Intent inference is about analyzing the actions and activities of an adversarial force or target of interest to reach a conclusion (prediction) on its purpose. In this paper, we report one of our research works on intent inference to determine the likelihood of an attack aircraft being tracked by a military surveillance system delivering its weapon. Effective intent inference will greatly enhance the defense capability of a military force in taking preemptive action against potential adversaries. It serves as early warning and assists the commander in his decision making. For an air defense system, the ability to accurately infer the likelihood of a weapon delivery by an attack aircraft is critical. It is also important for an intent inference system to be able to provide timely inference. We propose a solution based on the analysis of flight profiles for offset pop-up delivery. Simulation tests are carried out on flight profiles generated using different combinations of delivery parameters. In each simulation test, the state vectors of the tracked aircraft are updated via the application of the Interacting Multiple Model filter. Relevant variables of the filtered track (flight trajectory) are used as inputs to a Mamdani-type fuzzy inference system. The output produced by the fuzzy inference system is the inferred possibility of the tracked aircraft carrying out a pop-up delivery. We present experimental results to support our claim that the proposed solution is indeed feasible and also provides timely inference that will assist in the decision making cycle.
Proc. SPIE. 5813, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005
KEYWORDS: Target detection, Detection and tracking algorithms, Sensors, Error analysis, Monte Carlo methods, Data processing, Brain-machine interfaces, Algorithm development, Filtering (signal processing), Data fusion
Tracking multiple ground targets under clutter and in real time poses
several likely challenges: vehicles often get masked by foliage or
line-of-sight (LOS) problems, manifesting in misdetections and false alarms. Further complications also arise when groups of vehicles merge or split. This paper presents an attempt to address these issues using a group tracking approach. Group tracking is a way to ameliorate, or at least soften the impact of such issues from the hope that at least partial information will be received from each target group even when the probability of detection, PD of each individual member is low. A Strongest Neighbour Association (SNA) method of measurement-to-track association based on space-time reasoning and track-measurement similarity measures has been derived. We combine the association strengths of the space-time dynamics, the degree-of-overlap and the historical affinity metrics to relate measurements and tracks. The state estimation is based on standard Kalman filter. Lastly, a Pairwise Historical Affinity Ratios (PHAR) is proposed for the detection of a split scenario. This method has been tested to work well on a simulated convoy-splitting scenario. Monte Carlo experiment runs of six different error rates with five different compositions of errors have been conducted to assess the performance of the tracker. Results indicated that the group tracker remains robust (>80% precision) even in the presence of high individual source track error rates of up to 30%.
Ground targets can be detected by multiple classes of sources in
military surveillance. There are two main challenges for the
acquisition of ground situation picture from data collected by
multiple sources. First, different sources provide different
information that describes military entities at different
granularities and accuracies. This makes processing of data in one
unified tracker difficult. Secondly, the data update rates of
these sources vary, some update rates could be very low (such as
hours), leading to greater difficulty for data association.
This paper presents our attempt in multi-source ground target
tracking, taking the above two issues into consideration. Targets
are tracked in groups, and multiple trackers are designed so that
data of different granularities are processed by the respective
trackers. Tracks from these trackers are then correlated to form
the common picture. Two strategies are proposed to handle the
problem of varying data update rate. The first strategy is to
exploit different approaches to calculate the beliefs of data
association according to update rates. When update rate is high,
the belief is calculated by a distance function based on estimated
kinematical states. When update rate is low, the belief of data
association is computed using Bayesian network. Bayesian network
infers the beliefs based on observed information and domain
knowledge. The second strategy is to exploit the complementary
information in different trackers to improve data association. The
first step is to find the correlation among tracks from different
trackers. This track-track correlation information is fed back to
modify the beliefs of data associations in the tracks.
Experiments demonstrated that such combination of multi-source
information not only produces more complete ground picture, but
also helps to improve the data association accuracy in the
In this paper, we describe a deghosting algorithm in multiple passive acoustic sensor environment. In a passive acoustic
sensor system, a target is detected by its bearing to the sensor, and the target location is obtained from triangulation of
bearings on different sensors. However, in multi-passive sensor and multi-target scenario, triangulation is difficult. This
is because multi-target triangulation results in a number of ghost targets being generated. In order to remove the
triangulating ghosts, the deghosting technique is essential to distinguish the true targets from the ghost targets. We
suggest a deghosting algorithm by applying Bayes’ theorem and the likelihood function on the acoustic signals. A
probability related to acoustic signal on each triangulating point is recursively computed and updated at every time
stamp or frame. The triangulating point will be classified as a true target, once its probability exceeds a predefined
threshold. Furthermore, acoustic signal has propagation delay. The situation yields the triangulating location biased to
the bearing of the nearest sensor. In our algorithm, the propagation delay problem is solved by matching the histories of
bearing tracks, and yields the unbiased location that has similar emitting times for the sensors contributing to the
triangulation point. The emitting times can be derived from detecting times and propagation delays. Performance result
is presented on simulation data.
The Sensor Management Team from DSO National Laboratories has developed a Decision Support System (DSS) to assist human operators in determining the most effective employment and/or deployment of a suite of sensors given a particular mission or operational scenario. The key issue addressed by the system is the resource allocation problem accompanied by two contradictory objectives, namely to maximize combined coverage of the sensor suite and to maximize survivability of the sensor within the suite. Furthermore, the system is to handle operational constraints on the usage of the suite of sensors. In this paper, we will describe how we handle the problem by formulating it as a Multiple Objective Optimization (MOO) problem. This system may be used as a pre-mission planning tool or a real time decision aid for the sensor suite commander. With the increase in size of the sensor suite and the number of possible deployment sites, the feasibility space of the employment/deployment configurations will grow tremendously. In order to allow for near real time decision support, the team has incorporated genetic algorithm to solve the MOO problem.
This paper demonstrates an approach of combining the hyperbolic and triangulation techniques for better location of targets in multi-site sensor environments. The paper also addresses the various techniques and terms used to find target location in multi-site sensor environments. These include elliptic, hyperbolic, triangulation techniques and their combination. The hyperbolic technique is discussed in detail and simulation results are shown.
In this paper, we introduce the application of fuzzy logic concepts to solve the time-delay problem in tracking moving target using passive acoustic sensors. Passive tracking which uses the direction of arrival or bearing of a target is a nontrivial task. The problem is made even more difficult to solve if the passive sensors measurement of bearing is based on acoustic signal only. This is because the acoustic signal introduce time-delay i.e. different senors spatially apart will receive the same target's acoustic signal at different time. The time-delay problem cannot be resolve easily partly because the amplitude of the acoustic signal strength cannot be modeled linearly; its behavior is nonlinear subjected to environmental conditions. To solve these problems we propose to apply the fuzzy logic concept, using information from sensors such as amplitude difference and time-stamp difference from different sensors. The defuzzified results provide one of the main factors for computing the correlation strength between different bearing tracks. The two tracks with the highest correlation strength are then used to determine the position of the target.
This paper presents a novel approach for tracking maneuvering target in the X-Y plane. The algorithm uses the fuzzy if-then rules and the exponential decaying for tuning the process noise covariance in its upscaling and downscaling respectively. A new method of estimating the turn rate is also proposed. This algorithm is compared with the IMM filter using simulated and real data.