Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar

In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting. We then compare the utility of several RL algorithms through a discussion of experiments performed on Commercial off-the-shelf (COTS) hardware. Each RL technique is evaluated in terms of convergence, radar detection performance achieved in a congested spectral environment, and the ability to share 100MHz spectrum with an uncooperative communications system. We examine policy iteration, which solves an environment posed as a Markov Decision Process (MDP) by directly solving for a stochastic mapping between environmental states and radar waveforms, as well as Deep RL techniques, which utilize a form of Q-Learning to approximate a parameterized function that is used by the radar to select optimal actions. We show that RL techniques are beneficial over a Sense-and-Avoid (SAA) scheme and discuss the conditions under which each approach is most effective.Comment: Accepted for publication at IEEE Intl. Radar Conference, Washington DC, Apr. 2020. This is the author's version of the wor

Detection and classification of buried dielectric anomalies using a separated aperture sensor and a neural network discriminator

Includes bibliographical references.The problem of detection and classification of buried dielectric anomalies using a separated aperture microwave sensor and an artificial neural network discriminator was considered. Several methods for training and data representation were developed to study the trainability and generalization capabilities of the networks. The effect of the architectural variation on the network performance was also studied. The principal component method was used to reduce the volume of the data and also the dimension of the weight space. Simulation results on two types of targets were obtained which indicated superior detection and classification performance when compared with the conventional methods

Hardware Design of a High Dynamic Range Radio Frequency (RF) Harmonic Measurement System

Radio frequency (RF) circuit elements that are traditionally considered to be linear frequently exhibit nonlinear properties that affect the intended operation of many other RF systems. Devices such as RF connectors, antennas, attenuators, resistors, and dissimilar metal junctions generate nonlinear distortion that degrades primary RF system performance. The communications industry is greatly affected by these unintended and unexpected nonlinear distortions. The high transmit power and tight channel spacing of the communication channel makes communications very susceptible to nonlinear distortion. To minimize nonlinear distortion in RF systems, specialized circuits are required to measure the low level nonlinear distortions created from traditionally linear devices, i.e., connectors, cables, antennas, etc. Measuring the low-level nonlinear distortion is a difficult problem. The measurement system requires the use of high power probe signals and the capability to measure very weak nonlinear distortions. Measuring the weak nonlinear distortion becomes increasingly difficult in the presence of higher power probe signals, as the high power probe signal generates distortion products in the measurement system. This paper describes a circuit design architecture that achieves 175 dB of dynamic range which can be used to measure low level harmonic distortion from various passive RF circuit elements

The Spectrum Analysis Solution (SAS) System: Theoretical Analysis, Hardware Design and Implementation

This paper describes a multichannel super-heterodyne signal analyzer, called the Spectrum Analysis Solution (SAS), which performs multi-purpose spectrum sensing to support spectrally adaptive and cognitive radar applications. The SAS operates from ultrahigh frequency (UHF) to the S-band and features a wideband channel with eight narrowband channels. The wideband channel acts as a monitoring channel that can be used to tune the instantaneous band of the narrowband channels to areas of interest in the spectrum. The data collected from the SAS has been utilized to develop spectrum sensing algorithms for the budding field of spectrum sharing (SS) radar. Bandwidth (BW), average total power, percent occupancy (PO), signal-to-interference-plus-noise ratio (SINR), and power spectral entropy (PSE) have been examined as metrics for the characterization of the spectrum. These metrics are utilized to determine a contiguous optimal sub-band (OSB) for a SS radar transmission in a given spectrum for different modalities. Three OSB algorithms are presented and evaluated: the spectrum sensing multi objective (SS-MO), the spectrum sensing with brute force PSE (SS-BFE), and the spectrum sensing multi-objective with brute force PSE (SS-MO-BFE)

Adaptable Bandwidth for Harmonic Step-Frequency Radar

A spectrum sensing technique is described which is used to enhance the performance of harmonic step-frequency radar in the presence of harmful radio frequency (RF) interference (RFI). This technique passively monitors the RF spectrum for subbands of high signal-to-interference-plus-noise ratio (SINR) within a constrained bandwidth of interest. An optimal subband is selected for the harmonic radar that maximizes SINR and minimizes the range resolution cell size, two conflicting objectives. The approach is tested using an experimental setup that injects high power RFI into a harmonic step-frequency radar, which significantly degrades radar performance. It is shown that the proposed spectrum sensing technique significantly improves the SINR and the peak-to-average sidelobe power level of the harmonic radar at the sacrifice of range resolution

A survey of general-purpose computation on graphics hardware

The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability, have made graphics hardware acompelling platform for computationally demanding tasks in awide variety of application domains. In this report, we describe, summarize, and analyze the latest research in mapping general-purpose computation to graphics hardware. We begin with the technical motivations that underlie general-purpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. We then aim the main body of this report at two separate audiences. First, we describe the techniques used in mapping general-purpose computation to graphics hardware. We believe these techniques will be generally useful for researchers who plan to develop the next generation of GPGPU algorithms and techniques. Second, we survey and categorize the latest developments in general-purpose application development on graphics hardware