteed, due to the descent property of MM algorithms and cyclic optimization. In addition, we extend the proposed algorithm to design waveforms for dual-function radar-communication systems. Finally, simulation results demonstrate the superiority of the proposed algorithms compared with state-of-the art methods.

Min-Max Optimization for MIMO Radar Waveform Design With Improved Power Efficiency

Transmit waveform design with power efficiency constraint is a prominent problem for colocated multiple-input-multiple-output (MIMO) radar. Though there are extensive relevant works in the literature, the existing peak-to-average-power ratio (PAR) constraint is insufficient in controlling the transmit power uniformity and the conventional constant modulus (CM) is imposed on both waveform temporal and spatial dimensions, resulting in an unnecessary loss of degrees of freedom. Thereby, we introduce an individual antenna power control (IAPC) scheme with a new peak-to-valley-power-ratio (PVR) constraint to flexibly control the transmit power uniformity along the spatial dimension. We then devise two min-max waveform designs for shaped transmit beampattern synthesis. To tackle the resultant non-convex problems, we propose a surrogate primal alternating direction method of multipliers (SP-ADMM) to approximately solve a surrogate primal subproblem for PVR projection since the standard ADMM is unable to tackle the intricate primal problems directly. We theoretically prove the convergence behavior of the SP-ADMM. Finally, experiments verify the effectiveness and improved performance of the developed methods against their counterparts, especially in the metric of sidelobe level.

Waveform Optimization with Sinr Criteria for Fda Radar in the Presence of Signal-Dependent Mainlobe Interference

In this paper, we focus on the design of the transmit waveforms of a frequency diverse array (FDA) in order to improve the output signal-to-interference-plus-noise ratio (SINR) in the presence of signal-dependent mainlobe interference. Since the classical multi-carrier matched filtering-based FDA receiver cannot effectively utilize the waveform diversity of FDA, a novel FDA receiver framework based on multi-channel mixing and low-pass filtering is developed to keep the separation of the transmit waveform at the receiver side, while preserving the FDA range-controllable degrees of freedom. Furthermore, a range-angle minimum variance distortionless response beamforming technique is introduced to synthesize receiver filter weights with the ability to suppress a possible signal-dependent mainlobe interference. The resulting FDA transmit waveform design problem is initially formulated as an optimization problem consisting of a non-convex objective function and multiple non-convex constraints. To efficiently slove this, we introduce two algorithms, one based on a signal relaxation technique, and the other based on the majorization minimization technique. The preferable performance of the proposed multi-channel low-pass filtering receiver and the optimized transmit waveforms is illustrated using numerical simulations, indicating that the resulting FDA system is not only able to effectively suppress mainlobe interference, but also to yield estimates with a higher SINR than the FDA system without waveform optimization.

Joint Transmit Waveform and Receive Filter Design for Dual-Function Radar-Communication Systems

In this paper, the problem of joint transmit waveform and receive filter design for dual-function radar-communication (DFRC) systems is studied. The considered system model involves a multiple antenna base station (BS) of a cellular system serving multiple single antenna users on the downlink. Furthermore, the BS simultaneously introduces sensing capabilities in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. A novel framework based on constrained optimization problems is proposed for the joint design of the transmit waveform and the radar receive filter such that different constraints related to the power amplifiers and the radar waveform are satisfied. In contrast to the existing approaches in the DFRC systems’ literature, the proposed approach does not require the knowledge of a predetermined radar beampattern in order to optimize the performance of the radar part through its approximation. Instead, a beampattern is generated by maximizing the radar receive signal-to-interference ratio (SINR) thus, enabling a more flexible design. Moreover, the radar receive filter processing and its optimization is considered for the first time on DFRC systems, enabling the effective exploitation of the available degrees of freedom in the radar receive array. Efficient algorithmic solutions with guaranteed convergence are developed for the defined constrained nonconvex optimization problems. The effectiveness of the proposed solutions is verified via numerical results.

Waveform design with controllable modulus dynamic range under spectral constraints

Transmit waveform design under practical temporal and spectral constraints plays a prominent role in determining the performance of active sensing systems. In temporal domain, it is highly desirable to control the variation of waveform modulus within a small range to improve the transmit power efficiency. To this end, we introduce a new constraint, referred to as modulus dynamic range constraint (MDRC), to directly control the waveform magnitude variation, wherein the constant modulus (CM) is a special case. In spectrum domain, we examine the transmit waveform design with a desirable spectral shape under the MDRC. A flat spectrum that corresponds to low auto-correlation sidelobes (ACS) is considered first and then extended to arbitrary-shaped spectrum synthesis. We formulate the design as a least-square minimization of spectrum matching and a phase compensation technique is adopted to transform the non-smooth problem sequentially. A projected-gradient algorithm (PGA) is proposed to solve the first problem and any limit point of PGA is proved to converge to the KKT point. Two fast-version algorithms with a quadratic convergence rate are then developed for large-scale waveform design. Subsequently, PGA is extended to synthesize arbitrary-shaped spectrum. Finally, numerical experiments demonstrate the superiority of the proposed algorithms over the state-of-the-art methods.