Using the unscented transform to reduce the number of measurements in drive tests

In a drive test, it is common to measure the electric field strength (or other related quantity) at fixed intervals to calibrate propagation models or to optimize wireless network coverage. We propose to select the measurement locations based on the unscented transform. Using just a few points (tens rather than thousands), we show that the statistics of the measurement tends to the correct value, which can save time and reduce the cost of drive tests.

An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking

In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection. Moreover, a self-adaptive gain modification coefficient is defined to solve the low accuracy problem caused by the sigma point reduction, and the problem of particle degradation is solved by modifying the weights calculation method. In addition, a new multi-sensor fusion model is proposed, which better integrates radar and infrared sensors. Simulation results show that IUPF effectively improves real-time performance while ensuring the tracking accuracy compared with other algorithms. Besides, compared with the traditional distributed fusion architecture, the proposed new architecture makes better use of the advantages of radar and an infrared sensor and improves the tracking accuracy.