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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.

Each of the four objectives can be applied within the military training environment. Military training often requires that soldiers achieve specific levels of performance or proficiency in each phase of training. For example, training courses impose entrance and graduation criteria, and awards are given for excellence in military performance. Frequently, training devices, training media, and training evaluators or observers also directly support the need to diagnose performance strengths and weaknesses. Training measures may be used as indices of performance, and to indicate the need for additional or remedial training.

The purpose of this paper is to present the aerodynamic analysis and external ballistics modeling used in the development of a rocket-target for short range air defence missile systems.

A computational fluid dynamics (CFD) analysis of the airflow around the rocket-target was carried out to estimate the drag, which was needed to develop a mathematical model for external ballistics of the rocket-target. Field-experimental testing was conducted to compare the model results to the data obtained experimentally using various additional measurement techniques such as global positioning system (GPS) coordinates marking of the crash and launch sites, air defence surveillance radar tracking and installing equipment for telemetric data capturing and transmission.

Various ballistic parameters such as the velocity and trajectory of the rocket-target were obtained taking into account the CFD analysis results and internal ballistics data. The field-experimental testing showed a good agreement between the model results and the results obtained by the experimental techniques.

The presented computational models and the experimental techniques could be used in future developments of similar aircraft.

This paper presents a research approach for developing a rocket-target. The results of the research were used as a basis for developing a rocket-target for short range air defence rocket systems. The developed rocket-target was successfully implemented in practice.

Nowadays advancement in acoustic technology can be explored with marine assets. The purpose of the paper is pervasive computing underwater target tracking has aroused military and civilian interest as a key component of ocean exploration. While many pervasive techniques are currently found in the literature, there is little published research on the effectiveness of these paradigms in the target tracking context.

The unscented Kalman filter (UKF) provides good results for bearing and elevation angles only tracking. Detailed methodology and mathematical modeling are carried out and used to analyze the performance of the filter based on the Monte Carlo simulation.

Due to the intricacy of maritime surroundings, tracking underwater targets using acoustic signals, without knowing the range parameter is difficult. The intention is to find out the solution in terms of standard deviation in a three-dimensional (3D) space.

A new method is found for the acceptance criteria for range, course, speed and pitch based on the standard deviation for bearing and elevation 3D target tracking using the unscented Kalman filter covariance matrix. In the Monte Carlo simulation, several scenarios are used and the results are shown.

From many decades, bearings-only tracking (BOT) is the interested problem for researchers. This utilises nonlinear filtering methods for state estimation as there is only information about the target, i.e. bearing is a nonlinear measurement. The measurement bearing is tangentially related to the target state vector. There are many nonlinear filtering algorithms developed so far in the literature.

In this research work, the recently developed nonlinear filtering algorithm, i.e. shifted Rayleigh filter (SRF), is applied to BOT.

The SRF is tested for two-dimensional BOT against various scenarios. The simulation results emphasise that the SRF performs well when compared to the standard nonlinear filtering algorithm, unscented Kalman filter (UKF).

SRF utilises the nonlinearities present in the bearing measurement through the use of moment matching. The SRF is able to produce the solution in highly noisy environment, long ranges and high dimension tracking.

Modern fighter aircraft using active electronically scanned array (AESA) fire control radars are able to detect and track targets at long ranges, in the order of 50 nautical miles or more. Low observable or stealth technology has contested the radar capabilities, reducing detection/tracking ranges roughly to one-third (or even less, for fighter aircraft radar). Hence, infrared search and track (IRST) systems have been reconsidered as an alternative to the radar. This study aims to explore and compare the capabilities and limitations of these two technologies, AESA radars and IRST systems, as well as their synergy through sensor fusion.

The AESA radar range is calculated with the help of the radar equation under certain assumptions, taking into account heat dissipation requirements, using the F-16 fighter as a case study. Concerning the IRST sensor, a new model is proposed for the estimation of the detection range, based on the emitted infrared radiation caused by aerodynamic heating.

The maximum detection range provided by an AESA radar could be restricted because of the increased waste heat which is produced and the relevant constraints concerning the cooling capacity of the carrying aircraft. On the other hand, IRST systems exhibit certain advantages over radars against low observable threats. IRST could be combined with a datalink with the help of data fusion, offering weapons-quality track.

An original approach is provided for the IRST detection range estimation. The AESA/IRST comparison offers valuable insight, while it allows for more efficient planning, at the military acquisition phase, as well as at the tactical level.

The need for a realistic aerial target for air defence weapon systems is universally accepted. In these days of ever escalating costs the use of a full size manned aircraft or highly sophisticated drone becomes increasingly less attractive. It is to provide a realistic, low cost and cost effective alternative that the Aero Electronics Ltd., target range has been developed.

This paper aims to target tracking in the marine environment is typically obtained by considering the measurement parameters like frequency, elevation and bearing. Marine environmental surveillance provides critical information and assistance for the exploitation and maintenance of marine resources.

With the use of intelligent sensor techniques like Hull-mounted and towed array sensors, convenient, precise and dependable three-dimensional (3D) underwater target tracking is introduced.

This research investigates a method to develop a reliable Unscented Kalman Filter (UKF) algorithm for enhanced underwater target tracking in a 3D scenario by using bearing, frequency and elevation measurements. In applications for underwater target tracking, uncertainty and inaccuracies are typically described by using Gaussian additive noise.

The proposed UKF algorithm is tested and analyzed using 100 Monte Carlo simulations with the Gaussian generated noise.

The purpose of this study is twofold. First, to estimate an optimal inflation rate for Ghana and second, to investigate factors that account for the differences between observed and target inflation.

The paper explored the questions within two econometric frameworks, the Autoregressive Distributed Lag (ARDL) and Threshold Regression Models using data spanning the period 1965–2019.

The study estimated a range of 5–7% optimal inflation for Ghana. While this confirms the single-digit inflation targeting by the Bank of Ghana, the range is lower than the central bank's band of 6–10%. The combined behaviours of the central bank, banks and external outlook influence inflation target misses.

The study urges the central bank to continue pursuing its single-digit inflation targeting. However, it implies that there is still room for the Bank to further lower the current inflation band to achieve an optimal outcome on growth and welfare. Again, the Bank should commit to increased transparency and accountability to enhance its credibility in attaining the targeted inflation.

The study is one of the first attempts in Africa in Ghana to estimate an optimal inflation target and investigate the underlying factors for deviation from the targets.

The purpose of this paper is to summarize the existing point cloud target detection algorithms based on deep learning, and provide reference for researchers in related fields. In recent years, with its outstanding performance in target detection of 2D images, deep learning technology has been applied in light detection and ranging (LiDAR) point cloud data to improve the automation and intelligence level of target detection. However, there are still some difficulties and room for improvement in target detection from the 3D point cloud. In this paper, the vehicle LiDAR target detection method is chosen as the research subject.

Firstly, the challenges of applying deep learning to point cloud target detection are described; secondly, solutions in relevant research are combed in response to the above challenges. The currently popular target detection methods are classified, among which some are compared with illustrate advantages and disadvantages. Moreover, approaches to improve the accuracy of network target detection are introduced.

Finally, this paper also summarizes the shortcomings of existing methods and signals the prospective development trend.

This paper introduces some existing point cloud target detection methods based on deep learning, which can be applied to a driverless, digital map, traffic monitoring and other fields, and provides a reference for researchers in related fields.