KEYWORDS: Sensors, Mahalanobis distance, 3D acquisition, Detection and tracking algorithms, Sensor networks, Missiles, Data fusion, Reliability, Radio propagation, Defense and security
Conventional algorithms for track association (termed "correlation" by convention) employ algorithms which are
applied to all sensor tracks at a specific time. The overall value of sensor networks for data fusion is closely
tied to the reliability of correct association of common objects tracked by the sensors. Multisensor architectures
consisting of gaps in target coverage requires that tracks must be propagated substantially forward or backward
to a common time for correlation. This naturally gives rise to the question: at which time should track correlation
be performed? In the conventional approach, a two-sensor correlation problem would be solved by propagating
the first sensor's tracks forward to the update time (current time) of the tracks from the second sensor. We
question this approach by showing simulation results that indicate that the current time can be the worst time
to correlate. In addition, a methodology for calculating the approximate optimal correlation time for linear-Gaussian tracking problems is provided.
In this paper, the problem of estimating sensor biases (e.g.,
range and bearing biases) from measurements of targets with
deterministic dynamics but uncertain initial conditions is
considered. The known dynamics are exploited by a single sensor to
self-calibrate or determine unknown sensor biases. The concept of
bias state tracklet fusion from tracks of multiple trajectories is
discussed. The effectiveness of this concept is demonstrated, and
the performance sensitivity to geometry variations and the number
of available targets is examined. For comparison, the bias state
tracklet estimator is compared to a nonlinear least squares (NLS)
estimator.
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