KEYWORDS: Particles, Sensors, Target detection, Detection and tracking algorithms, Signal to noise ratio, Particle filters, Interference (communication), Signal processing, Signal detection, Radar
Track-before-detect (TBD) refers to a tracking scheme where detection of a target is not made by placing a threshold on the sensor data. Rather, the complete sensor data is used to detect and track a target in the absence of a data threshold. By using all of the sensor data a TBD algorithm can detect and track targets which have a lower signal power than could be detected by using a standard detection and tracking scheme.
This paper presents an efficient particle filter TBD algorithm, which models the signal processing stages which may be found in a sensor such as radar. In this type of sensor the noise is modelled as the magnitude of a complex Gaussian process, which is Rayleigh distributed. This noise model and the model of the sensor signal processing is incorporated into the filter derivation. It is shown that in a simple simulation the algorithm can detect and track targets with a signal-to-noise ratio as low as 3dB.
Over-the-horizon radar (OTHR) uses the refraction of high frequency radiation through the ionosphere in order to detect targets beyond the line-of-sight horizon. The complexities of the ionosphere can produce multipath propagation, which may result in multiple resolved detections for a single target. When there are multipath detections, an OTHR tracker will produce several spatially separated tracks for each target. Information conveying the state of the ionosphere is required in order to determine the true location of the target and is available in the form of a set of possible propagation paths, and a transformation from measured coordinates into ground coordinates for each path. Since there is no a-priori information as to how many targets are in the surveillance region, or which propagation path gave rise to which track, there is a joint target and propagation path association ambiguity which must be resolved using the available track and ionospheric information. The multipath track association problem has traditionally been solved using a multiple hypothesis technique, but a shortcoming of this method is that the number of possible association hypotheses increases exponentially with both the number of tracks and the number of possible propagation paths. This paper proposes an algorithm based on a combinatorial optimisation method to solve the multipath track association problem. The association is formulated as a two-dimensional assignment problem with additional constraints. The problem is then solved using Lagrangian relaxation, which is a technique familiar in the tracking literature for the multidimensional assignment problem arising in data association. It is argued that due to a fundamental property of relaxations convergence cannot be guaranteed for this problem. However, results show that a multipath track-to-track association algorithm based on Lagrangian relaxation, when compared with an exact algorithm, provides a large reduction in computational effort, without significantly degrading association accuracy.
KEYWORDS: Radar, Detection and tracking algorithms, Target detection, Electronic filtering, Algorithm development, Data modeling, Time metrology, Signal processing, Stochastic processes, Signal detection
Over-the-horizon Radar (OTHR) uses the ionosphere as a propagation medium to detect targets beyond the line-of-sight horizon. The layered structure of the ionosphere can support several signal propagation paths between the radar site and detected targets, often giving rise to multiple radar tracks for a single target. A multi-hypothesis multipath track fusion (MPTF) algorithm for OTHR has been developed and reported in earlier publications. In this paper, the MPTF formalism is developed from first principles to explicitly explore sources of track dependence which arise in OTHR track fusion. In particular, a solution is proposed which accounts for track-to-track dependencies arising from common target ionospheric dynamic processes. The algorithm is applied to the simplest nontrivial case, where the ionosphere is modeled as two spherically-symmetric reflecting layers, and two radar tracks are observed.
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