11 accepted papers
In this paper, we explore the joint design of a transmit waveform and receive filter to enhance the detection performance of multiple-input multiple-output (MIMO) radar. Target echoes are assumed to be embedded in signal-dependent interference and colored Gaussian noise. As design metrics, we exploi…
Dual-function radar-communication (DFRC) system is flexible to be applied in a variety of scenarios. However, it is challenging to implement a low-cost low-complexity DFRC system due to the dynamic cooperation between radar sensing and communication tasks. In this paper, we propose to implement a lo…
To maximize the signal-to-interference-plus-noise ratio (SINR) under a constant-envelope constraint, an efficient joint design of the transmit waveform and the receive filter for multipleinput multiple-output (MIMO) radars is essential. In this paper, we propose a novel optimization framework to sol…
Cognitive radar framework has recently been proposed in radar signal processing to develope algorithms for target detection, tracking, and waveform design in the presence of nonstationary environmental (clutter) characteristics. In this framework, there are the three main steps: sensing the environm…
Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics. Graph matching problems can be cast as two types of quadratic assignment problems (QAPs): Koopmans-Beckmann's QAP or Lawler's QAP. In our paper, we provide a unifying view for these two pr…
Domain shift, which occurs when there is a mismatch between the distributions of training (source) and testing (target) datasets, usually results in poor performance of the trained model on the target domain. Existing algorithms typically solve this issue by reducing the distribution discrepancy in…
Graph-structured data arise in wide applications, such as computer vision, bioinformatics, and social networks. Quantifying similarities among graphs is a fundamental problem. In this paper, we develop a framework for computing graph kernels, based on return probabilities of random walks. The advant…
We investigate the problem of direction of arrival (DOA) estimation using sparse linear arrays, such as co-prime and nested arrays, in the case of missing data resulting from sensor failures. We introduce a signal model where sensor failures occur after taking certain number of snapshots. We formula…
In this paper, we provide a novel regression algorithm based on a Gaussian random field (GRF) indexed by a Riemannian manifold (M, g). We utilize both the labeled and unlabeled data sets to exploit the geometric structure of M. We use the recovered heat (H) kernel as the covariance function for the…
Sparse linear arrays, such as co-prime and nested arrays, can identify up to O(M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) sources with only O(M) sensors by using co-array based MUSIC. We conduct analytical performance analysis of two co…
Multiple scattering effects are commonly ignored in the detection and estimation of scatterers in signal processing research, because the energy of the first-order scattering is much larger than that of higher-order components. Although multiple scattering can significantly increase the estimation p…