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Register a new userMIMO Radar Waveform-Filter Design for Extended Target Detection from a View of Games
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This paper studies the Two-Person Zero Sum(TPZS) game between a Multiple-Input Multiple-Output(MIMO) radar and an extended target with payoff function being the output Signal-to-Interference-pulse-Noise Ratio(SINR) at the radar receiver. The radar player wants to maximize SINR by adjusting its transmit waveform and receive filter. Conversely, the target player wants to minimize SINR by changing its Target Impulse Response(TIR) from a scaled sphere centered around a certain TIR. The interaction between them forms a Stackelberg game where the radar player acts as a leader. The Stackelberg equilibrium strategy of radar, namely robust or minimax waveform-filter pair, for three different cases are taken into consideration. In the first case, Energy Constraint(EC) on transmit waveform is introduced, where we theoretically prove that the Stackelberg equilibrium is also the Nash equilibrium of the game, and propose Algorithm 1 to solve the optimal waveform-filter pair through convex optimization. Note that the EC cant meet the demands of radar transmitter due to high Peak Average to power Ratio(PAR) of the transmit waveform, thus Constant Modulus and Similarity Constraint(CM-SC) on waveform is considered in the second case, and Algorithm 2 is proposed to solve this problem, where we theoretically prove the existence of Nash equilibrium for its Semi-Definite Programming(SDP) relaxation form. And the optimal waveform-filter pair is solved by calculating the Nash equilibrium followed by the randomization schemes. In the third case,...
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The existence of multipath brings extra looks of targets. This paper considers the extended target detection problem with a narrow band Multiple-Input Multiple-Output(MIMO) radar in the presence of multipath from the view of waveform-filter design. The goal is to maximize the worst-case Signal-to-Interference-pulse-Noise Ratio(SINR) at the receiver against the uncertainties of the target and multipath reflection coefficients. Moreover, a Constant Modulus Constraint(CMC) is imposed on the transmit waveform to meet the actual demands of radar. Two types of uncertainty sets are taken into consideration. One is the spherical uncertainty set. In this case, the max-min waveform-filter design problem belongs to the non-convex concave minimax problems, and the inner minimization problem is converted to a maximization problem based on Lagrange duality with the strong duality property. Then the optimal waveform is optimized with Semi-Definite Relaxation(SDR) and randomization schemes. Therefore, we call the optimization algorithm Duality Maximization Semi-Definite Relaxation(DMSDR). Additionally, we further study the case of annular uncertainty set which belongs to non-convex non-concave minimax problems. In order to address it, the SDR is utilized to approximate the inner minimization problem with a convex problem, then the inner minimization problem is reformulated as a maximization problem based on Lagrange duality. We resort to a sequential optimization procedure alternating between two SDR problems to optimize the covariance matrix of transmit waveform and receive filter, so we call the algorithm Duality Maximization Double Semi-Definite Relaxation(DMDSDR). The convergences of DMDSDR are proved theoretically. Finally, numerical results highlight the effectiveness and competitiveness of the proposed algorithms as well as the optimized waveform-filter pair.
Frequency-hopping (FH) MIMO radar-based dual-function radar communication (FH-MIMO DFRC) enables communication symbol rate to exceed radar pulse repetition frequency, which requires accurate estimations of timing offset and channel parameters. The estimations, however, are challenging due to unknown, fast-changing hopping frequencies and the multiplicative coupling between timing offset and channel parameters. In this paper, we develop accurate methods for a single-antenna communication receiver to estimate timing offset and channel for FH-MIMO DFRC. First, we design a novel FH-MIMO radar waveform, which enables a communication receiver to estimate the hopping frequency sequence (HFS) used by radar, instead of acquiring it from radar. Importantly, the novel waveform incurs no degradation to radar ranging performance. Then, via capturing distinct HFS features, we develop two estimators for timing offset and derive mean squared error lower bound of each estimator. Using the bounds, we design an HFS that renders both estimators applicable. Furthermore, we develop an accurate channel estimation method, reusing the single hop for timing offset estimation. Validated by simulations, the accurate channel estimates attained by the proposed methods enable the communication performance of DFRC to approach that achieved based on perfect timing and ideal knowledge of channel.
Dual-functional radar-communication (DFRC) systems can simultaneously perform both radar and communication functionalities using the same hardware platform and spectrum resource. In this paper, we consider multi-input multi-output (MIMO) DFRC systems and focus on transmit beamforming designs to provide both radar sensing and multi-user communications. Unlike conventional block-level precoding techniques, we propose to use the recently emerged symbol-level precoding approach in DFRC systems, which provides additional degrees of freedom (DoFs) that guarantee preferable instantaneous transmit beampatterns for radar sensing and achieve better communication performance. In particular, the squared error between the designed and desired beampatterns is minimized subject to the quality-of-service (QoS) requirements of the communication users and the constant-modulus power constraint. Two efficient algorithms are developed to solve this non-convex problem on both the Euclidean and Riemannian spaces. The first algorithm employs penalty dual decomposition (PDD), majorization-minimization (MM), and block coordinate descent (BCD) methods to convert the original optimization problem into two solvable sub-problems, and iteratively solves them using efficient algorithms. The second algorithm provides a much faster solution at the price of a slight performance loss, first transforming the original problem into Riemannian space, and then utilizing the augmented Lagrangian method (ALM) to obtain an unconstrained problem that is subsequently solved via a Riemannian Broyden-Fletcher-Goldfarb-Shanno (RBFGS) algorithm. Extensive simulations verify the distinct advantages of the proposed symbol-level precoding designs in both radar sensing and multi-user communications.
MIMO transmit arrays allow for flexible design of the transmit beampattern. However, the large number of elements required to achieve certain performance using uniform linear arrays (ULA) maybe be too costly. This motivated the need for thinned arrays by appropriately selecting a small number of elements so that the full array beampattern is preserved. In this paper, we propose Learn-to-Select (L2S), a novel machine learning model for selecting antennas from a dense ULA employing a combination of multiple Softmax layers constrained by an orthogonalization criterion. The proposed approach can be efficiently scaled for larger problems as it avoids the combinatorial explosion of the selection problem. It also offers a flexible array design framework as the selection problem can be easily formulated for any metric.
The problem of data-driven joint design of transmitted waveform and detector in a radar system is addressed in this paper. We propose two novel learning-based approaches to waveform and detector design based on end-to-end training of the radar system. The first approach consists of alternating supervised training of the detector for a fixed waveform and reinforcement learning of the transmitter for a fixed detector. In the second approach, the transmitter and detector are trained simultaneously. Various operational waveform constraints, such as peak-to-average-power ratio (PAR) and spectral compatibility, are incorporated into the design. Unlike traditional radar design methods that rely on rigid mathematical models with limited applicability, it is shown that radar learning can be robustified by training the detector with synthetic data generated from multiple statistical models of the environment. Theoretical considerations and results show that the proposed methods are capable of adapting the transmitted waveform to environmental conditions while satisfying design constraints.
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