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In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. Furthermore, a post-processing algorithm has been designed to remove the Non Line-Of-Sight (NLOS) UWB ranging estimates to further improve the accuracy of the proposed solution. This solution has been tested using both a simulated environment and a real environment. This research work is in the scope of the PRoPART European Project. The different real tests have been performed on the AstaZero proving ground using a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) as testing platform. Thus, a real time positioning solution has been achieved complying with the accuracy requirements for the PRoPART use case.

High-level DSLs for low-level programming.

When writing embedded domain specific languages in Haskell, it is often convenient to be able to make an instance of the Monad class to take advantage of the do-notation and the extensive monad libraries. Commonly it is desirable to compile such languages rather than just interpret them. This introduces the problem of monad reification, i.e. observing the structure of the monadic computation. We present a solution to the monad reification problem and illustrate it with a small robot control language. Monad reification is not new but the novelty of our approach is in its directness, simplicity and compositionality.

Writing high performance GPGPU code is often difficult and timeconsuming, potentially requiring laborious manual tuning of lowlevel details. Despite these challenges, the cost in ignoring GPUs in high performance computing is increasingly large.Auto-tuning is a potential solution to the problem of tedious manual tuning. We present a framework for auto-tuning GPU kernels which are expressed in an embedded DSL, and which expose compile-time parameters for tuning. Our framework allows for kernels to be polymorphic over what search strategy will tune them, and allows search strategies to be implemented in the same metalanguage as the kernel-generation code (Haskell). Further, we show how to use functional programming abstractions to enforce regular (hyper-rectangular) search spaces.We also evaluate several common search strategies on a variety of kernels, and demonstrate that the framework can tune both EDSL and ordinary CUDA code.

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