Signal-dependent Interference Research Articles

Joint beampattern and symbol-level waveform design for integrated sensing and communication

Tackling interference is an essential issue for the wireless system, which benefits the quality of service (QoS). In this paper, we consider an integrated sensing and communication (ISAC) system and focus on suppressing the signal-dependent interferences (SDIs) toward the radar and exploiting the constructive part of multi-user interference (MUIs) in communication. A novel optimization problem to minimize the transmitting power considering the channel fluctuations is formulated. We propose a tailored semidefinite relax (SDR) and successive convex approximation (SCA) based algorithm to simultaneously design the beampattern and symbol-level waveform. Simulations show that the proposed algorithm outperforms the conventional robust design method and a heuristic algorithm based on waveform scaling, reducing power consumption.

Joint Transceiver Beamforming for Multi-Target Single-User Joint Radar and Communication

This letter considers the joint design of transmit and receive beamforming (TRBF) vectors for multi-target single-user joint radar and communication (JRC) systems in the presence of signal-dependent interference. An iterative framework is devised to maximize the signal-to-interference-plus-noise ratio (SINR) of a target while guaranteeing the SINRs of the user and the rest of the targets. At each iteration, the problem of designing transmit beamformer is non-convex and can be solved by semidefinite programming (SDP) optimization. Moreover, an orthogonalization-based transmit beamforming method is proposed to efficiently obtain a suboptimal analytical solution. Numerical results verify the effectiveness of the proposed framework.

Robust adaptive orthogonal frequency division multiplexing integrated radar and communications waveform design under clutter circumstances

To improve the target recognition and classification performance of integrated radar and communications system in signal-dependent interference, we propose an innovative robust adaptive orthogonal frequency division multiplexing integrated radar and communications waveform design taking into account the clutter. We set the uncertainty class for clutter and other prior information to solve the problem of system performance degradation caused by inaccurate information in actual environments. Then, we formulate the optimization function to maximize the normalized weighted sum of mutual information and data information rate subject to the constraint of the total transmitted energy. To solve the optimization problem, we propose a maximum marginal allocation algorithm that transforms the original problem into a multi-stage energy distribution problem, and performs optimal energy distribution in each stage according to the optimization criteria. Moreover, by considering a more practical scenario of multiple downlink communication users, a joint scheme for channel allocation and waveform design is presented to ensure the fairness of multiple communication users. Simulation results show the effectiveness and feasibility of the designed waveform and channel allocation method.

Joint Transmit and Receive Beamforming Design for Integrated Sensing and Communication

We consider the problem of the joint design of transmit and receive beamforming (TRBF) vectors for integrated sensing and communication systems in the signal-dependent interference scenario. The dual-functional base station can communicate with downlink users and detect targets simultaneously. By adopting the signal-to-interference-plus-noise ratio (SINR) as performance metrics for both radar and communication, we propose the joint design of the TRBF strategy to manifest the balance of radar and communication in both perfect and imperfect channel state information scenarios. Meanwhile, the upper bound performance of the SINR for target detection is analyzed. Numerical results show the SINR trade-offs between target detection and user communication.

Optimal Target Classification Using Frequency-Based Radar Waveform Design

In this article, we introduce a frequency-based waveform design to maximize binary target classification. The optimization problems can be solved via optimal solvers available in the literature. The presented formulation is applicable to the classification scenarios where the extended target frequency responses (TFRs) are complex, random, and normally distributed with unequal mean vectors. However, their covariance matrices are either identical or different. For the former, i.e., identical covariance matrices, the optimization problem consists of a cost function that has been obtained by deriving a closed-form expression of the probability of misclassification. For the latter, i.e., different covariance matrices, we present an optimization problem where the cost function has been obtained via the Fisher separation function. For this, we also present a closed-form expression for an approximate solution to the optimization problem. We show that the proposed solution achieves performance levels comparable to the exact solution of the optimization problem obtained via state-of-the-art optimization solvers while incurring low computational complexity. We expand on the two main scenarios by introducing clutter (i.e., signal-dependent interference) in the signal model and studying possible closed-form designs in extreme waveform energy levels. Simulations are conducted using synthetically generated data in addition to the civilian vehicle data from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, in order to validate the proposed method.

Limited-Memory Receive Filter Design for Massive MIMO Radar in Signal-Dependent Interference

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.

Multi-Spectrally Constrained Transceiver Design Against Signal-Dependent Interference

This paper focuses on the joint synthesis of constant envelope transmit signal and receive filter aimed at optimizing radar performance in signal-dependent interference and spectrally contested-congested environments. To ensure the desired Quality of Service (QoS) at each communication system, a precise control of the interference energy injected by the radar in each licensed/shared bandwidth is imposed. Besides, along with an upper bound to the maximum transmitted energy, constant envelope (with either arbitrary or discrete phases) and similarity constraints are forced to ensure compatibility with amplifiers operating in saturation regime and bestow relevant waveform features, respectively. To handle the resulting NP-hard design problems, new iterative procedures (with ensured convergence properties) are devised to account for continuous and discrete phase constraints, capitalizing on the Coordinate Descent (CD) framework. Two heuristic procedures are also proposed to perform valuable initializations. Numerical results are provided to assess the effectiveness of the conceived algorithms in comparison with the existing methods.

MIMO Radar Codes/Filter Bank Optimization Design in Clutter Environment

Multiple-input multiple-output (MIMO) radar optimization design for extended targets in clutter is a challenging problem owing to the lack of perfect knowledge of the target impulse response (TIR). Assuming uncertain TIR, this paper focuses on the joint robust design of the radar codes and filter bank in MIMO structure to improve extended target detection capability in signal-dependent interference environment. Considering the average and the worst-case signal to interference plus noise ratio (SINR) at the output as the performance metrics to maximize, we propose two iterative optimization procedures, under similarity and energy requirements on the radar code. The convergence property and asymptotic optimality of two algorithms are analyzed. Finally, simulation results have displayed that two proposed optimization strategies achieve higher objective values than the existing approaches, and share reasonable computational burden.

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.

Joint waveform design of cognitive radar based on LSTM network in the presence of signal-dependent interference

Waveform design is a key component of cognitive radar. However, waveforms designed using a single criterion have difficulty meeting the multi-working mode of radar. We present a joint waveform design method in the presence of signal-dependent interference based on a long short term memory network. The proposed method combines characteristics of mutual information (MI) and signal to interference-plus-noise-ratio (SINR) criteria and achieves a smooth trade-off between them. The minimum mean square error (MMSE) method is set as a comparison to prove the enhanced capabilities in detection and recognition. Here, we initiate a final recognition rate indicator of radar to measure the radar’s comprehensive performance. As a result, compared with the MI criterion, SINR criterion, and MMSE method, to use as a transmission signal, the proposed paradigm increased the final recognition rate by 4.08%, 6.89%, and 5.25%, respectively, verifying the effectiveness of the proposed method.

Signal design based on post‐detection SINR for MIMO radar in the presence of signal‐dependent interference

In this study, the target detection and signal design problems in the presence of signal-dependent interference for a colocated multiple-input multiple-output (MIMO) radar employing a space-(slow) time transmit signal are addressed. Due to the large dimension of the clutter covariance matrix (CCM) in MIMO radars, the Kronecker form of the CCM is exploited to derive a Rao-based detector, which does not require the secondary data. According to the numerical results, the proposed detector considerably outperforms its competitors. Additionally, by tackling a non-convex sum of ratio problem, a low-complexity algorithm is devised to maximise the post-detection signal-to-interference-plus-noise ratio ( SINR) under constant modulus and similarity constraints. Finally, using the simulation results, the effectiveness of the designed signal on the system performance is highlighted.

Constant modulus waveform design for colocated MIMO radar: A convex relaxation approach

This paper considers the waveform design for colocated MIMO radar to improve target detectability embedded in signal-dependent interferences, and maximize the SINR (signal-to-interference-plus-noise ratio). Due to constant modulus constraint, the problem is indeed non-convex and NP-hard. To address the challenge of this intractable problem, we propose a novel optimization framework for solving the problem based on convex relaxation. In the proposed algorithm, the original optimization problem is reformulated heuristically by introducing an auxiliary variable. Then it can be relaxed to a convex optimization problem and efficiently solved by alternative optimizing. Numerical results show that the proposed algorithm improves the beampattern performance significantly by generating much deeper notches at the interference directions, then obtains higher SINR over the state-of-the-art method.

Adaptive Radar Waveform Design Based on Weighted MI and the Difference of Two Mutual Information Metrics

This study deals with the problem of radar waveform design based on the weighted mutual information (MI) and the difference of two mutual information metrics (DMI) in signal-dependent interference. Since the target and clutter information are included in the received signal at the beginning of the design, DMI-based waveform is designed according to the following criterion: maximizing the MI between the received signal and target impulse response while minimizing the MI between the received signal and the clutter impulse response. This criterion is equivalent to maximizing the difference between the first MI and the second MI. Then maximizing the difference of two types of MI is used as the objective function, and the optimization model with the transmitted waveform energy constraint is established. In order to solve it, we resort to maximum marginal allocation (MMA) method to find the DMI-based waveform. Since DMI-based waveform does not allocate energy to the frequency band where the clutter power spectral density (PSD) is greater than the target PSD, we propose to weight the MI-based waveform and DMI-based waveform to synthesize the final optimal waveform. It could provide different trade-offs between two types of MI. Simulation results show the proposed algorithm is valid.

Energy Efficiency Optimization in Radar-Communication Spectrum Sharing

Energy efficiency, possibly coupled with cognition-based and spectrum-sharing architectures, is a key enabling technology for green communications in 5G-and-beyond standards. In this context, the present paper considers a multiple-input multiple-output communication system cooperatively coexisting with a surveillance radar: the objective function is the communication system energy efficiency, while radar operation is safeguarded by constraining the minimum received signal-to-disturbance ratio for a set of range-azimuth cells of the controlled scene, and no time synchronization between them is assumed. The degrees of freedom are the transmit powers of both systems, the space-time communication codebook, and the linear filters at the radar receiver. The resulting optimization problem is non-convex, due to both the objective function and the presence of signal-dependent interference (clutter): we develop a block-coordinate-ascent approximate solution and offer a thorough performance assessment, so as to elicit the merits of the proposed approach, along with the interplay among the achievable energy efficiency, the density of scatterers in the environment, and the size of the set of protected radar cells.

Robust transmit waveform and receive filter design in the presence of eclipsing loss and signal-dependent interference

Eclipsing is the designation for the attendant loss that occurs for return echoes that arrive when the receiver is turned off during the transmission of a long pulse. Assuming unknown knowledge about the target echo delay (TED), this paper presents a robust design approach to jointly optimize the transmit sequence and receive filter for the detection of radar targets in the presence of return echo eclipsing loss and signal-dependent interference.The worst-case signal-to-interference-plus-noise-ratio (SINR) at the output of the filter is considered as the performance measure of the system. The robust design problem is formulated as a non-convex max-min optimization problem to robustify the system SINR with respect to the unknown target eclipsing loss condition. In addition to an energy constraint, an upper bound to the peak-to-average-power ratio (PAR) is imposed on the transmit sequence. The original non-convex optimization problem is solved using relaxation methods and an iterative optimization process alternating between two sequential semi-definite programming (SDP) problems. Then, the randomization methods are utilized to synthesize the transmit sequence and the corresponding filter sharing the robust response. Finally, the effectiveness of the proposed procedure is demonstrated through experimental results, underlining the performance enhancement offered by a robust joint design.

Performance analysis of the matched subspace detector in the presence of signal-dependent interference for MIMO radar

For a statistical multiple-input multiple-output (MIMO) radar, we investigate the maximum achievable diversity gain and the minimum possible probability of miss detection and the conditions of achieving those at sufficiently high signal-to-noise ratio (SNR) when the matched subspace detector is employed in the presence of signal-dependent interference. In addition to linear dependence between target returns at each receiver, we show that linear dependence between target and interference subspaces reduces the diversity gain when the matched subspace detector is employed. We derive the approximated form of the probability of miss and introduce the signaling loss concept based on it. We show that the probability of miss is minimized at high SNR when not only is the diversity gain maximized, but the signaling loss is also minimized. Then, we use the signaling loss corresponding to the maximum diversity gain as a novel design metric for designing signals and minimizing the probability of miss at sufficiently high SNR in statistical MIMO radar. Due to the fact that designing the optimal signals is challenging, we derive analytically the detection performance loss of quasi-optimal signals by employing the signaling loss concept.

Quadratic Semidefinite Programming for Waveform-Constrained Joint Filter-Signal Design in STAP

In waveform-adaptive space-time adaptive processing (STAP), signal-dependent interference can be mitigated through joint filter-signal design. For the common signal-to-interference-plus-noise ratio (SINR) optimality criterion, these designs result in biquadratic problems, which can be efficiently solved through joint relaxation. In this paper, we consider the addition of two realistic waveform constraints to this process, constant modulus (CM) and the similarity constraint (SC), and demonstrate that they are both amenable to a quadratic semidefinite program (QSDP) via relaxation. We examine the optimality conditions of the newly constrained problems and demonstrate, both analytically and numerically, improved performance over existing iterative techniques. To increase solution robustness, additional refinement strategies for rank-inflated relaxed solutions are also provided and analyzed which extend the aforementioned performance gain.

MIMO Radar Waveform Optimization in Clutter Under General Power Constraints

In this paper, we investigate a novel framework on the waveform optimization for multiple-input multiple-output (MIMO) radar systems under assumptions of statistical information of scattering matrices and colored noise. Different from most of traditional designs under total power constraint, in our framework, a general power constraint named as multiple weighted power constraints is provided, which includes total power constraint, per-antenna power constraints as its special cases. In the framework, the transmit and receive correlations of parameter matrix to estimate and the spatial and time correlations of noise matrix are all taken into account. Moreover, the signal-dependent interference is also taken into account, which can be considered as a self-interference noise. Based on the available statistics of scattering matrices and colored noise, two kinds of estimation schemes are proposed in this paper, which can realize different trade-offs between complexity and performance. Finally, some numerical results are given to demonstrate the performance advantages of the proposed radar waveform designs.

Robust Transceiver Design for Extended Target Detection in a Signal-Dependent Interference Scenario

Assuming unknown knowledge of target impulse response (TIR), this paper develops robust design methods to optimize the transceiver for the detection of extended targets in the presence of signal-dependent interference. Two different transceiver design schemes comprising single filter and multiple filters methods are presented to handle the fact that the TIR of extended target is unknown at the transmit side. The lowest signal to interference plus noise ratio (SINR) is considered at the receiver side as the performance measure of the system. Two robust design problems are formulated as max-min optimization problems to robustify the system SINR with respect to the unknown TIR. The former is solved using an iterative optimization process alternating between two sequential semi-definite programming (SDP) problems; the latter is solved employing an iterative optimization procedure alternating between a convex and a generalized fractional programming problems. Finally, several numerical examples highlight that the proposed design schemes have better robustness compared to several counterparts which are presented in open literature.

Riemannian Geometric Optimization Methods for Joint Design of Transmit Sequence and Receive Filter on MIMO Radar

In this paper, we study the joint design of a transmit sequence and a receive filter for an airborne multiple-input multiple-output (MIMO) radar system to improve its moving target detection performance in the presence of signal-dependent interference. The optimization problem is formulated to maximize the output signal-to-noise-plus-interference ratio (SINR), subject to the waveform constant-envelope (CE) constraint. To address the challenge of this non-convex problem, we propose a novel optimization framework for solving the problem over a Riemannian manifold which is the product of complex circles and a Euclidean space. Manifold optimization views the constrained optimization problem as an unconstrained one over a restricted search space. The Riemannian gradient descent algorithms and the Riemannian trust-region algorithm are then developed to solve the reformulated problem efficiently with low iteration complexity. In addition, the proposed manifold-based algorithms provably converge to an approximate local optimum from an arbitrary initialization point. Numerical experiments demonstrate the algorithmic advantages and performance gains of the proposed algorithms.