17 CHAPTER 2 LITERATURE SURVEY 2.1 INTRODUCTION While tracking a target in realistic physical environments, the sensor information related to the target is being updated with incorrect data computed due to thermal noise, false alarms, clutter, occlusions and shadows. Consequently, tracking performance degrades and the resulting tracking errors are often far worse than those predicted by the tracking filter’s error covariance matrix. The proposed research work provides solutions for efficient tracking of targets in a radar sensor network, wireless sensor network and camera sensor network. This chapter gives the overview of existing techniques used for target tracking in various sensor networks. The taxonomy of single and multiple target tracking techniques are presented in this chapter. The various requirements and challenges in the design of the target tracking algorithms are also discussed. Section 2.2 provides tracking of targets under radar sensor network and section 2.3 explains about tracking of targets in Wireless Sensor Network. Tracking of targets in camera sensor network is explained in section 2.4.
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CHAPTER 2
LITERATURE SURVEY
2.1 INTRODUCTION
While tracking a target in realistic physical environments, the
sensor information related to the target is being updated with incorrect data
computed due to thermal noise, false alarms, clutter, occlusions and shadows.
Consequently, tracking performance degrades and the resulting tracking
errors are often far worse than those predicted by the tracking filter’s error
covariance matrix. The proposed research work provides solutions for
efficient tracking of targets in a radar sensor network, wireless sensor network
and camera sensor network. This chapter gives the overview of existing
techniques used for target tracking in various sensor networks. The taxonomy
of single and multiple target tracking techniques are presented in this chapter.
The various requirements and challenges in the design of the target tracking
algorithms are also discussed. Section 2.2 provides tracking of targets under
radar sensor network and section 2.3 explains about tracking of targets in
Wireless Sensor Network. Tracking of targets in camera sensor network is
explained in section 2.4.
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2.2 TRACKING OF TARGETS UNDER RADAR SENSOR
NETWORK
Multiple Targets Tracking (MTT) is an important topic under radar
surveillance, since many applications such as remote sensing observing
system, ground based target recognition, detection and tracking, detecting
speed of the vehicle & highway safety, target tracking in ATC, aircraft safety,
electronic warfare, ship safety and navigation are based upon it.
2.2.1 Existing Algorithms
The data association is the basic problem of MTT. Various methods
for multiple targets tracking have been analyzed in the literature are described
below.
The Figure 2.1 shows classification of literature survey of target
tracking in radar sensor network.
The existing literature survey available for target tracking in radar
sensor network can be mainly classified as maneuvering target and non
maneuvering target for single target and multiple targets. Various
methodologies such as data association, position estimation and classification
techniques are available for tracking multiple targets. This thesis mainly
focuses on data association and position estimation.
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In MTT, the data (location information represented by spherical
coordinates) produced by the same source is identified and partitioned into
sets of tracks. Also MTT finds a number of targets and parameters such as
position, velocity and acceleration for each track (Blackman 1986). Li and
Jilkov (2000) presented the methodology of new targets identification, new
plot creation and existing track updation for each scan. Observations that are
not assigned to existing tracks are used to form new tentative tracks. Once a
tentative track is formed from the observations, it is updated by successive
scans. The gate size and time duration allowed for confirming observation can
be chosen as functions of the confidence in the validity of the original
observation. A track which is not updated by successive scans has to be
deleted.
Bar-Shalom and Fortmann (1988) explained that when tracking is
performed in an environment that contains clutter and/or more than one
object, the measurements need to be associated with the correct tracks. Not all
measurements convey information about the tracked object and the
measurements that are not informative about the tracked object are called
clutter. Determining which measurements are informative and which are not,
is usually referred to as data association. As a result of this process, data
association is able to produce a set of tracks for a target.
Multiple targets tracking with radar applications (Blackman 1986,
Blackman et al 1993) described multiple target tracking and data association
of the sensor data for individual targets. When multiple number of
observations are received by the tracking system, it is necessary to assign
each incoming observation report to a specific target track. The popular
mechanism for classifying reports was the “nearest-neighbor rule” (Liggins
et al 2009). The idea of the rule is to estimate each target position at the time
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of a new position report, and then assign that report to the target nearest to
such estimate.
Bar-Shalom and Tse (1975) have proposed an all-neighbor PDA
approach to correlate sensor data under the assumption of a single target. The
PDA method is based on computing the posterior probability of each
candidate measurement found in a validation gate, assuming that only one real
target is present and all other measurements are Poisson-distributed clutter.
The PDA and its extension JPDA (Blackman 1986) are used for tracking
single and multiple targets respectively. In JPDA method, joint posterior
probabilities are computed for multiple targets in a Poisson clutter.
However, these methods are computationally heavy and have no
explicit provision for track initiation. Although many association (Smith and
Sameer 2006) and tracking algorithms (Liggins et al 2009) have been
suggested, it is still difficult to generate and maintain tracks in practice
(Musicki 2007).
Fortmann et al (1983) proposed a new JPDA algorithm for multiple
targets in clutter. This was a target oriented approach, in the sense that a set of
established targets is used to form gates in the measurement space and to
compute posterior probabilities.
Roecker et al (1995) proposed a multiple scan or n-back scan JPDA
algorithm which addresses itself to the problem of measurement to track data
association in a multiple target and clutter environment and uses multiple
scans of measurements along with the present target information to produce
better weights for data association.
In MTT, there are number of methods for classifying the observed
data into tracks. MHT uses track splitting technique for accurate decision
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making from the observed data (Musicki and Suvorova 2008). Under this
MHT scheme, the tracking system does not have to commit immediately or
irrevocably to a single assignment of each report. If a report is highly
correlated with more than one track, an updated copy of each track can be
created; subsequent reports can be used to determine which assignment is
correct. As more reports come in, the track associated with the correct
assignment will rapidly converge on the true target trajectory, whereas the
falsely updated tracks are less likely to be correlated with subsequent reports.
The n-backscan MHT approach requires information collected from
‘n’ number of previous scans for making a decision. Hence it needs more
memory for maintaining numerous track hypotheses (Feo et al 1997). The
main drawback of n-backscan MHT is the exponential increase in
computation complexity and memory requirement. Bar-Shalom et al (2007)
discussed several theoretical issues relating to the score function for the
measurement-to-track association/assignment decision in the track oriented
version of the MHT. The score function is the ratio of the Probability Density
Function (PDF) of a measurement having originated from a track to the PDF
of the measurement having a different origin and is called as likelihood ratio.
When the system is linear with additive Gaussian noise, there exists
an analytical solution to the Bayesian time and measurement update equations.
The solution is given by the KF. Many books describing different aspects of
the KF exist (Simon 2001). Since the system is linear and Gaussian, the
update formula will remain Gaussian, and hence all Gaussian systems can be
described by their first two moments (mean and covariance). The update
equations consist of mean and covariance update. The original KF (Kalman
1960, Kalman 1961) defined in continuous-time, but soon a discrete version
was also derived. Much of the classical theory is described in Anderson and
Moore (1979). For the discretized-linearization, (Gustafsson 2000), the non-
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linear continuous-time system was linearized and then the system was
discretized. Anderson and Moore (1979) and Bar-Shalom and Li (1993) have
discussed the EKF for the discrete-time.
Farina et al (2002) have compared the estimation performance
(error mean and standard deviation; consistency test) of nonlinear filters like
the Extended Kalman Filter (EKF), the. statistical linearization, the particle
filtering, and the Unscented Kalman filter (UKF).
Singer et al (1974) proposed a new optimal filter for target tracking
in dense multitarget environment. The sensitivity of tracking accuracy to data
rate, maneuver magnitude, maneuver correlation coefficient, and single-look
measurement accuracy of KF is discussed
The problems and issues involved in Multitarget Ocean tracking
using a heterogeneous set of passive acoustic measurements are outlined by
Fortmann and Baron (1979). They have also described an approach to solve
data association and maneuver detection problems. Their method uses an EKF
with both geographic and acoustic states, and handles measurement vectors
such as bearing/frequency and delay/Doppler difference.
To resolve the problem of track-to-track association in a distributed
multisensor situation, He and Zhang (2006) presented independent and
dependent sequential track correlation algorithms based on those of Singer
(1970) and Bar-Shalom (1981). In this paper, based on sequential track
correlation algorithm, the restricted and attenuation memory track correlation
algorithms and sequential classic assignment rules are explained. The
correlation performances of the sequential algorithms are much better than
those of Singer (1970) and Bar-Shalom (1981) with a little more computation
and memory burden under the environments of dense targets, interfering noise
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and track cross. The computational complexity of these algorithm increases
with increasing environmental parameter under consideration. Also,
performance of these algorithms reduces with increased number of targets.
Keuk (1998) derived an optimal combinational method which can
be used under different operating conditions. The method related to MHT
uses a sequential likelihood ratio test and derives benefit from processing
signal strength information. Multiscan data association can significantly
enhance tracking performance (Battistelli et al 2011) in critical radar