Search results

To recover the growing deficit between American and near-peer mobile artillery ranges, the US Army is exploring the use of the M982 Excalibur munition, a family of long-range precision projectiles. This paper aims to analyze the effectiveness of the M982 in comparison to the M795 and M549A1 projectiles to further the understanding of what this new asset contributes.

Based upon doctrinal scenarios for target destruction, a statistical analysis is performed using Monte Carlo simulation to identify a likely probability of kill ratio for the M982. A values-based hierarchical modeling approach is then used to differentiate the M982 from similar-type projectiles quantitatively in terms of several different attributes. Finally, sensitivity analyzes are presented for each of the value attributes, to identify areas where measures may lack robustness in precision.

Based upon a set of seven value measures, such as maximum range, effective range, the expected number of rounds to destroy a target, and the unit cost of a munition, the M982 1a-2 was found to be best suited for engaging point and small area targets. It is noted, however, that the M795 and M549A1 projectiles are likely better munition options for large area targets. Hence, an integrated targeting plan may best optimize the force’s weapon systems against a near-peer adversary.

The findings provide initial evidence that doctrinal adjustments in how the Army uses its artillery systems may be beneficial in facing near-peer adversaries. In addition, the values-based modeling approach offered in this research provides a framework for which similar technological advances may be examined.

The US Government is challenged to maintain pace as the world’s de facto provider of space object cataloging data. Augmenting capabilities with nontraditional sensors present an expeditious and low-cost improvement. However, the large tradespace and unexplored system of systems performance requirements pose a challenge to successful capitalization. This paper aims to better define and assess the utility of augmentation via a multi-disiplinary study.

Hypothetical telescope architectures are modeled and simulated on two separate days, then evaluated against performance measures and constraints using multi-objective optimization in a heuristic algorithm. Decision analysis and Pareto optimality identifies a set of high-performing architectures while preserving decision-maker design flexibility.

Capacity, coverage and maximum time unobserved are recommended as key performance measures. A total of 187 out of 10¹⁷ architectures were identified as top performers. A total of 29% of the sensors considered are found in over 80% of the top architectures. Additional considerations further reduce the tradespace to 19 best choices which collect an average of 49–51 observations per space object with a 595–630 min average maximum time unobserved, providing redundant coverage of the Geosynchronous Orbit belt. This represents a three-fold increase in capacity and coverage and a 2 h (16%) decrease in the maximum time unobserved compared to the baseline government-only architecture as-modeled.

This study validates the utility of an augmented network concept using a physics-based model and modern analytical techniques. It objectively responds to policy mandating cataloging improvements without relying solely on expert-derived point solutions.

Ground Penetrating Radar is a multidisciplinary Nondestructive Evaluation technique that requires knowledge of electromagnetic wave propagation, material properties and antenna theory. Under some circumstances this tool may require auxiliary algorithms to improve the interpretation of the collected data. Detection, location and definition of target’s geometrical and physical properties with a low false alarm rate are the objectives of these signal post-processing methods. Basic approaches are focused in the first two objectives while more robust and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.

The purpose of this paper is to test the performance of the control system developed for the helicopter automatic approach and landing on the moving vessel deck, when different values of backlashes are applied to the four control actuators.

The system consists of automatic control algorithm based on the linear quadratic regulator and the vessel motion prediction algorithm based on autoregressive method with parameters calculated using Burg’s method. Necessary navigation data is provided by on-board inertial navigation system/Global Positioning System. Calculated control commands are executed by four electromechanical actuators. Performance of the mission, which is based on selected procedure of approach and landing of the helicopter on the moving vessel deck, is analyzed taking into account different values of backlashes applied to the actuators.

In this paper, a description of the control system dedicated for automatic approach and landing of the helicopter on the moving vessel deck is shown. Necessary information about helicopter dynamic model, control system and vessel motion model is included. Tests showing influence of actuator backlashes on the mission performance are presented.

The developed control methodology can be adapted for selected helicopter and used in prospective development of an automatic flight control system (AFCS) or in a simulator. The system can be used to define in which conditions helicopter can perform safe and successful automatic approach and landing on a moving vessel deck.

In this paper, an integrated control system is presented; influence of the control actuator backlashes on the mission performance is analyzed.

The paper aims to solve the problem of personnel intrusion identification within the limits of high-speed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy of object recognition in dark and harsh weather conditions.

This paper adopts the fusion strategy of radar and camera linkage to achieve focus amplification of long-distance targets and solves the problem of low illumination by laser light filling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm for multi-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposes a linkage and tracking fusion strategy to output the correct alarm results.

Simulated intrusion tests show that the proposed method can effectively detect human intrusion within 0–200 m during the day and night in sunny weather and can achieve more than 80% recognition accuracy for extreme severe weather conditions.

(1) The authors propose a personnel intrusion monitoring scheme based on the fusion of millimeter wave radar and camera, achieving all-weather intrusion monitoring; (2) The authors propose a new multi-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring under adverse weather conditions; (3) The authors have conducted a large number of innovative simulation experiments to verify the effectiveness of the method proposed in this article.

This paper aims to discuss the classification of targets based on their radar cross-section (RCS). The wavelength, the dimensions of the targets and the distance from the antenna are in the order of 1 mm, 1 m and 10 m, respectively.

The near-field RCS is considered, and the physical optics approximation is used for its numerical calculation. To model real scenarios, the authors assume that the incident angle is a random variable within a narrow interval, and repeated observations of the RCS are made for its random realizations. Then, the histogram of the RCS is calculated from the samples. The authors use a nearest neighbor rule to classify conducting plates with different shapes based on their RCS histogram.

This setup is considered as a simple model of traffic road sign classification by millimeter-wavelength radar. The performance and limitations of the algorithm are demonstrated through a set of representative numerical examples.

The proposed method extends the existing tools by using near-field RCS histograms as target features to achieve a classification algorithm.