A comparison of processing approaches for distributed radar sensing

Radar networks received increasing attention in recent years as they can outperform single monostatic or bistatic systems. Further attention is being dedicated to these systems as an application of the MIMO concept, well know in communications for increasing the capacity of the channel and improving the overall quality of the connection. However, it is here shown that radar network can take advantage not only from the angular diversity in observing the target, but also from a variety of ways of processing the received signals. The number of devices comprising the network has also been taken into the analysis. Detection and false alarm are evaluated in noise only and clutter from a theoretical and simulated point of view. Particular attention is dedicated to the statistics behind the processing. Experiments have been performed to evaluate practical applications of the proposed processing approaches and to validate assumptions made in the theoretical analysis. In particular, the radar network used for gathering real data is made up of two transmitters and three receivers. More than two transmitters are well known to generate mutual interference and therefore require additional e�fforts to mitigate the system self-interference. However, this allowed studying aspects of multistatic clutter, such as correlation, which represent a first and novel insight in this topic. Moreover, two approaches for localizing targets have been developed. Whilst the first is a graphic approach, the second is hybrid numerical (partially decentralized, partially centralized) which is clearly shown to improve dramatically the single radar accuracy. Finally the e�ects of exchanging angular with frequency diversity are shown as well in some particular cases. This led to develop the Frequency MIMO and the Frequency Diverse Array, according to the separation of two consecutive frequencies. The latter is a brand new topic in technical literature, which is attracting the interest of the technical community because of its potential to generate range-dependant patterns. Both the latter systems can be used in radar-designing to improve the agility and the effciency of the radar

MIMO Radar Waveform Design and Sparse Reconstruction for Extended Target Detection in Clutter

This dissertation explores the detection and false alarm rate performance of a novel transmit-waveform and receiver filter design algorithm as part of a larger Compressed Sensing (CS) based Multiple Input Multiple Output (MIMO) bistatic radar system amidst clutter. Transmit-waveforms and receiver filters were jointly designed using an algorithm that minimizes the mutual coherence of the combined transmit-waveform, target frequency response, and receiver filter matrix product as a design criterion. This work considered the Probability of Detection (P D) and Probability of False Alarm (P FA) curves relative to a detection threshold, τ th, Receiver Operating Characteristic (ROC), reconstruction error and mutual coherence measures for performance characterization of the design algorithm to detect both known and fluctuating targets and amidst realistic clutter and noise. Furthermore, this work paired the joint waveform-receiver filter design algorithm with multiple sparse reconstruction algorithms, including: Regularized Orthogonal Matching Pursuit (ROMP), Compressive Sampling Matching Pursuit (CoSaMP) and Complex Approximate Message Passing (CAMP) algorithms. It was found that the transmit-waveform and receiver filter design algorithm significantly outperforms statically designed, benchmark waveforms for the detection of both known and fluctuating extended targets across all tested sparse reconstruction algorithms. In particular, CoSaMP was specified to minimize the maximum allowable P FA of the CS radar system as compared to the baseline ROMP sparse reconstruction algorithm of previous work. However, while the designed waveforms do provide performance gains and CoSaMP affords a reduced peak false alarm rate as compared to the previous work, fluctuating target impulse responses and clutter severely hampered CS radar performance when either of these sparse reconstruction techniques were implemented. To improve detection rate and, by extension, ROC performance of the CS radar system under non-ideal conditions, this work implemented the CAMP sparse reconstruction algorithm in the CS radar system. It was found that detection rates vastly improve with the implementation of CAMP, especially in the case of fluctuating target impulse responses amidst clutter or at low receive signal to noise ratios (β n). Furthermore, where previous work considered a τ th=0, the implementation of a variable τ th in this work offered novel trade off between P D and P FA in radar design to the CS radar system. In the simulated radar scene it was found that τ th could be moderately increased retaining the same or similar P D while drastically improving P FA. This suggests that the selection and specification of the sparse reconstruction algorithm and corresponding τ th for this radar system is not trivial. Rather, a tradeoff was noted between P D and P FA based on the choice and parameters of the sparse reconstruction technique and detection threshold, highlighting an engineering trade-space in CS radar system design. Thus, in CS radar system design, the radar designer must carefully choose and specify the sparse reconstruction technique and appropriate detection threshold in addition to transmit-waveforms, receiver filters and building the dictionary of target impulse responses for detection in the radar scene

Cloud-aided wireless systems: communications and radar applications

This dissertation focuses on cloud-assisted radio technologies for communication, including mobile cloud computing and Cloud Radio Access Network (C-RAN), and for radar systems. This dissertation first concentrates on cloud-aided communications. Mobile cloud computing, which allows mobile users to run computationally heavy applications on battery limited devices, such as cell phones, is considered initially. Mobile cloud computing enables the offloading of computation-intensive applications from a mobile device to a cloud processor via a wireless interface. The interplay between offloading decisions at the application layer and physical-layer parameters, which determine the energy and latency associated with the mobile-cloud communication, motivates the inter-layer optimization of fine-grained task offloading across both layers. This problem is modeled by using application call graphs, and the joint optimization of application-layer and physical-layer parameters is carried out via a message passing algorithm by minimizing the total energy expenditure of the mobile user. The concept of cloud radio is also being considered for the development of two cellular architectures known as Distributed RAN (D-RAN) and C-RAN, whereby the baseband processing of base stations is carried out in a remote Baseband Processing Unit (BBU). These architectures can reduce the capital and operating expenses of dense deployments at the cost of increasing the communication latency. The effect of this latency, which is due to the fronthaul transmission between the Remote Radio Head (RRH) and the BBU, is then studied for implementation of Hybrid Automatic Repeat Request (HARQ) protocols. Specifically, two novel solutions are proposed, which are based on the control-data separation architecture. The trade-offs involving resources such as the number of transmitting and receiving antennas, transmission power and the blocklength of the transmitted codeword, and the performance of the proposed solutions is investigated in analysis and numerical results. The detection of a target in radar systems requires processing of the signal that is received by the sensors. Similar to cloud radio access networks in communications, this processing of the signals can be carried out in a remote Fusion Center (FC) that is connected to all sensors via limited-capacity fronthaul links. The last part of this dissertation is dedicated to exploring the application of cloud radio to radar systems. In particular, the problem of maximizing the detection performance at the FC jointly over the code vector used by the transmitting antenna and over the statistics of the noise introduced by quantization at the sensors for fronthaul transmission is investigated by adopting the information-theoretic criterion of the Bhattacharyya distance and information-theoretic bounds on the quantization rate

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