In this paper, we study the performance of the multipath-assisted multitarget tracking using multiframe assignment
for initiating and tracking multiple targets by employing one or more transmitters and receivers. The basis
of the technique is to use the posterior Cramer-Rao lower bound (PCRLB) to quantify the optimal achievable
accuracy of target state estimation. When resolved multipath signals are present at the sensors, if proper measures
are not taken, multiple tracks will be formed for a single target. In typical radar systems, these spurious
tracks are removed from tracking, and therefore the information carried in such target return tracks are wasted.
In multipath environment, in every scan the number of sensor measurements from a target is equal to the number
of resolved signals received by different propagation modes. The data association becomes more complex as this
is in contrary to the standard data association problem whereas the total number of sensor measurements from
a target is equal to at most one. This leads to a challenging problem of fusing the direct and multipath measurements
from the same target. We showed in our evaluations that incorporating multipath information improves
the performance of the algorithm significantly in terms of estimation error. Simulation results are presented to
show the effectiveness of the proposed method.
In this paper, the previous work multipath-assisted multitarget tracking using multiframe assignment is extended
to the case where there are uncertainties in multipath reflection points at the receiver. An algorithm is proposed
for initiating and tracking multiple targets using multiple transmitters and receivers. This algorithm is capable of
exploiting multipath target returns from distinct and unknown propagation modes. When multipath returns are
not utilized appropriately within the tracker, (e.g., discarded as clutter or incorporated with incorrect propagation
mode assumption) the potential information in the multipath returns is lost. In real scenarios, it is more
appropriate to assume that the locations of the reflection points/surfaces are not accurately known.
Integrating multipath information into the tracker by correctly identifying the multipath mode and identifying
the reflection point can help improve the accuracy of tracking. The challenge in improving tracking results using
multipath measurements is the fusion of direct and multipath measurements from the common target when the
multipath-reflection mode is unknown. The problem becomes even more challenging with false alarms and missed
detections. We propose an algorithm to track the target with uncertainty in multipath reflection points/surface
using the multiframe assignment technique. Simulation results are presented to show the effectiveness of the
proposed algorithm on a ground target tracking problem.
In this paper an algorithm for multipath-assisted multitarget tracking using multiframe assignment is proposed
for initiating and tracking multiple targets using one or more transmitters and receivers. This algorithm is capable
of exploiting multipath target returns from distinct propagation modes that are resolvable by the receiver. When
resolved multipath returns are not utilized within the tracker, i.e., discarded as clutter, potential information
conveyed by the multipath detections of the same target is wasted. In this case, spurious tracks are formed using
target-originated multipath measurements, but with an incorrect propagation mode assumption. Integrating
multipath information into the tracker (and not discarding it) can help improve the accuracy of tracking and
reduce the number of false tracks. The challenge in improving tracking results using multipath measurements
is the fusion of direct and multipath measurements from the common target. The problem will be considered
in an environment with false alarms and missed detections. We propose a multiframe assignment technique to
incorporate multipath information. The simulation results are presented to show the effectiveness of the proposed
algorithm with an example of tracking ground targets.
A passive coherent location (PCL) system exploits the ambient FM radio or television signals from powerful
local transmitters, which makes it ideal for covert tracking. In a passive radar system, also known as PCL
system, a variety of measurements can be used to estimate target states such as direction of arrival (DOA), time
difference of arrival (TDOA) or Doppler shift. Noise and the precision of DOA estimation are main issues in
a PCL system and methods such as conventional beam forming (CBF) algorithm, algebraic constant modulus
algorithm (ACMA) are widely analyzed in literature to address them. In practical systems, although it is
necessary to reduce the directional ambiguities, the placement of receivers closed to each other results in larger
bias in the estimation of DOA of signals, especially when the targets move off bore-sight. This phenomenon leads
to degradation in the performance of the tracking algorithm. In this paper, we present a method for removing
the bias in DOA to alleviate the aforementioned problem. The simulation results are presented to show the
effectiveness of the proposed algorithm with an example of tracking airborne targets.
Passive coherent location (PCL), which uses the commercial signals as illuminators of opportunity, is an emerging
technology in air defense systems. The advantages of PCL are low cost, low vulnerability to electronic counter
measures, early detection of stealthy targets and low-altitude detection. However, limitations of PCL include lack
of control over illuminators, poor bearing accuracy, time-varying sensor parameters and limited observability.
In this paper, multiple target tracking using PCL with high bearing error is considered. In this case, the
challenge is to handle high nonlinearity due to high measurement error. In this paper, we implement the
converted measurement Kalman filter, unscented Kalman filter and particle filter based PHD filter for PCL
radar measurements and compare their performances.