In this paper an efficient adaptive parameter control scheme for Multi Function Radar (MFR) is used. This scheme has been introduced in.5 The scheme has been designed in such a way that it meets constraints on specific quantities that are relevant for target tracking while minimizing the energy spent. It is shown here, that
this optimal scheme leads to a considerable variation of the realized detection probability, even within a single scenario. We also show that constraining or fixing the probability of detection to a certain predefined value leads to a considerable increase in the energy spent on the target. This holds even when one optimizes the fixed probability of detection. The bottom line message is that the detection probability is not a design parameter by itself, but merely the product of an optimal schedule.
KEYWORDS: Radar, Digital filtering, Detection and tracking algorithms, Target detection, Surveillance, Signal to noise ratio, Doppler effect, Signal detection, Signal processing, Electronic filtering
In this paper the initial results are presented of a recursive filter based approach to Track Before Detect (TBD) for surveillance radar. Because of the relatively low update rate and large amount of data per scan, brute force methods such as the velocity filter banks or Hough transform for electro-optical sensors are not feasible. We therefore have designed a recursive algorithm that integrates the measured signal strength only for the most likely target trajectory. Furthermore, this algorithm is only started for those radar cells in a scan that have exceeded a preselection threshold. As will be shown, the use of a low preselection threshold significantly reduces the number of radar cells to be considered at a negligible performance reduction. The feasibility of the proposed algorithm is demonstrated through simulations. Further research planned and possible extensions to the initial approach are discussed.
In this paper we present an efficient one-scan-back Probabilistic Data Filter (PDAF). Regarding the general case of an N-scan-back PDAF, it has been noted in the literature that with each additional scan back, there is a considerable increase in computational load while the amount of improvement in tracking performance diminishes. We therefore have designed a filter that aims to benefit at a minimal increase in computational cost from the one-scan-back architecture that effectively rules out unlikely measurement pairings. In this filter, we use the measurements in previous scan only to produce better weights for the measurements in the present scan. Thus, as compared to a "full" one-scan-back PDAF, we considerably reduce the number of updating and merging steps each scan. For the proposed filter, and the closely related "standard" (zero-scan-back) PDAF and "full" one-scan-back PDAF, we provide the theoretical background, numerical implementation, and simulation results.
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