91 research outputs found
Optimization Based Sensor Placement for Multi-Target Localization With Coupling Sensor Clusters
No full textpeer reviewe
Constant-Modulus Waveform Design With Polarization-Adaptive Power Allocation in Polarimetric Radar
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Optimization methods for radar waveform design
No full textRadar has been evolving itself significantly since MIMO and cognition were introduced. One important research topic is the design of radar waveform. By exploiting the waveform diversity, the performance of the radar systems can be improved significantly. The waveform design needs to satisfy three requirements. First, the designed waveform should perform well in the evaluation of some metrics. Second, the designed waveform should satisfy some nice properties so that it will be compatible with the hardware configuration and application scenarios. Last but not least, the design algorithm should be computationally efficient for the real-time radar applications. The focus of this dissertation is on the development of efficient optimization methods based on some optimization frameworks for radar waveform design. The first question is what metric is used to measure the design performance. Among many metrics, the most important one is signal-to-interference plus noise ratio (SINR), which determines the probability of detection. Thus, the common formulation of the design problem is a maximization of the derived SINR subject to some waveform constraints. The existing methods to solve this kind of problems are mostly based the semidefinite programming (SDP). However, this kind of methods are very time-consuming, and in some cases, without guarantee of monotonicity or further convergence. To deal with these issues, we propose optimization methods based on the majorization-minimization framework for the SINR maximization problem in the context of cognitive radar. Then, we extend our methodology into the joint design problem, in which we jointly design the transmit waveforms and receive filters in the context of MIMO radar. The derived algorithm is very flexible in that it can deal with many waveform constraints by only a slight modification. The numerical results show that our methods can achieve the same or even better SINR than the benchmarks but with less computational cost. The second question is what properties the design waveform should possess. An important one is the desired spectral shape. Specifically, the transmit sequence should avoid certain frequency bands or try to minimize the spectral power on those bands. The motivation behind is spectral sharing, which becomes a solution to tackle the ever growing demand of spectrum resources from multiple RF services. Recently proposed spectral level ratio (SLR) is interesting and simple compared with other existing approaches. We extend th SLR to a regularized SLR (RSLR) which is more suitable for optimization. However, the formulated RSLR minimization problem is very hard to solve because it is fractional, nonsmooth and nonconvex. Thus, our goal is to develop optimization methods for the RSLR problem. We develop two algorithms by combining both the majorization-minimization and the Dinkelbach’s algorithm. Numerical simulations show that one algorithm is suitable for generating good initialization, and the other algorithm performs better than the benchmark in terms of both SLR and running time.</p
Cognition-enabled waveform design for ambiguity function shaping
No full textOne distinguished feature of cognitive radar is the ability of intelligent sensing, which relies much on the transmit waveform in a self-perpetuating manner. On the one hand, the transmit waveform affects significantly on the quality of the backscatter echoes, from which the environmental parameters are inferred by estimation and learning techniques. On the other hand, the waveform design based on the extracted information will further strengthen the radar performance in the next illumination. This chapter focuses on the latter aspect to illustrate how to design waveforms under specific circumstances by exploiting the prior knowledge obtained by a cognitive radar. Two waveform design problems for different application scenarios are presented in a unified waveform design pattern from the perspective of ambiguity function. The first problem is in fact to shape the ambiguity function by making use of the prior information on the scatters. The second problem is to design a waveform with the desired spectral shape for coexistence by leveraging on the information of stopbands and passbands
Sequence design for spectral shaping via minimization of regularized spectral level ratio
No full textThe topic of sequence design has received considerable attention due to its wide applications in active sensing. One important desired property for the design sequence is the spectral shape. In this paper, the sequence design problem is formulated by minimizing the regularized spectral level ratio subject to a peak-to-average power ratio constraint. Then, two algorithms are proposed by combining both the Dinkelbach's algorithm and the majorization-minimzation (MM) method organically. Specifically, by using the Dinkelbach's algorithm, the challenging fractional programming problem can be handled by solving a series of subproblems, which are further solved via the MM method. The numerical experiments verify the effectiveness of the optimization metric and demonstrate the performance of the proposed algorithms compared with the benchmark.</p
Radar waveform design via the majorization-minimization framework
No full textA multiple input multiple output (MIMO) radar transmits several probing waveforms simultaneously from its transmit antennas. It can improve the interference rejection capability and parameter identifiability as well as provide fl exibility for transmit beam pattern design . In addition, a cognitive approach for radar systems, which capitalizes on the information obtained from the surrounding environment or the prior knowledge stored in the platform, was proposed . The significance of MIMO radar and the cognitive approach has recently motivated active research into the waveform design. A well -designed waveform can allow for more accurate detection and estimation
A fast algorithm for joint design of transmit waveforms and receive filters
No full textJoint design of transmit waveforms and receive filters has been an active research field since the emergence of MIMO radar. The design problem is usually formulated into a maximization of the signal-to-interference-plus-noise ratio (SINR), subject to waveform constraints. A widely used approach is the alternating optimization scheme combined with the rank-one constrained SDP programming. In this paper, a new algorithm based on the majorization-minimization (MM) method is proposed. This algorithm is not only capable of dealing with various waveform constraints, but also computationally efficient. Furthermore, its connection to the alternating optimization approach is also revealed. Numerical experiments show that the proposed algorithm outperforms the existing benchmarks in terms of running time and/or achieved SINR.</p
Exploiting constructive interference in symbol level hybrid beamforming for dual-function radar-communication system
No full textpeer reviewedIn this letter, we study the Hybrid Beamforming (HBF) design for a Dual-Function Radar-Communication (DFRC) system, which serves Multiple Users (MUs) and detects a target in the presence of signal-dependent clutters, simultaneously. Unlike conventional beamforming strategies, we propose a novel one on the symbol level, which exploits Constructive Interference (CI) to achieve a trade-off between radar and communication using one platform. To implement this novel strategy, we jointly design the DFRC transmit HBF and radar receive beamforming by maximizing the radar Signal to Interference plus Noise Ratio (SINR) while ensuring the Quality of Service (QoS) of downlink communication. To tackle the formulated non-convex problem, we propose an iterative algorithm, which combines the Majorization-Minimization (MM) and Alternating Direction Method of Multipliers (ADMM) judiciously. The numerical experiments indicate that our algorithm yields the CI properly for robust communications and achieves better performance than the conventional HBF benchmarks in both communication bit error rate and radar SINR
A Hybrid Approach to Optimal TOA-Sensor Placement With Fixed Shared Sensors for Simultaneous Multi-Target Localization
No full textpeer reviewedThis paper focuses on optimal time-of-arrival (TOA) sensor placement for multiple target localization simultaneously. In previous work, different solutions only using non-shared sensors to localize multiple targets have been developed. Those methods localize different targets one-by-one or use a large number of mobile sensors with many limitations, such as low effectiveness and high network complexity. In this paper, firstly, a novel optimization model for multi-target localization incorporating shared sensors is formulated. Secondly, the systematic theoretical results of the
optimal sensor placement are derived and concluded using the A-optimality criterion, i.e., minimizing the trace of the inverse Fisher information matrix (FIM), based on rigorous geometrical derivations. The reachable optimal trace of Cramér-Rao lower bound (CRLB) is also derived. It can provide optimal conditions for many cases and even closed form solutions for some special cases. Thirdly, a novel numerical optimization algorithm to quickly find and calculate the (sub-)optimal placement and achievable lower bound is explored, when the model becomes complicated with more practical constraints. Then, a hybrid method for solving the most general situation, integrating both the analytical and numerical solutions, is proposed. Finally, the correctness and effectiveness of the proposed theoretical and mathematical methods are demonstrated by several simulation examples
Transmit Waveform/Receive Filter Design for MIMO Radar With Multiple Waveform Constraints
No full textIn this paper, we consider the joint design of both transmit waveforms and receive filters for a colocated multiple-input-multiple-output (MIMO) radar with the existence of signal-dependent interference and white noise. The design problem is formulated into a maximization of the signal-to-interference-plus-noise ratio (SINR), including various constraints on the transmit waveforms. Compared with the traditional alternating semidefinite relaxation approach, a general and flexible algorithm is proposed based on the majorization-minimization method with guaranteed monotonicity, lower computational complexity per iteration and/or convergence to a B-stationary 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.</p
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