Adaptive Interference Removal for Un-coordinated Radar/Communication Co-existence

Most existing approaches to co-existing communication/radar systems assume that the radar and communication systems are coordinated, i.e., they share information, such as relative position, transmitted waveforms and channel state. In this paper, we consider an un-coordinated scenario where a communication receiver is to operate in the presence of a number of radars, of which only a sub-set may be active, which poses the problem of estimating the active waveforms and the relevant parameters thereof, so as to cancel them prior to demodulation. Two algorithms are proposed for such a joint waveform estimation/data demodulation problem, both exploiting sparsity of a proper representation of the interference and of the vector containing the errors of the data block, so as to implement an iterative joint interference removal/data demodulation process. The former algorithm is based on classical on-grid compressed sensing (CS), while the latter forces an atomic norm (AN) constraint: in both cases the radar parameters and the communication demodulation errors can be estimated by solving a convex problem. We also propose a way to improve the efficiency of the AN-based algorithm. The performance of these algorithms are demonstrated through extensive simulations, taking into account a variety of conditions concerning both the interferers and the respective channel states

A nonquadratic regularization-based technique for joint SAR imaging and model error correction

Regularization based image reconstruction algorithms have successfully been applied to the synthetic aperture radar (SAR) imaging problem. Such algorithms assume that the mathematical model of the imaging system is perfectly known. However, in practice, it is very common to encounter various types of model errors. One predominant example is phase errors which appear either due to inexact measurement of the location of the SAR sensing platform, or due to effects of propagation through atmospheric turbulence. We propose a nonquadratic regularization-based framework for joint image formation and model error correction. This framework leads to an iterative algorithm, which cycles through steps of image formation and model parameter estimation. This approach offers advantages over autofocus techniques that involve post-processing of a conventionally formed image. We present results on synthetic scenes, as well as the Air Force Research Laboratory (AFRL) Backhoe data set, demonstrating the effectiveness of the proposed approach