This paper 1 , deals with a radar track before detect application in a multi target setting. Track before detect is a method to track weak objects on the basis of raw radar measurements, e.g. the reflected power of the target plus noise. In classical target tracking, the tracking process is performed on the basis of pre-processed measurements, that are constructed from the original measurement data every time step. In this way no integration over time takes place and information is lost. In this paper we will give a modelling setup and a particle filter based algorithm to deal with a multiple target track before detect situation. In simulations we show that, using this method, it is possible to track multiple, closely spaced, weak targets.
Abstract-For certain types of sensor-target configurations a point target model or approach is not suitable and the physical extent of the target has to be accounted for in the processing. An extended target track before detect algorithm is presented and the performance is compared to an algorithm based on the point target assumption. Simulations illustrate the gain in performance obtained by using the extended target model where a particle filter is used for the track before detect implementation.
In this letter, we will present a new result that can be used for detection purposes. We show that when estimating the a posteriori probability density of a possible signal in noise by means of a particle filter, the output of the filter, i.e., the unnormalized weights, can be used to approximately construct the likelihood ratio, which arises in many different detection schemes.
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