Abstract
A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point measurements from the observed sensor data. Track before detect (TBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. Various different approaches exist for tackling the TBD problem. This article compares the ability of several different approaches to detect low amplitude targets. The following algorithms are considered in this comparison: Bayesian estimation over a discrete grid, dynamic programming, particle filtering methods, and the histogram probabilistic multihypothesis tracker. Algorithms are compared on the basis of detection performance and computation resource requirements.
Highlights
Traditional tracking algorithms are designed assuming that the sensor provides a set of point measurements at each scan
The common approach is to apply a threshold to the data and to treat those cells that exceed the threshold as point measurements, perhaps using interpolation methods to improve accuracy
The detection performance of four alternative track-beforedetect algorithms has been investigated for a range of target signal-to-noise ratio (SNR) values and speeds
Summary
Traditional tracking algorithms are designed assuming that the sensor provides a set of point measurements at each scan. A practical sensor may provide a data image, where each pixel corresponds to the received power in a particular spatial location (e.g., range bins and azimuth beams) In this case, the common approach is to apply a threshold to the data and to treat those cells that exceed the threshold as point measurements, perhaps using interpolation methods to improve accuracy. Particle filtering may be able to achieve similar estimation performance for lower cost by using less sampling points than would be required for a discrete grid Another alternative approach is the histogram probabilistic multihypothesis tracker, H-PMHT [14, 15]. A closely related concept is to use a separate Markov chain for the presence or absence of a target as originally introduced for PDA in [22] This approach has been used for the particle filter [13] and a generalised version was applied to PMHT in [23]. The performance of the algorithms is investigated via simulations of low SNR targets in Section 4 and Section 5 concludes
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