Fuzzy Chance-constrained Programming Based Security Information Optimization for Low Probability of Identification Enhancement in Radar Network Systems

In this paper, the problem of low probability of identification (LPID) improvement for radar network systems is investigated. Firstly, the security information is derived to evaluate the LPID performance for radar network. Then, without any prior knowledge of hostile intercept receiver, a novel fuzzy chance-constrained programming (FCCP) based security information optimization scheme is presented to achieve enhanced LPID performance in radar network systems, which focuses on minimizing the achievable mutual information (MI) at interceptor, while the attainable MI outage probability at radar network is enforced to be greater than a specified confidence level. Regarding to the complexity and uncertainty of electromagnetic environment in the modern battlefield, the trapezoidal fuzzy number is used to describe the threshold of achievable MI at radar network based on the credibility theory. Finally, the FCCP model is transformed to a crisp equivalent form with the property of trapezoidal fuzzy number. Numerical simulation results demonstrating the performance of the proposed strategy are provided

Performance Estimates of the Pseudo-Random Method for Radar Detection

A performance of the pseudo-random method for the radar detection is analyzed. The radar sends a pseudo-random sequence of length $N$, and receives echo from $r$ targets. We assume the natural assumptions of uniformity on the channel and of the square root cancellation on the noise. Then for $r \leq N^{1-\delta}$, where $\delta > 0$, the following holds: (i) the probability of detection goes to one, and (ii) the expected number of false targets goes to zero, as $N$ goes to infinity.Comment: 5 pages, two figures, to appear in Proceedings of ISIT 2014 - IEEE International Symposium on Information Theory, Honolul

A Vector Channel Based Approach to MIMO Radar Waveform Design for Extended Targets

Radar systems have been used for many years for estimating, detecting, classifying, and imaging objects of interest (targets). Stealthier targets and more cluttered environments have created a need for more sophisticated radar systems to gain more precise information about the radar environment. Because modern radar systems are largely defined in software, adaptive radar systems have emerged that tailor system parameters such as the transmitted waveform and receiver filter to the target and environment in order to address this need. The basic structure of a radar system exhibits many similarities to the structure of a communication system. Recognizing the parallel composition of radar systems and information transmission systems, initial works have begun to explore the application of information theory to radar system design, but a great deal of work still remains to make a full and clear connection between the problems addressed by radar systems and communication systems. Forming a comprehensive definition of this connection between radar systems and information transmission systems and associated problem descriptions could facilitate the cross-discipline transfer of ideas and accelerate the development and improvement of new system design solutions in both fields. In particular, adaptive radar system design is a relatively new field which stands to benefit from the maturity of information theory developed for information transmission if a parallel can be drawn to clearly relate similar radar and communication problems. No known previous work has yet drawn a clear parallel between the general multiple-input multiple-output (MIMO) radar system model considering both the detection and estimation of multiple extended targets and a similar multiuser vector channel information transmission system model. The goal of this dissertation is to develop a novel vector channel framework to describe a MIMO radar system and to study information theoretic adaptive radar waveform design for detection and estimation of multiple radar targets within this framework. Specifically, this dissertation first provides a new compact vector channel model for representing a MIMO radar system which illustrates the parallel composition of radar systems and information transmission systems. Second, using the proposed framework this dissertation contributes a compressed sensing based information theoretic approach to waveform design for the detection of multiple extended targets in noiseless and noisy scenarios. Third, this dissertation defines the multiple extended target estimation problem within the framework and proposes a greedy signal to interference-plus-noise ratio (SINR) maximizing procedure based on a similar approach developed for a collaborative multibase wireless communication system to optimally design wave forms in this scenario

Goldstone Solar System Radar (GSSR)

The primary objective of the Goldstone Solar System Radar is the investigation of solar system bodies by means of Earth-based radar. Targets of primary interest include the Galilean moons, Saturn's rings and moons, and Earth-approaching asteroids and comets. Planets are also of interest, particularly Mercury and the planets to which NASA has not yet planned spacecraft visits. Based on a history of solid achievement, including the definition of the Astronomical Unit, imaging and topography of Mars, Venus, and Mercury, and contributions to the general theory of relativity, the program will continue to support flight project requirements and its primary objectives. The individual target objectives are presented, and information on the following topics are presented in tabular form: Deep Space Network support, compatibility tests, telemetry, command, and tracking support responsibility

Boundary Contour System and Feature Contour System

When humans gaze upon a scene, our brains rapidly combine several different types of locally ambiguous visual information to generate a globally consistent and unambiguous representation of Form-And-Color-And-DEpth, or FACADE. This state of affairs raises the question: What new computational principles and mechanisms are needed to understand how multiple sources of visual information cooperate automatically to generate a percept of 3-dimensional form? This chapter reviews some modeling work aimed at developing such a general-purpose vision architecture. This architecture clarifies how scenic data about boundaries, textures, shading, depth, multiple spatial scales, and motion can be cooperatively synthesized in real-time into a coherent representation of 3-dimensional form. It embodies a new vision theory that attempts to clarify the functional organzation of the visual brain from the lateral geniculate nucleus (LGN) to the extrastriate cortical regions V4 and MT. Moreover, the same processes which are useful towards explaining how the visual cortex processes retinal signals are equally valuable for processing noisy multidimensional data from artificial sensors, such as synthetic aperture radar, laser radar, multispectral infrared, magnetic resonance, and high-altitude photographs. These processes generate 3-D boundary and surface representations of a scene.Office of Naval Research (N00011-95-I-0409, N00014-95-I-0657

Remote Sensing of Ocean Winds and Waves with Bistatic HF Radar

High frequency, or HF, coastal radars collect a vast amount of data on ocean currents, winds and waves. The technology continuously measures the parameters, by receiving and interpreting electromagnetic waves scattered by the ocean surface. Formulating the methods to interpret the radar data, to obtain accurate measurements, has been the focus of many researchers since the 1970s. Much of the existing research has been in monostatic radar theory, where the transmitter and receiver are stationed together. However, a larger, higher quality dataset can be obtained by utilising bistatic radar theory, whereby the transmitter and receiver are located at separate sites. In this work, the focus is on bistatic radar, where the most commonly used mathematical model for monostatic radar is adapted for bistatic radar. Methods for obtaining current, wind and wave information from the model are then described and in the case of winds and waves, tested. Investigating the derived model shows that it does not always fit the real data well, due to undesirable effects of the radar. These effects can be incorporated into the model but then the existing methods used to obtain ocean information may not be applicable. Therefore, a new method for measuring ocean waves from the model is developed. The recent advances in machine learning have been substantial, with the neural network becoming proficient at finding the link between complexly related datasets. In this work, a neural network is used to model the relationship between the developed radar model and the directional ocean spectrum. It is shown to successfully invert both monostatic and (for the first time) bistatic HF radar data and with this success, it becomes a viable option for obtaining ocean surface parameters from radar data