The basic idea of Statistical Model Checking is to repeatedly simulate the behaviour of a real-world system in order to say something about the probability that some performance property is satisfied. When the system model is huge, a single simulation run can take hours. Accordingly, it is vital to be able to terminate as soon as possible. We show how currently used techniques can be compared to a random walk on the real line. We then discuss the shortcomings of these methods and how these can be alleviated.
Radar systems are used widely for estimating the position, kinematic properties and other characteristics of both stationary and moving objects (also called targets).
Several radar parameters, such as the emitting power, direction of emission and the waveform characteristics, can be selected online for improved performance according to the scenario under consideration.
We will demonstrate how sensor management can be used for selecting the optimal radar parameters and therefore, improve the performance of the two main radar functions, meaning target tracking and search for undetected targets.
The aforementioned research is being carried out in the MC IMPULSE project https://mcimpulse.isy.liu.se
Radar systems are used widely for detecting and tracking stationary or moving objects (also called targets). The classical approach to detect and track a target proceeds in two phases: a first detection step consist in pre-processing the raw radar signal to keep only detection “plots”. A second tracking step aims to estimate the actual state of the target from these detection “plots”. In the first step a threshold decision is already made and obviously results in a loss of information. To overcome this problem, the Track before Detect (TBD) approach proposes to base the tracking on the raw measurements instead of plots.
First, a model-based integrated detection & tracking extended to include ambiguities and eclipsing effects in range and Doppler will be detailed. Then, it will be applied by means of a particle filter. The proposed particle filter succeeds in resolving range and Doppler ambiguities and detecting and tracking multiple targets in a TBD context.