The accuracy of radar tracks depends strongly on
the variances of the measurements, and those variances are
inversely proportional to the signal-to-noise (SNR) produced
the hardware and signal processor. The signal processor uses
matched filter processing, and the efficiency of that depends on
knowledge of the kinematics of the target. In particular, the
matched filter performance depends heavily on range rate and
range acceleration. Traditionally, the predicted state of the target
from the track filter is used for matched filter processing, but
the predicted kinematic state can have rather large errors, and
those errors result in match filter loss. This loss can be very
large for maneuvering (i.e., accelerating) targets. In this paper, an
expected-maximization (EM) approach is taken to jointly address
signal processing and tracking. The signal processor maximizes
the SNR using the predicted state and produces measurements.
The state estimator ( e.g., Kalman filter) uses those measurements
to produce expected values of the kinematic state (i.e. the nuisance
parameters). The signal processor then maximizes the SNR
using the new state estimates. This process continues until the
maximum likelihood values of the measurements are achieved.
In this paper, the Interacting Multiple Model (IMM) estimator
is introduced for the tracking function better address sudden
maneuvers. The EM-Based approach to join signal processing
and tracking are presented along with a discussion of the real-time
computing. Monte Carlo simulation results are given to
illustrate a 6 dB improvement in SNR and enhanced tracks for
a maneuvering target.
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Relative target estimation using a cascade of extended Kalman filters
This paper presents a method of tracking multiple ground targets from an unmanned aerial vehicle (UAV) in a 3D reference
frame. The tracking method uses a monocular camera and makes no assumptions on the shape of the terrain or the target
motion. The UAV runs two cascaded estimators. The first is an Extended Kalman Filter (EKF), which is responsible for
tracking the UAV’s state, such as position and velocity relative to a fixed frame. The second estimator is an EKF that is
responsible for estimating a fixed number of landmarks within the camera’s field of view. Landmarks are parameterized by a
quaternion associated with bearing from the camera’s optical axis and an inverse distance parameter. The bearing quaternion
allows for a minimal representation of each landmark’s direction and distance, a filter with no singularities, and a fast update
rate due to few trigonometric functions. Three methods for estimating the ground target positions are demonstrated: the first
uses the landmark estimator directly on the targets, the second computes the target depth with a weighted average of converged
landmark depths, and the third extends the target’s measured bearing vector to intersect a ground plane approximated from the
landmark estimates. Simulation results show that the third target estimation method yields the most accurate results.
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- Award ID(s):
- 1650547
- NSF-PAR ID:
- 10053365
- Date Published:
- Journal Name:
- Proceedings of the International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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