In this paper, we consider the joint design of both transmit waveforms and receive filters for a colocated multipleinput-multiple-output (MIMO) radar with the existence of signaldependent interference and white noise. The design problem is formulated into a maximization of the signal-to-interferenceplus- noise ratio (SINR), including various constraints on the transmit waveforms. Compared with the traditional alternating semidefinite relaxation (SDR) approach, a general and flexible algorithm is proposed based on the majorization-minimization (MM) method with guaranteed monotonicity, lower computational complexity per iteration and/or convergence to a Bstationary point. Many waveform constraints can be flexibly incorporated into the algorithm with only a few modifications. Furthermore, the connection between the proposed algorithm and the alternating optimization approach is revealed. Finally, the proposed algorithm is evaluated via numerical experiments in terms of SINR performance, ambiguity function, computational time, and properties of the designed waveforms. The experiment results show that the proposed algorithms are faster in terms of running time and meanwhile achieve as good SINR performance as the the existing methods.

The ambiguity function plays an important role in radar systems. In fact, many radar design problems can be interpreted from the perspective of persuing desired ambiguity functions to adapt to various application scenes. In this paper, we consider designing a radar sequence, subject to a peak-to-average power ratio (PAR) constraint, to maximize the signal-to-interference plus noise ratio, which can also be interpreted as designing a sequence with a desired ambiguity function. From an optimization point of view, this is equivalent to optimizing a complex quartic function with the PAR constraint. An efficient algorithm based on the general majorization–minimization (MM) method is developed to solve this problem with guaranteed convergence to a stationary point under some mild conditions. In addition, the unit-modulus constraint, as a special case, is considered and another algorithm is proposed, which is the combination of the general MM and the coordinate descent method. Numerical experiments show that the proposed algorithms can shape a desired ambiguity function based on the prior information, and the performance is much better compared with the existing methods.

Beam Hopping (BH) is a popular technique considered for next-generation multi-beam satellite communication system which allows a satellite focusing its resources on where they are needed by selectively illuminating beams. While beam illumination plan can be adjusted according to its needs, the main limitation of convectional BH is the adjacent beam avoidance requirement needed to maintain acceptable levels of interference. With the recent maturity of precoding technique, a natural way forward is to consider a dynamic beam illumination scheme with selective precoding, where large areas with high-demand can be covered by multiple active precoded beams. In this paper, we mathematically model such beam illumination design problem employing an interference-based penalty function whose goal is to avoid precoding whenever possible subject to beam demand satisfaction constraints. The problem can be written as a binary quadratic programming (BQP). Next, two convexification frameworks are considered namely: (i) A Semi-Definition Programming (SDP) approach particularly targeting BQP type of problems, and (ii) Multiplier Penalty and Majorization-Minimization (MPMM) based method which guarantees to converge to a local optimum. Finally, a greedy algorithm is proposed to alleviate complexity with minimal impact on the final performance. Supporting results based on numerical simulations show that the proposed schemes outperform the relevant benchmarks in terms of demand matching performance while minimizing the use of precoding.

Source localization plays a key role in many applications including radar, wireless and underwater communications. Among various localization methods, the most popular ones are Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), Angle-Of-Arrival (AOA) and Received Signal Strength (RSS) based. Since the Cramér-Rao lower bounds (CRLB) of these methods depend on the sensor geometry explicitly, sensor placement becomes a crucial issue in source localization applications. In this paper, we consider finding the optimal sensor placements for the TOA, TDOA, AOA and RSS based localization scenarios. We first unify the three localization models by a generalized problem formulation based on the CRLB-related metric. Then a u nified op t imization fra m ework for o ptimal s ensor placemen t (UTMOST) is developed through the combination of the alternating direction method of multipliers (ADMM) and majorization-minimization (MM) techniques. Unlike the majority of the state-of-the-art works, the proposed UTMOST neither approximates the design criterion nor considers only uncorrelated noise in the measurements. It can readily adapt to to different design criteria (i.e. A, D and E-optimality) with slight modifications within the framework and yield the optimal sensor placements correspondingly. Extensive numerical experiments are performed to exhibit the efficacy and flexibility of the proposed framework.

Optimal allocation of shared resources is key to deliver the promise of jointly operating radar and communications systems. In this paper, unlike prior works which examine synergistic access to resources in colocated joint radar-communications or among identical systems, we investigate this problem for a distributed system comprising heterogeneous radars and multi-tier communications. In particular, we focus on resource allocation in the context of multi-target tracking (MTT) while maintaining stable communications connections. By simultaneously allocating the available power, dwell time and shared bandwidth, we improve the MTT performance under a Bayesian tracking framework and guarantee the communications throughput. Our ${a}$ lter ${n}$ ating allo ${c}$ ation of ${h}$ eterogene ${o}$ us ${r}$ esources (ANCHOR) approach solves the resulting non-convex problem based on the alternating optimization method that monotonically improves the Bayesian Cramér-Rao bound. Numerical experiments demonstrate that ANCHOR significantly improves the tracking error over two baseline allocations and stability under different target scenarios and radar-communications network distributions.

Reconfigurable Intelligent Surface (RIS) brings a transformative potential to radar systems by providing a new dimension to control electromagnetic waves. This paper focuses on enhancing target detectability through the design and analysis of RIS-aided radar. A comprehensive signal model is established, recognizing both Line of Sight (LoS) and Non-Line of Sight (N-LoS) returns, factoring in RIS location and range gate shifts. This modeling enables an examination of the RIS-dependent effect of enlarged clutter region, underscoring the essential need for precise RIS phase optimization. A joint design problem encompassing transmit waveform, receive filter, and RIS phase is then formulated with the aim to optimize Signal-to-Interference-plus-Noise-Ratio (SINR), complying with practical waveform constraints and discrete RIS phase alphabet. For this nonconvex problem, an iterative algorithm is developed to monotonically enhance SINR, ensuring convergence by alternately updating the radar waveform and RIS phases. Through the majorization-minimization framework, radar waveform updates are achieved using the feasible point pursuit technique, while a quasi-closed form solution is employed for the RIS phases. Simulation results demonstrate the efficacy of the proposed design, revealing the crucial role of RIS in the system.