Data association is the crucial part of any multitarget tracking algorithm in a scenario with multiple closely
spaced targets, low probability of detection and high false alarm rate. Multiframe assignment, which solves the
data association problem as a constrained optimization, is one of the widely accepted methods to handle the
measurement origin uncertainty. If the targets do not maneuver, then multiframe assignment with one or two
frames will be enough to find the correct data association. However, more frames must be considered in the
data association for maneuvering targets. Also, a target maneuver might be hard to detect when maneuvering
index, which is the function of sampling time, is small. In this paper, we propose an improved multiframe
data association with better cost calculation using backward multiple model recursion, which increases the
maneuvering index. The effectiveness of the proposed algorithm is demonstrated with simulated data.
Passive Coherent Location (PCL) is a low-cost system for tracking of air targets clandestinely using illuminators of
opportunity such as FM broadcast and digital TV. Due to an increased interest in PCL systems, researchers have
been working on different configurations of available sources of opportunity and receivers capable of extracting
plots from reflected signals of opportunity. The configuration can be multiple-transmitter-single-receiver or singletransmitter-
multiple-receiver. Unlike standard radar systems, which can be optimized for detection probability
and/or false alarm rate using different transmitted signals and adaptive thresholding, PCL systems are prone to
poor detection due to low signal-to-noise (SNR). This leads to high clutter with low probability of detection of
target of interest. In this work, we implement a multisensor-multitarget tracking system that fuses measurements
from different PCL systems to improve tracking results. The benefits of the fusion are demonstrated using real
data from NATA SET Panel 108 on PCL as well as simulated data.
In this paper, a new state estimation algorithm for estimating the states of targets that are separable into
linear and nonlinear subsets with non-Gaussian observation noise distributed according to a mixture of Gaussian
functions is proposed. The approach involves modeling the collection of targets and measurements as random
finite sets and applying a new Rao-Blackwellised Approximate Conditional Mean Probability Hypothesis Density
(RB-ACM-PHD) recursion to propagate the posterior density. The RB-ACM-PHD filter jointly estimates the
time-varying number of targets and the observation sets in the presence of data association uncertainty, detection
uncertainty, noise and false alarms. The proposed algorithm approximates a mixture Gaussian distribution with a
moment-matched Gaussian in the weight update phase of the filtering recursion. A two dimensional maneuvering
target tracking example is used to evaluate the merits of the proposed algorithm. The RB-ACM-PHD filter
results in a significant reduction in computation time while maintaining filter accuracies similar to the standard
sequential Monte Carlo PHD implementation.
Passive Coherent Location (PCL) systems use existing commercial signals (e.g., FM broadcast, digital TV) as
the illuminators of opportunity in air defence systems. PCL Sytems have many advantages such as low cost,
covert operation and low vulnerability to electronic counter measures, over conventional radar systems. The main
disadvantage of PCL systems is that the transmitter locations and the transmitted signals cannot be controlled.
Thus, it is possible to have multiple transmitters that transmit the same signal/frequency inside the coverage
region of the receiver. Thus, multiple measurements that originated from different transmitters and reflected
by the same target will be received. Even though using multiple transmitters will facilitate better estimates
of the target states due to spatial diversity, one cannot use these measurements without resolving transmitter
and measurement origin uncertainties. This adds another level of complexity to the standard data association
problem where the uncertainty is only in measurement origins. That is, there are two uncertainties that need to
be resolved in order to track multiple targets. One is the measurement-to-target association and the other is the
measurement-to-transmitter association. In this work, a tracking algorithm is proposed to track multiple targets
using PCL systems with the above data association uncertainties. The efficiency of the proposed algorithm is
demonstrated on realistically simulated data.
The Interacting Multiple Model (IMM) estimator has been proven to be effective in tracking agile targets.
Smoothing or retrodiction, which uses measurements beyond the current estimation time, provides better estimates
of target states. Various methods have been proposed for multiple model smoothing in the literature.
In this paper, a new smoothing method, which involves forward filtering followed by backward smoothing while
maintaining the fundamental spirit of the IMM, is proposed. The forward filtering is performed using the standard
IMM recursion, while the backward smoothing is performed using a novel interacting smoothing recursion.
This backward recursion mimics the IMM estimator in the backward direction, where each mode conditioned
smoother uses standard Kalman smoothing recursion. Resulting algorithm provides improved but delayed estimates
of target states. Simulation studies are performed to demonstrate the improved performance with a
maneuvering target scenario. The comparison with existing methods confirms the improved smoothing accuracy.
This improvement results from avoiding the augmented state vector used by other algorithms. In addition, the
new technique to account for model switching in smoothing is a key in improving the performance.
The Probability Hypothesis Density (PHD) filter is a computationally tractable alternative to the optimal nonlinear
filter. The PHD filter propagates the first moment instead of the full posterior density. Evaluation of
the PHD enables one to extract the number of targets as well as their individual states from noisy data with
data association uncertainties. Recently, a smoothing algorithm was proposed by the authors to improve the
capability of PHD based tracking. Smoothing produces delayed estimates, which yield better estimates not only
for the target states but also for the unknown number of targets. However, in the case of the maneuvering target
tracking problem, this single model method may not provide accurate estimates. In this paper, a multiple model
PHD smoothing method is proposed to improve the tracking of multiple maneuvering targets. A fast sequential
Monte Carlo implementation for a special case is also provided. Simulations are performed with the proposed
method consisting of multiple maneuvering targets. Simulation results confirm the improved performance of the
Passive sonar is widely used in practice to covertly detect maritime vessels. However, the detection of stealthy
vessels often requires active sonar. The risk of the overt nature of active sonar operation can be reduced by
using multistatic sonar techniques. Cheap sonar sensors that do not require any beamforming technique can be
exploited in a multistatic system for spacial diversity. In this paper, Gaussian mixture probability hypothesis
density (GMPHD) filter, which is a computationally cheap multitarget tracking algorithm, is used to track multiple
targets using the multistatic sonar system that provides only bistatic range and Doppler measurements.
The filtering results are further improved by extending the recently developed PHD smoothing algorithm for
GMPHD. This new backward smoothing algorithm provides delayed, but better, estimates for the target state.
Simulations are performed with the proposed method on a 2-D scenario. Simulation results present the benefits
of the proposed algorithm.
The optimal Bayesian multi-target tracking is computationally demanding. The probability hypothesis density
(PHD) filter, which is a first moment approximation of the optimal one, is a computationally tractable alternative.
By evaluating the PHD, one can extract the number of targets as well as their individual states. Recent
sequential Monte Carlo (SMC) implementation of the PHD filter paves the way to apply the PHD filter to nonlinear non-Gaussian problems. It seems that the particle implementation of PHD filter is more dependent on current measurements, especially in the case of low observable target problems (i.e., estimates are sensitive
to missed detections and false alarms). In this paper, a PHD smoothing algorithm is proposed to improve the capability of the PHD based tracking system. By performing smoothing, which gives delayed estimates, we will get not only better estimates for target states but also better estimate for number of targets. Simulations are
performed on proposed method with a multi-target scenario. Simulation results confirm the improved performance of the proposed algorithm.
Radar systems have good radial resolution, but they have poor angular resolution that results in unresolved
measurements. This problem can be mitigated by utilizing the spatial diversity of multistatic radar system. In
this paper, the detection of unresolved targets with a hybrid radar system using signal level fusion is considered.
The system consists of two receivers: one is co-located with the transmitter and the other is located far from the
transmitter. The area of interest, where the transmitter is focused on, is divided into grids, which are formed
by circular range bins of the monostatic receiver and elliptical range bins of the bistatic receiver. Assuming
these grids are good enough to resolve the targets (i.e., each grid has at most one target and vice versa), the
amplitudes of the targets (corresponding to all grids) that maximize the likelihoods of the signals obtained from
both receivers are determined. These optimum values are then compared against a threshold for the final decision.
Simulation studies are performed to demonstrate the proposed algorithm for hybrid radar system with
unresolved targets. The simulation results confirm the enhancement in detection of unresolved targets by fusing
coherently received signals from both monostatic and bistatic receivers.
Detection and estimation of multiple unresolved targets with a monopulse radar is limited by the availability
of information in monopulse signals. The maximum possible number of targets that can be extracted from
the monopulse signals of a single bin is two. Recently two approaches have been proposed in the literature to
overcome this limitation. The first is joint-bin processing that exploits target spill-over among adjacent cells
by modeling the target returns in the adjacent cells. In addition to making use of the additional information
available in target spill-over, it handles a more practical problem where the usual assumption of ideal sampling
is relaxed. The second approach is to make use of tracking information in detection through joint detection and
tracking with the help of Monte Carlo integration of a particle filter. It was shown that the extraction of even
more targets is possible with tracking information. In this paper, a new approach is proposed to combine make
the best of these two approaches - a new joint detection and tracking algorithm with multibin processing. The
proposed method increases the detection ability as well as tracking accuracy. Simulation studies are carried out
with amplitude comparison monopulse radar for an unresolved target scenario. The relative performances of
various methods are also provided.
Detection and estimation of multiple unresolved targets with a monopulse radar is a challenging problem. For ideal single bin processing, it was shown in the literature that at most two unresolved targets can be extracted from the complex matched filter output signal. In this paper, a new algorithm is developed to jointly detect and track more than two targets from a single detected bin. This method involves the use of tracking data in detection. For this purpose, target states are transformed into detection parameter space, which involves high nonlinearity. In order to handle this, the sequential Monte Carlo (SMC) method, which is proved to be effective for nonlinear non-Gaussian estimation problems, is used as the basis of the closed loop system for tracking multiple unresolved targets. In addition to the standard SMC steps, the detection parameters corresponding to the predicted particles are evaluated using the nonlinear monopulse radar beam model. It in turn enables the evaluation of the likelihood of the monopulse signal given tracking data. That is, we evaluate the likelihoods of different hypotheses of possible combinations of targets being in different detected bins. The hypothesis testing is used to find the correct detection event. The particles are updated and resampled according to the hypothesis that has the highest likelihood (score). A simulated amplitude comparison monopulse radar is used to generate the data with more than unresolved two targets. Simulation results confirm the possible extraction and tracking of more than two targets jointly.
The particle filter is an effective technique for target tracking in the presence of nonlinear system model, nonlinear measurement model or non-Gaussian noise in the system and/or measurement processes. In this paper, we compare three particle filtering algorithms on a spawning ballistic target tracking scenario. One of the algorithms, the tagged particle filter (TPF), was recently developed by us. It uses separate sets of particles for separate tracks. However, data association to different tracks is interdependent. The other two algorithms implemented in this paper are the probability hypothesis density (PHD) algorithm and the joint multitarget probability density (JMPD). The PHD filter propagates the first order statistical moment of multitarget density using particles. While, the JMPD stacks the states of a number of targets to form a single particle that is representative of the whole system. Simulation results are presented to compare the performances of these algorithms.
The particle filter is an effective technique for target tracking in the presence of nonlinear system model, nonlinear measurement model or non-Gaussian noise in the system and/or measurement processes. However, the current particle filtering algorithms for multitarget tracking suffer from high computational requirements. In this paper, we present a new implementation of the particle filter, called the tagged particle filtering (TPF) algorithm, to handle multitarget
tracking problems in an efficient manner. The TPF uses a separate sets of particles for each track. Here, each particle is associated with the closest (in terms of likelihoods) measurement. The particles for a particular track may form separate groups in terms of the measurements associated with them and they evolve independently in groups till two or more groups of particles are separated by a distance large enough to be called separate tracks. A
decision is made as to which of the groups is to be retained. Since
this algorithm keeps a separate set of particles for each track, the state estimation for individual tracks does not require any additional computation. Also, this algorithm is association free and target class information can be added to the state for feature aided
tracking. Simulation results are obtained by applying this tracking filter to a spawning target scenario.