Aerospace and Electronic Systems Magazine July 2017 Tutorial XI - 64

IEEE AES Tutorials
research area are identified. These issues include not only improving efficiency, but also increased concerns regarding the cost and
complexity of power supplying systems.

pending on the estimation results in order to make the estimation robust to target maneuvers as well as target appearance or
disappearance.

FUNDAMENTALS OF ENERGY-CONSTRAINED SENSOR
NETWORK SYSTEMS

NONLINEAR FILTERS: BEYOND THE KALMAN FILTER
FRED DAUM, Member, IEEE
Raytheon

BRIAN M. SADLER, Senior Member, IEEE
U.S. Army Research Laboratory
This article is an overview of energy-constrained sensor networks, focusing on energy-conserving communications and
signal processing strategies. We assume battery-driven nodes,
employing robust communications, with little or no fixed infrastructure. Our discussion includes architectures, communications connectivity, capacity and scalability, mobility, network
localization and synchronization, distributed signal processing,
and cross-layer issues. Because energy is a precious system resource, all aspects of the network must be designed with energy
savings in mind. In particular, transmissions and idle listening
must be minimized, which implies the use of duty cycling to the
maximum extent possible. When external assets are available,
for tasks such as network synchronization and node geolocation,
these can greatly relieve the energy burden and significantly enhance network lifetime.

PROBABILISTIC DATA ASSOCIATION TECHNIQUES FOR
TARGET TRACKING WITH APPLICATIONS TO SONAR, RADAR
AND EO SENSORS
Y. BAR-SHALOM, Fellow, IEEE
University of Connecticut
T. KIRUBARAJAN, Member, IEEE
McMaster University
Canada
X. LIN
University of Connecticut
We present an overview of the probabilistic data association
(PDA) technique and its application for different target tracking scenarios, in particular for low observable (LO) (low SNR)
targets. A summary of the PDA technique is presented. The use
of the PDA technique for tracking low observable targets with
passive sonar measurements is presented. This "target motion
analysis" is an application of the PDA technique, in conjunction with the maximum likelihood (ML) approach, for target
motion parameter estimation via a batch procedure. The use
of the PDA technique for tracking highly maneuvering targets
combined radar resource management is described. This illustrates the application of the PDA technique for recursive state
estimation using the interacting multiple model (IMM) estimator with probabilistic data association filter (PDAF) (IMMPDAF). Then we present a flexible (expanding and contracting)
sliding-window parameter estimator using the PDA approach
for tracking the state of a maneuvering target using measurements from an electro-optical (EO) sensor. This, while still a
batch procedure, has the flexibility of varying the batches de64

Nonlinear filters can provide estimation accuracy that is vastly
superior to extended Kalman filters for some important practical applications. We compare several types of nonlinear filters,
including: particle filters (PFs), unscented Kalman filters, extended Kalman filters, batch filters and exact recursive filters.
The key practical issue in nonlinear filtering is computational
complexity, which is often called "the curse of dimensionality."
It has been asserted that PFs avoid the curse of dimensionality,
designed PFs with good proposal densities sometimes avoid the
curse of dimensionality, but not otherwise. Future research in
nonlinear filtering will exploit recent progress in quasi-Monte
Carlo algorithms (rather than boring old Monte Carlo methods),
as well as ideas borrowed from physics (e.g., dimensional interpolation) and new mesh-free adjoint methods for solving PDEs.
This tutorial was written for normal engineers, who do not have
nonlinear filters for breakfast.

MULTIBASELINE CROSS-TRACK SAR INTERFEROMETRY:
A SIGNAL PROCESSING PERSPECTIVE
FULVIO GINI, Senior Member, IEEE
FABRIZIO LOMBARDINI, Senior Member, IEEE
University of Pisa
Italy
Synthetic aperture radar interferometry (InSAR) is a powerful
and increasingly expanding technique for measuringthe topography of a surface, its changes over both short- and long-time scale,
and other changes in the detailed characteristicsof the surface.
We provide a tutorial description of recent results of the research
activity at the University of Pisa on multibaseline (MB) InSAR
processing. The main focus is on the problem of retrieving both
heights and radar reflectivities of natural layover areas by means
of a cross-track InSAR (XTI-SAR) system with a uniform linear
array (ULA). It is formulated as the problem of detecting and
estimating a multi-component signal corrupted by multiplicative
noise-the speckle in the radar imaging jargon-and by additive
white Gaussian noise. Application to the InSAR problem of both
nonparametric and parametric modern spectral estimation techniques is described. The problem of estimating the number of
signal components in the presence of speckle is also addressed.
Finally, a brief mention is given to recent research trends on
robust methods for nonperfectly calibrated arrays, on processing for non-ULA configurations, and on MB SAR tomography,
which is an extension of MB SAR interferometry for the full 3D
mapping of semitransparent volume scattering layers. State of the
art of other advanced multichannel interferometric techniques is
also briefly recalled.

IEEE A&E SYSTEMS MAGAZINE

JULY 2017, Part II of II

Table of Contents for the Digital Edition of Aerospace and Electronic Systems Magazine July 2017 Tutorial XI