Adaptive MIMO Radar for Target Detection, Estimation, and Tracking

We develop and analyze signal processing algorithms to detect, estimate, and track targets using multiple-input multiple-output: MIMO) radar systems. MIMO radar systems have attracted much attention in the recent past due to the additional degrees of freedom they offer. They are commonly used in two different antenna configurations: widely-separated: distributed) and colocated. Distributed MIMO radar exploits spatial diversity by utilizing multiple uncorrelated looks at the target. Colocated MIMO radar systems offer performance improvement by exploiting waveform diversity. Each antenna has the freedom to transmit a waveform that is different from the waveforms of the other transmitters. First, we propose a radar system that combines the advantages of distributed MIMO radar and fully polarimetric radar. We develop the signal model for this system and analyze the performance of the optimal Neyman-Pearson detector by obtaining approximate expressions for the probabilities of detection and false alarm. Using these expressions, we adaptively design the transmit waveform polarizations that optimize the target detection performance. Conventional radar design approaches do not consider the goal of the target itself, which always tries to reduce its detectability. We propose to incorporate this knowledge about the goal of the target while solving the polarimetric MIMO radar design problem by formulating it as a game between the target and the radar design engineer. Unlike conventional methods, this game-theoretic design does not require target parameter estimation from large amounts of training data. Our approach is generic and can be applied to other radar design problems also. Next, we propose a distributed MIMO radar system that employs monopulse processing, and develop an algorithm for tracking a moving target using this system. We electronically generate two beams at each receiver and use them for computing the local estimates. Later, we efficiently combine the information present in these local estimates, using the instantaneous signal energies at each receiver to keep track of the target. Finally, we develop multiple-target estimation algorithms for both distributed and colocated MIMO radar by exploiting the inherent sparsity on the delay-Doppler plane. We propose a new performance metric that naturally fits into this multiple target scenario and develop an adaptive optimal energy allocation mechanism. We employ compressive sensing to perform accurate estimation from far fewer samples than the Nyquist rate. For colocated MIMO radar, we transmit frequency-hopping codes to exploit the frequency diversity. We derive an analytical expression for the block coherence measure of the dictionary matrix and design an optimal code matrix using this expression. Additionally, we also transmit ultra wideband noise waveforms that improve the system resolution and provide a low probability of intercept: LPI)

Information limits in remote sensing and target tracking

In this research we focus on information limits in the remote sensing and target tracking field. Three topics are studied: a dynamic Cramér-Rao bound for target tracking in clutter, target intervisibility, and monopulse radar detection and localization of multiple unresolved targets via joint bin processing. ^ We develop a Cramér-Rao lower bound (CRLB) for target tracking, that is, for the state estimates of dynamic systems in the presence of false alarms and missed detections. We show that the CRLB obeys a Riccati-like equation, with the exception that the measurement-noise covariance term is multiplied by an information reduction factor (IRF). The calculation of the IRF and the existence of efficient estimators are also addressed. ^ In the second topic we study intervisibility---the existence of an unobstructed line of sight (LOS) between two points---accounting for the vertical and horizontal errors in the estimated locations of both points as well as elevation errors in the database of the terrain that could obstruct the LOS between these points. This is a significant factor in limiting information gathering in real systems. The errors will first be simply treated as a white noise sequence: we assume no correlation between the intervisibility at two different times, and the probability of an instantaneous intervisibility event will be in this case developed. Consequently, we will present a second treatment in which the errors are stochastic processes of a certain bandwidth, and both the probability density function of an intervisibility interval and the average number of intervisibility intervals over a certain time period will be developed. ^ In a monopulse radar system, if several closely-spaced targets fall within the same radar beam and between two adjacent matched filter samples in range, the monopulse information from both of these samples can and should be used for estimation, both of angle and of range (i.e., estimation of the range to sub-bin accuracy). In our research, a model of closely spaced targets that fall between adjacent matched filter samples is established and a maximum likelihood (ML) extractor will be developed. The limits on the number of targets that can be estimated are given. A minimum description length (MDL) criterion is used to decide on the number of targets between the matched filter samples.