An Efficient Indoor Target Tracking Algorithm Using TDOA Measurements With Applications to Ultra-Wideband Systems

The ultra-wideband technique has shown its effectiveness for indoor target tracking. Various types of measurements have been applied to ultra-wideband systems for indoor target tracking, and the time difference of arrival (TDOA) measurement-based approaches are the most widely used methods due to their good accuracy and feasibility. Target tracking with the TDOA measurements usually encounters the problem of correlated measurement noises, as one sensor network utilizes the common reference sensor for measurement generation. The off-diagonal entries in the measurement error covariance matrix become non-zero values, which makes the standard target tracking algorithms inconvenient for practical installation of an ultra-wideband system. Another problem in sensor networks is properly exploiting the measurements obtained from multiple sensors considering practical conditions, such as storage limitations or computational resource consumption. The parallel update and the serial update are usually applied for the multi-sensor tracking problem. This paper presents a target tracking algorithm that integrates the Cholesky decomposition to decorrelate the measurement noises for the serial update, thus improving computational efficiency. The proposed algorithm is realized in an ultra-wideband system for real-time target tracking, and an experiment using real data is conducted to validate its practicability.