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Multi-input multi-output (MIMO) radar with massive antennas is promising for high resolution applications. However, a big challenge of this system is that the hardware cost and power consumption will increase significantly, if high-resolution quantizers are adopted. In this article, we consider MIMO radar deployed with one-bit digital-to-analog converters, and investigate the problem of designing one-bit transmit sequence with good spatial and spectral properties. Specifically, the one-bit waveform design problem is formulated by minimizing the mean-square error between the desired and designed transmit beampatterns, subject to spectral constraints. The resulting problem, including a nonconvex quartic objective and a nonconvex discrete constraint, is NP-hard, and an alternating optimization (AltOpt) framework with the aid of “almost equivalent” criterion is thereby developed to handle it. Particularly, in the AltOpt framework, a low-complexity algorithm is developed based on the alternating direction method of multipliers approach. Numerical simulations are provided to show the advantages of the proposed method over the state-of-the-art techniques in terms of the spatial and spectral properties as well as computational complexity. [ABSTRACT FROM AUTHOR]

This study discusses the integrated system of full‐duplex (FD) multiple‐input‐multiple‐output (MIMO) communication and MIMO radar, which has been rarely considered in published studies. Such an integrated system is composed of a base station and several uplink (UL) and downlink (DL) users, and is supposed to simultaneously implement three functionalities, that is, target detection, UL and DL communications. The degrees of freedom of system design involve the precoding and combining schemes for UL and DL communications, as well as the receiving filter for radar echo. An optimization problem is developed to maximise a compound communication sum‐rate metric subject to the SINR constraint imposed on target detection performance. The resulted non‐convex and NP‐hard problem is first divided into two sub‐problems. Then, for one of the sub‐problems that is still intractable, its solution is iteratively obtained based on the fractional programing technique and the consensus alternating direction method of multipliers algorithm. In the simulation part, the convergence performance and computational efficiency of the proposed algorithm are illustrated. In addition, the target detection performance, DL and UL communication performance of the integrated system are also evaluated. [ABSTRACT FROM AUTHOR]

Maneuvering extended object tracking is a new research field due to the rapid development of modern sensor technology. Multiple measurements may be resolved from different unknown sources on an object by using a high-resolution radar. In this case, the object should be regarded as an extended one with object extension, e.g., its shape may be described by the star-convex random hypersurface model. This model is usually specified by a one-dimensional radial function. However, the divergence of the shape estimation and a high error of the kinematic state estimation are likely to occur when an extended object maneuvers. This is because the radial function may take a negative value after Fourier series expansion, which leads to unpredictable estimation results. Unfortunately, the model itself is unable to solve this problem via the subsequent iterations. In this paper, we proposed a modified shape estimation approach to track an extended object with a star-convex random hypersurface model based on minimum cosine distance. Both the extension state and kinematic state at the current time are reinitialized once the radial function takes a negative value. Moreover, a mathematical model was constructed by using the principle of minimum cosine distance, so as to obtain more reasonable weight distribution coefficients for the correction of the extension state. Simulation results in different scenarios demonstrated the effectiveness of the proposed tracking approach. [ABSTRACT FROM AUTHOR]

In netted colocated multiple-input multiple-output (MIMO) radar system (NCMRS), constrained to the limited resource, one must decide when to run the NCMRS, which nodes are to be activated, how much energy should be consumed, and which waveform type as well as parameter should be transmitted by each activated node. To this end, in this article, we consider the problem of joint waveform control and space-time resource management for maneuvering targets tracking in NCMRS. First, the joint waveform type as well as parameter selection and space-time resource management optimization model is proposed, where both the network system resource consumption and the overall tracking performance of maneuvering targets are considered and employed as the network comprehensive cost. Then, to solve the established optimization problem efficiently, a modified particle swarm optimization (MPSO)-based joint waveform control and space-time resource optimization (JWCSTRO) algorithm is put forward. In the MPSO-based JWCSTRO algorithm, the system sampling period, the activated nodes, the node-target assignment, and the subarray number, transmit energy and transmit waveform type, as well as parameter of each activated node can be adjusted jointly and adaptively, where the former five implement the space-time resource optimization and the last one achieves the waveform control in NCMRS. Finally, numerical simulation results demonstrate the effectiveness of the proposed MPSO-based JWCSTRO algorithm. Furthermore, compared with the five other benchmark algorithms, the maximum and minimum improvement ratios of the network comprehensive cost of the proposed MPSO-based JWCSTRO algorithm are 44.89% and 21.68%, respectively. [ABSTRACT FROM AUTHOR]

This letter proposes a limited-memory receive filter design to maximize the output signal-to-interference-plus-noise ratio (SINR) of massive multiple-input multiple-output (MIMO) radar. The associated problem is a minimum variance distortionless response (MVDR) one which can be analytical solved, but the computation of the MVDR solution would be prohibitive in the context of massive MIMO radar, due to the huge memory consumption. To this end, we propose a low-complexity algorithm that exploits a reparameterization method to avoid the significant memory consumption, and solves the resulting problem with several simple algebraic operations. We show that the memory consumption of the proposed algorithm depends on the number of interference sources. Therefore, for the scenario with a large number of interference sources, we extend the proposed algorithm by the alternating direction method of multipliers (ADMM), which promotes a further memory reduction. Finally, numerical simulations are conducted to demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Extremely low-resolution (e.g. one-bit) analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) can substantially reduce hardware cost and power consumption for MIMO radar especially with large scale antennas. In this paper, we focus on the detection performance analysis and joint design for the MIMO radar with one-bit ADCs and DACs. Specifically, under the assumption of low signal-to-noise ratio (SNR) and interference-to-noise ratio (INR), we derive the expressions of probability of detection ($\mathcal {P}_{d}$) and probability of false alarm ($\mathcal {P}_{f}$) for one-bit MIMO radar and also the theoretical performance gap to infinite-bit MIMO radars for the noise-only case. We further find that for a fixed $\mathcal {P}_{f}$ , $\mathcal {P}_{d}$ depends on the defined quantized signal-to-interference-plus-noise ratio (QSINR), which is a function of the transmit waveform and receive filter. Thus, an optimization problem arises naturally to maximize the QSINR by joint designing the waveform and filter. For the formulated problem, we propose an alternating waveform and filter design for QSINR maximization (GREET). At each iteration of GREET, the optimal receive filter is updated via the minimum variance distortionless response (MVDR) method, and due to the difficulty in global optimality, an alternating direction method of multipliers (ADMM) based algorithm is devised to efficiently find a high-quality suboptimal one-bit waveform. Numerical simulations are consistent to the theoretical performance analysis and demonstrate the effectiveness of the proposed design algorithm. [ABSTRACT FROM AUTHOR]

Dual-function radar-communication (DFRC) systems with Orthogonal Frequency Division Multiplexing (OFDM) make efficient use of spectrum, while maintaining reliable robustness against multipath fading for communication and improved estimation accuracy for radar. Nevertheless, it would be costly to use a fully digital beamforming architecture, where each antenna is equipped with a radio frequency (RF) chain. This motivates us to consider hybrid beamforming for OFDM-DFRC system in this work. More specifically, the analog beamformer for the whole bandwidth and digital beamformer for each subcarrier are simultaneously designed, by jointly optimizing the spectral efficiency (SE) of communication and spatial spectrum matching error (SSME) of radar. To tackle the resulting optimization problem, a consensus alternating direction method of multipliers (consensus-ADMM) approach is devised. Further, in a practical sense, we consider the issue of hybrid transmit beamforming design with finite-resolution phase shifters, to facilitate economical implementation of the analog beamformer. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

In this paper, the problem of transmit sequence design for a dual-function radar-communication (DFRC) system equipped with one-bit digital-to-analog converters (DACs) is investigated. More specifically, a nonconvex problem is formulated by minimizing the symbol mean-square error, while ensuring the target localization performance for radar. A direct one-bit sequence design tackled by an alternating minimization (AM) approach, which involves a simple unconstrained quadratic sub-problem with closed-form solution and a quadratically constrained nonconvex sub-problem tackled by the alternating direction method of multipliers (ADMM) algorithm, is developed. The solutions of primal variables under the ADMM framework are provided and the convergence is discussed. Additionally, an indirect but computationally more efficient design, which is realized by transmit beamforming based on an accelerated primal gradient (APG) method, is presented. For better understanding of this design, both the Cramér-Rao Bound (CRB) and symbol error probability of the resulting beamformer with one-bit quantization using the Bussgang theorem are analyzed. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

This article considers the waveform design problem for airborne multiple-input-multiple-output (MIMO) radar systems with space-time adaptive processing (STAP). We choose the mutual information between the received signal and the target impulse response as the design metric to achieve enhanced detection performance under the influences from neighboring range cells. In order to solve the resulting optimization problem, we first decompose the objective function into two parts and consider their optimization individually. We optimize the first part by relaxing it using an upper bound and optimize the second part using a simple optimization procedure based on the majorization–minimization (MM) algorithm. The alternation direction method of multipliers (ADMM) framework is then adopted to derive the overall solution, where the MM algorithm is employed again to handle the fourth-order term of the waveform vector. Numerical results are provided to show that the proposed algorithm has better detection performance than that of the existing methods. [ABSTRACT FROM AUTHOR]

This paper considers the problem of co-existence of collocated multiple-input multiple-output (MIMO) radar and downlink multiple-input single-output (MISO) communication systems, which share the same frequency band. As the interference caused by the communication systems would degrade the accuracy of radar target localization, we formulate the co-design of radar waveform and communication transmit weights by minimizing the Cramér-Rao Bound (CRB) of direction-of-arrival (DOA) estimation, subject to a set of constraints accounting for the worst-case signal-to-noise-plus-interference ratio (SINR), similarity and energy. However, the objective is nonconvex and all variables are coupled together in the SINR constraints, the formulated problem is thus NP-hard. Towards that end, a decentralized block successive upper-bound minimization (D-BSUM) method based on the decomposition theory is developed. More specifically, at each iteration of this algorithm, the radar waveform is obtained with the aid of the alternating direction method of multipliers (ADMM) algorithm, and communication weights are obtained by exploiting the semidefinite programs (SDP). It is also proved that the proposed SDP-based method gives a rank-one solution. Numerical simulations are conducted to evaluate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

High-frequency radar is easily affected by radio frequency interference (RFI) since it shares the band with many radio services. To mitigate the RFIs, this study designs receiver for radar's fast-time processing. Two optimisation problems are, respectively, proposed, both involving an objective function accounting for maximising the signal-to-interference plus noise ratio, but are distinguished with different constrains for sidelobes control. Furthermore, the authors show that the two proposed problems both can be reformulated as the second-order cone programming, hence some well-established interior point methods can be introduced to resolve them. Simulation results show that the designed filters can precisely control the sidelobe level from different perspectives, with the RFIs suppressed to achievable greatest extent. [ABSTRACT FROM AUTHOR]

This article designs waveform in fast-time domain to optimise high-frequency radar performances embedded in the co-channel interference and coloured noise. Considering the whitening filter is applied for fast-time processing, a novel objective function accounting for both signal to interference plus noise ratio (SINR) and integrated range sidelobe level (IRSL) is introduced and the constant modulus constraint is forced on the probing waveform. An iterative algorithm based on the alternating direction method of multipliers is presented, resolving such non-convex quartic optimisation problem with a polynomial-time computational complexity. In each iteration, two quadratic problems are presented, with the closed-form solutions given. Finally, the authors evaluate the effectiveness of the designed waveform in terms of the range-Doppler processing result, SINR, IRSL and power spectrum. [ABSTRACT FROM AUTHOR]

In sky-wave over-the-horizon radar (OTHR), it is quite difficult to handle the issues of target location (ground range) and height (altitude) estimation due to their joint effect on group range. This work addresses the joint estimation of target location and height by the means of multipath propagation of OTHR signals and structure of a two-dimensional (2D) array. Usually, the multipath signal results from ionosphere propagation in OTHR and can be produced in multi-input?multi-output (MIMO) radar by transmitting signals of various carrier frequencies. A 2D array provides the potential of elevation resolution, which is related to ground and slant ranges. By the multi-quasi-parabolic (MQP) ionospheric model, we formulate the multipath propagation and signal model for the OTHR with a target at location r and height h. Moreover, the joint maximum-likelihood estimates (MLEs) of r and h are derived, and the joint Fisher information matrix (FIM) is calculated. With the so-obtained FIM, the estimability is analyzed; that is: r and h are estimable if and only if either a multipath signal or 2D array is available. The joint Cramer-Rao bound (CRB) is computed and discussed for accuracy improvement. Additionally, the estimability is also extended to the joint estimation of target location, height, and velocity. [ABSTRACT FROM PUBLISHER]

A reasonable resource management strategy can maximize the sensing performance of radar network system via adaptively controlling the working parameters of interest. In this paper, a collaborative trajectory planning and transmit resource scheduling (CTPTRS) strategy for multiple target tracking (MTT) in distributed radar network system with ground-based transmitters and airborne receivers (GTAR) is proposed. Firstly, the CTPTRS for the distributed radar network with GTAR (DRN-GTAR) is formulated as a non-convex optimization problem. To be more precise, the transmit power with the bandwidth of each transmitter and the trajectory of each airborne front-receiver are controlled jointly and adaptively to maximize the MTT performance. Secondly, to tackle the non-convex optimization problem, an efficient two-stage solution technique integrated with the semi-definite programming (SDP), the Latin hypercube sampling-based stochastic simulation (LHS) and the improved particle swarm optimization-tabu search (IPSOTS) is proposed. Finally, simulation results demonstrate the effectiveness of the proposed CTPTRS strategy. Furthermore, in terms of the MTT performance, the proposed strategy owns superior performance compared with six other existing baseline strategies. • Trajectory planning and resource scheduling for radar network with GTAR. • Transmit power with bandwidth and trajectory can be controlled jointly. • A solution technique integrated with the SDP, LHS and IPSOTS is proposed. [ABSTRACT FROM AUTHOR]