KEYWORDS: Artificial intelligence, Radar, Maritime surveillance, Data fusion, Surveillance, Detection and tracking algorithms, Motion models, Time metrology, Head, System identification
Using the Automatic Identification System (AIS) ships identify themselves intermittently by broadcasting their
location information. However, traditionally radars are used as the primary source of surveillance and AIS is
considered as a supplement with a little interaction between these data sets. The data from AIS is much more
accurate than radar data with practically no false alarms. But unlike the radar data, the AIS measurements
arrive unpredictably, depending on the type and behavior of a ship. The AIS data includes target IDs that can
be associated to initialized tracks. In multitarget maritime surveillance environment, for some targets the revisit
interval form the AIS could be very large. In addition, the revisit intervals for various targets can be different.
In this paper, we proposed a joint probabilistic data association based tracking algorithm that addresses the
aforementioned issues to fuse the radar measurements with AIS data. Multiple AIS IDs are assigned to a track,
with probabilities updated by both AIS and radar measurements to resolve the ambiguity in the AIS ID source.
Experimental results based on simulated data demonstrate the performance the proposed technique.
KEYWORDS: Radar, Detection and tracking algorithms, Tin, Data processing, Sensors, Antennas, Target detection, Systems modeling, Phased arrays, Time metrology
Electronically scanned array radars as well as mechanically steered rotating antennas return measurements
with different time stamps during the same scan while sweeping form one region to another. Data association
algorithms process the measurements at the end of the scan in order to satisfy the common one measurement
per track assumption. Data processing at the end of a full scan resulted in delayed target state update. This
issue becomes more apparent while tracking fast moving targets with low scan rate sensors. In this paper, we
present new dynamic sector processing algorithm using 2D assignment for continuously scanning radars. A
complete scan can be divided into sectors, which could be as small as a single detection, depending on the
scanning rate and sparsity of targets. Data association followed by filtering and target state update is done
dynamically while sweeping from one end to another. Along with the benefit of immediate track updates,
continuous tracking results in challenges such as multiple targets spanning multiple sectors and targets crossing
consecutive sectors. Also, associations performed in the current sector may require changes in association done
in previous sectors. Such difficulties are resolved by the proposed 2D assignment algorithm that implements
an incremental Hungarian assignment technique. The algorithm offers flexibility with respect to assignment
variables for fusing of measurements received in consecutive sectors. Furthermore the proposed technique can
be extended to multiframe assignment for jointly processing data from multiple scanning radars. Experimental
results based on rotating radars are presented.
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.
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