Range and Accuracy Improvement of Artillery Rocket Using Fixed Canards Trajectory Correction Fuze

This paper presents a two-phase guidance and control algorithm to extend the range and improve the impact point accuracy of a 122-mm rocket using a fixed canards trajectory correction fuze. The guidance algorithm consists of a unique glide and correction phase of the rocket trajectory that is activated after the flight’s apex. The glide phase operates in an open-loop configuration where guidance commands are generated to increase the range of the rocket. In contrast, the correction phase operates in a closed-loop configuration where the Impact Point Prediction method based on Modified Projectile Linear Theory is used as a feedback channel to correct the range and drift errors. The proposed fixed canards trajectory correction fuze has a simple and reliable single channel roll-orientation control configuration. The rocket trajectory model consists of a 7-DOF non-linear dynamic model of a dual-spin rocket configuration with a fixed canards correction fuze mounted at the nose. A Monte Carlo simulation of the rocket’s inertial and launch point perturbations show that the fixed canards fuze with the proposed guidance algorithm can double the range of the rocket without changing the rocket motor thrust-time curve. At the same time, the rocket’s accuracy can also be improved beyond the results of an unguided rocket.

Using Particle Swarm Optimization to Compute Hundreds of Possible Directional Paths to Get Back/Stay in the Drilling Window

One of the responsibilities of a directional driller (DD) is the computation of the current bit position given the last survey station measurement, and with that information calculate the path back to plan if directional correction is needed. Having only a few minutes during a drilling connection to perform these calculations, the DD is limited to compute only a handful of possible paths that will be presented to the Drilling Engineer/Company Man. With this information, the Company Man will decide which path to follow. The present work aims to develop a computer algorithm that replicates the field knowledge of DDs but can compute hundreds of paths in less than one minute. In addition, since the objective of the trajectory correction may differ, the algorithm also can optimize for one of three goals: maximum rate of penetration (ROP), minimum tortuosity in the path, or maximum footage in the drilling target window. The paper presents examples of four different path recommendations in the lateral portion of a horizontal well. The results show the optimum recommended paths for the same position for a specific optimization goal. Finally, a comparison between the running time and number of paths computed is presented. All results were obtained during the validation tests of the algorithm.

Infrared Small Target Detection Method with Trajectory Correction Fuze Based on Infrared Image Sensor

Due to the complexity of background and diversity of small targets, robust detection of infrared small targets for the trajectory correction fuze has become a challenge. To solve this problem, different from the traditional method, a state-of-the-art detection method based on density-distance space is proposed to apply to the trajectory correction fuze. First, parameters of the infrared image sensor on the fuze are calculated to set the boundary limitations for the target detection method. Second, the density-distance space method is proposed to detect the candidate targets. Finally, the adaptive pixel growth (APG) algorithm is used to suppress the clutter so as to detect the real targets. Three experiments, including equivalent detection, simulation and hardware-in-loop, were implemented to verify the effectiveness of this method. Results illustrated that the infrared image sensor on the fuze has a stable field of view under rotation of the projectile, and could clearly observe the infrared small target. The proposed method has superior anti-noise, different size target detection, multi-target detection and various clutter suppression capability. Compared with six novel algorithms, our algorithm shows a perfect detection performance and acceptable time consumption.