使用距离率测量值的自适应转弯率估计
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Adaptive Turn Rate Estimation using Range Rate
Measurements
Thecoordinatedturn(CT)modelisoftenusedtotrack
maneuvering target which performs CT motion. The key point
to the successful use of this model is to determine the turn rate
parameter. A new method to estimate the turn rate by using
radar range-rate measurements is presented. First, four possible
turn rates can be achieved with the range-rate measurements.
Second, the minimum turn rate and its opposite value are chosen
to be the possible turn rates. Finally, an interacting multiple
model (IMM) algorithm with one constant velocity (CV) model
and two CT models is designed to track the maneuvering target
which performs uniform and ct motions. Monte-Carlo simulation
results show that this algorithm is not only better than equivalent
noise approach in tracking performance, but also better than the
conventional IMM algorithm with the adaptive turn rate model.
Further simulations show that the algorithm is robust with the
accuracy of the range-rate measurements.
I. INTRODUCTION
Target motion uncertainty is one of the most
important challenges in target tracking. To solve this
problem, various adaptive estimation algorithms such
as equivalent noise, input detection and estimation,
and switching model approaches, have been presented
[1].
The equivalent noise approach intends to cover the
targ et maneuver by adjusting the process noise level
[2]. Under uniform motion, the filter operates with a
low-level noise, but once target maneuver is detected,
a high-level process noise will be used.
The input detection and estimation approach [3]
assumes that the target maneuver can be modeled
by an unknown control input. First, the input is
estimated; then by using this estimated input, the
target state can be estimated a s the conventional
recursive Bayesian estimator.
The switching model approach [4] is usually
composed of two models, one is a nonmaneuver
model and the other is a maneuver model. The
Manuscript received September 7, 2005; revised March 22, 2006;
released for publication September 5, 2006.
IEEE Log No. T-AES/42/4/890195.
Refereeing of this contribution was handled by P. K. Willet t.
This work was supported by the National Key Fundamental
Research & Development Programs (973) of China, Number
2001CB309403. This work was also supported by the National
Natural Science Foundation of China under Grant 60602026.
0018-9251/06/$17.00
c
° 2006 IEEE
nonmaneuver model is used in uniform motion
fashion and when maneuver is detected, the model
used in the filter is switched to the maneuver model.
The multiple model approach [5] is considered
the mainstream in maneuvering target tracking, which
assumes that the target’s dynamic model obeys one of
a finite number of models. A bank of filters, each of
which uses only one of the models in the model set,
are used to estimate the target state in parallel and the
final state estimate is the combination of each filter’s
output. In the class of multiple model approaches, the
interacting multiple model (IMM) algorithm is the
most effective one.
A civil or military aircraft often performs
coordinated turn (CT) motion. To solve the problem
of tracking a target with such kind of maneuver,
a maneuver detection method was presented in
[6] based on the range-rate measurements, which
was named the c
min
method therein. Range rate,
also called Doppler, is the radial velocity along the
radar range direction. It was shown in [6] that the
centripetal acceleration could be estimated by using
the range-rate measurements. Once the centripetal
acceleration exceeds a threshold, a maneuver is
declared and the process noise level is increased to
a high level. This is by nature an equivalent noise
approach which intends to account for the target
maneuver by a high-level process noise. Whether
the target maneuver occurs or not, a constant velocity
(CV) model is u sed in this algorithm. This algorithm
will face the model mismatching problem when the
CT motion is involved. The CT model is often used
to track a target with CT motion. An IMM algorithm
consisting of one CV model and several CT models
can be used to cover a wide range of maneuvers. But
how to determine the turn rate parameter remains
the major challenge in case CT models are used.
There are three kinds of methods which can be used
to solve this problem. The first class [7] assumes
that the turn rate is known, but unfortunately this
assumption is unrealistic and the tracking performance
will deteriorate when the assumed turn rate is far
away from the true one. The second class [8] tries
to augment the turn rate parameter into the state
vector and estimate the turn rate as part of the state
vector recursively. This is really a difficult nonlinear
problem. The third class [9] estimates the turn rate
online by using the estimated acceleration magnitude
over the estimated speed. Generally speaking, the
acceleration estimates are not accurate enough and
thus lead to inaccurate turn rate estimate.
Through a modification to the c
min
method, a new
method to estimate the turn rate based on the range
rate measurements is presented here, then an IMM
algorithm consisting of one CV model and two CT
models is used to track the maneuvering target.
The rest of the paper is organized as follows. In
Section II, how to estimate the turn rate parameter
1532 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 42, NO. 4 OCTOBER 2006
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