KEYWORDS: Detection and tracking algorithms, Dysprosium, Sensors, Filtering (signal processing), Time metrology, Data communications, Data centers, Data fusion, Signal processing, Sensor fusion
In multisensor target tracking systems receiving out-of-sequence measurements from local sensors is a common
situation. In the last decade many algorithms have been proposed to update a target state with an OOSM
optimally or suboptimally. However, what one faces in the real world is multiple OOSMs, which arrive at the
fusion center in, generally, arbitrary orders, e.g., in succession or interleaved with in-sequence measurements.
A straightforward approach to deal with this multi-OOSM problem is by sequentially applying a given OOSM
algorithm; however, this simple solution does not guarantee optimal update under the multi-OOSM scenario. The
present paper discusses the differences between the single-OOSM processing and the multi-OOSM processing, and
presents the general solution to the multi-OOSM problem, called the complete in-sequence information (CISI)
approach. Given an OOSM, in addition to updating the target state at the most recent time, the CISI approach
also updates the states between the OOSM time and the most recent time, including the state at the OOSM
time. Three novel CISI methods are developed in this paper: the information filter-equivalent measurement
(IF-EqM) method, the CISI fixed-point smoothing (CISI-FPS) method and the CISI fixed-interval smoothing
(CISI-FIS) method. Numerical examples are given to show the optimality of these CISI methods under various
multi-OOSM scenarios.
In a real tracking system, track breakages can occur due to highly maneuvering targets, low detection probability,
or clutter. Previously, a track segment association approach (TSA) was developed for an airborne early warning
(AEW) system to improve track continuity by"stitching" broken track segments pertaining to the same target.
However, this technique cannot provide satisfactory association performance in tracking with a GMTI radar
ground moving targets employing evasive move-stop-move maneuvers. To avoid detection by a GMTI radar,
targets can deliberately stop for some time before moving again. Since a GMTI radar does not detect a target
when the radial velocity (along the line-of-sight from the sensor) falls below a certain minimum detectable velocity
(MDV), the move-stop-move maneuvers of the targets usually lead to broken tracks as a result. We present a
new TSA technique which employs a dummy track to formulate a complete association. By using an IMM
estimator with state-dependent mode transition probabilities (IMM-SDP) for track segment prediction (forward
and backward), the proposed algorithm can effectively stitch both "regular" broken tracks and broken tracks due
to targets' move-stop-move maneuvers. Comparisons are given to show the advantages of the proposed algorithm
in broken tracks reduction and track continuity improvement.
This paper presents a novel method for tracking ground moving targets with a GMTI radar. To avoid detection
by the GMTI radar, targets can deliberately stop for some time before moving again. The GMTI radar does not
detect a target when the radial velocity (along the line-of-sight from the sensor) falls below a certain minimum
detectable velocity (MDV). We develop a new approach by using state-dependent mode transition probabilities
to track move-stop-move targets. Since in a real scenario, the maximum deceleration is always limited, a target
can not switch to the stopped-target model from a high speed. Therefore, with the use of the stopped-target
model, the Markov chain of the mode switching has jump probabilities that depend on the target's kinematic
state. A mode transition matrix with zero jump probabilities to the stopped-target mode is used when the speed
is above a certain "stopping" limit (above which the target cannot stop in one sampling interval, designated as
"fast stage") and another transition matrix with non-zero jump probabilities to the stopped-target mode is used
when the speed is below this limit (designated as "slow stage"). The stage probabilities are calculated using the
kinematic state statistics from the IMM estimator and then used to combine the state-dependent mode transition
probabilities (SDP) in the two different transition matrices. The experimental results show that the proposed
algorithm outperforms previous methods.
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