Test data generation with a Kalman filter-based adaptive genetic algorithm

Test data generation with a Kalman filter-based adaptive genetic algorithm

Software testing is a crucial part of software development. It enables quality assurance, such as correctness, completeness and high reliability of the software systems. Current state-of-the-art software testing techniques employ search-based optimisation methods, such as genetic algorithms to handle the difficult and laborious task of test data generation. Despite their general applicability, ...

A Hybrid Adaptive Unscented Kalman Filter Algorithm

In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for statement and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value...

A novel adaptive Kalman filter based NLOS error mitigation algorithm

In this paper, we presented an algorithm for NLOS error mitigation based on adaptive Kalman filter with colored measurement noise. To eliminate NLOS error which induced by TOF-based distance measurements, a colored noise model is firstly established according to measurement noise and the filter parameters are adjusted dynamically based on the severity of NLOS environment. Then combined with ada...

Path-oriented test cases generation based adaptive genetic algorithm

The automatic generation of test cases oriented paths in an effective manner is a challenging problem for structural testing of software. The use of search-based optimization methods, such as genetic algorithms (GAs), has been proposed to handle this problem. This paper proposes an improved adaptive genetic algorithm (IAGA) for test cases generation by maintaining population diversity. It uses ...

Extended Kalman Filter Based Fuzzy Adaptive Filter