Cramer-Rao Bound for Target Localization for Widely Separated MIMO Radar

In this paper, we derive the Cramer-Rao Bounds (CRBs) for the 2-dimensional (2D) target localization and velocity estimations for widely separated Multiple-Input Multiple-Output (MIMO) radar. The transmitters emit signals with different frequencies and the receivers receive these signals with amplitude fluctuations and with Doppler shifts due to the target motion. The received signal model is constructed using the Swerling target fluctuations to take into account the undesired effects of target amplitude and phase fluctuations. Moreover, the time delays and the Doppler frequencies are included in the signal model to get a more realistic model. Then, the Cramer-Rao Bounds are derived for the proposed signal model for the target position and velocity estimations. Contrary to known models of CRBs, we derived the CRBs jointly and using the Swerling target fluctuations

Power Optimization for Network Localization

Reliable and accurate localization of mobile objects is essential for many applications in wireless networks. In range-based localization, the position of the object can be inferred using the distance measurements from wireless signals exchanged with active objects or reflected by passive ones. Power allocation for ranging signals is important since it affects not only network lifetime and throughput but also localization accuracy. In this paper, we establish a unifying optimization framework for power allocation in both active and passive localization networks. In particular, we first determine the functional properties of the localization accuracy metric, which enable us to transform the power allocation problems into second-order cone programs (SOCPs). We then propose the robust counterparts of the problems in the presence of parameter uncertainty and develop asymptotically optimal and efficient near-optimal SOCP-based algorithms. Our simulation results validate the efficiency and robustness of the proposed algorithms.Comment: 15 pages, 7 figure

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Passive MIMO Radar Detection

Passive multiple-input multiple-output (MIMO) radar is a sensor network comprised of multiple distributed receivers that detects and localizes targets using the emissions from multiple non-cooperative radio frequency transmitters. This dissertation advances the theory of centralized passive MIMO radar (PMR) detection by proposing two novel generalized likelihood ratio test (GLRT) detectors. The first addresses detection in PMR networks without direct-path signals. The second addresses detection in PMR networks with direct-path signals. The probability distributions of both test statistics are investigated using recent results from random matrix theory. Equivalence is established between PMR networks without direct-path signals and passive source localization (PSL) networks. Comparison of both detectors with a centralized GLRT for active MIMO radar (AMR) detection reveals that PMR may be interpreted as the link between AMR and PSL sensor networks. In particular, under high direct-path-to-noise ratio (DNR) conditions, PMR sensitivity and ambiguity approaches that of AMR. Under low-DNR conditions, PMR sensitivity and ambiguity approaches that of PSL. At intermediate DNRs, PMR sensitivity and ambiguity smoothly varies between that of AMR and PSL. In this way, PMR unifies PSL and AMR within a common theoretical framework. This result provides insight into the fundamental natures of active and passive distributed sensing

Robust Inference in Wireless Sensor Networks

This dissertation presents a systematic approach to obtain robust statistical inference schemes in unreliable networks. Statistical inference offers mechanisms for deducing the statistical properties of unknown parameters from the data. In Wireless Sensor Networks (WSNs), sensor outputs are transmitted across a wireless communication network to the fusion center (FC) for final decision-making. The sensor data are not always reliable. Some factors may cause anomaly in network operations, such as malfunction, corruption, or compromised due to some unknown source of contamination or adversarial attacks. Two standard component failure models are adopted in this study to describe the system vulnerability: the probabilistic and static models. In probabilistic models, we consider a widely known ε−contamination model, where each node has ε probability of malfunctioning or being compromised. In contrast, the static model assumes there is up to a certain number of malfunctioning nodes. It is assumed that the decision center/network operator is aware of the presence of anomaly nodes and can adjust the operation rule to counter the impact of the anomaly. The anomaly node is assumed to know that the network operator is taking some defensive actions to improve its performance. Considering both the decision center (network operator) and compromised (anomalous) nodes and their possible actions, the problem is formulated as a two-player zero-sum game. Under this setting, we attempt to discover the worst possible failure models and best possible operating strategies. First, the effect of sensor unreliability on detection performance is investigated, and robust detection schemes are proposed. The aim is to design robust detectors when some observation nodes malfunction. The detection problem is relatively well known under the probabilistic model in simple binary hypotheses testing with known saddle-point solutions. The detection problem is investigated under the mini-max framework for the static settings as no such saddle point solutions are shown to exist under these settings. In the robust estimation, results in estimation theory are presented to measure system robustness and performance. The estimation theory covers probabilistic and static component failure models. Besides the standard approaches of robust estimation under the frequentist settings where the interesting parameters are fixed but unknown, the estimation problem under the Bayes settings is considered where the prior probability distribution is known. After first establishing the general framework, comprehensive results on the particular case of a single node network are presented under the probabilistic settings. Based on the insights from the single node network, we investigate the robust estimation problem for the general network for both failure models. A few robust localization methods are presented as an extension of robust estimation theory at the end