scispace - formally typeset
Search or ask a question
Topic

Bounding overwatch

About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.


Papers
More filters
Journal Article
TL;DR: The Army's current attempt at digital command and control (C2) systems will allow better visualization of the battlefield than in the past, but a framework is needed to place its importance in perspective.
Abstract: DESPITE THE BEST EFFORTS of the staff, the plan was unraveling. The scouts reported the enemy moving forward into the security zone instead of staying where the situational template said they would defend from. This invalidated the projected direct and indirect fire plan. The task force commander would have to rely on his lead team commander to find the enemy then develop and issue verbal orders at that point. He felt helpless and unable to provide any other guidance to his lead commander. He was unable to visualize the changes that needed to occur to influence the battle later Battlefield visualization, a key component of battle command, is the process of visualizing the unit's current state and a future state (of mission success), formulating concepts of operations to get from one to the other at least cost, and articulating this sequence in intent and guidance.1 The Army's current attempt at digital command and control (C2) systems will allow better visualization of the battlefield than in the past. As commander of the 1-22 Infantry Battalion, 4th Infantry Division (ID) (Mechanized (M)), I had the opportunity to test and field Force XXI Battle Command Brigade and Below (FBCB2), which is a digital Battle Command Brigade and Below Control System. FBCB2 uses information-age technology to enable soldiers to receive, compare, and transmit situational awareness (SA) information more quickly than was previously possible and to send and receive C2 messages. FBCB2 transmits and receives data across the wireless Fixed Tactical Internet (FTI) via the Enhanced Position Location Reporting System (EPLARS) data radio and Single Channel Ground Air Radio System. Each FBCB2 derives its own location via the precision lightweight global positioning system receiver. Through these interfaces, the FBCB2 automatically updates and broadcasts its current location to all other FBCB2-equipped platforms. These radios also transmit and receive C2 messages such as orders, overlays, and reports. The FBCB2 computer is the heart of the system and comes with a keyboard, touch-sensitive screen, and removable hard-disk drive. The system is located inside the vehicle next to the platform commander. To describe the power of visualization that FBCB2 brings to battalion- and company-level units, a framework is needed to place its importance in perspective. Combat power and its elements provide this framework. Combat Power and Visualization Combat power is a commonly used term that describes the resource that commanders use to accomplish the mission. Field Manual (FM) 101-5-1, Operational Terms and Graphics, defines combat power as "the total means of destructive and/or disruptive force that a military unit/formation can apply against the opponent at a given time-a combination of the effects of maneuver, firepower, protection, and leadership."2 Field Manual 3-0, Operations, adds information as an element of combat power.3 Maneuver. Field Manual 3-0 describes maneuver as "the employment of forces, through movement combined with fire or fire potential, to achieve a position of advantage with respect to the enemy to accomplish the mission. Maneuver is the means by which commanders concentrate combat power to achieve surprise, shock, momentum, and dominance."4 FBCB2 allows the commander to visualize the effects of terrain, to plan for distributed movement and maneuver, and to monitor execution. The value of FBCB2 is particularly apparent in two instances of maneuver: the transition from movement to maneuver and the rapid concentration of forces. Using the FBCB2 enemy situational template and the circular line-of-sight tool, leaders can visualize the enemy's maximum engagement line and determine the location of the phase line that triggers the change in movement techniques from traveling or traveling overwatch to bounding overwatch. The commander can monitor the progress and formation of subordinate elements and view the transition as units make the appropriate changes. …

5 citations

Journal ArticleDOI
01 Jan 2022-Sensors
TL;DR: A vision-based tracking-by-detection framework is implemented to acquire each person’s clothing status, key posture, and bounding box information simultaneously simultaneously, proving the usefulness of the proposed method in a multi-person scenario of real-life applications.
Abstract: Satisfactory indoor thermal environments can improve working efficiencies of office staff. To build such satisfactory indoor microclimates, individual thermal comfort assessment is important, for which personal clothing insulation rate (Icl) and metabolic rate (M) need to be estimated dynamically. Therefore, this paper proposes a vision-based method. Specifically, a human tracking-by-detection framework is implemented to acquire each person’s clothing status (short-sleeved, long-sleeved), key posture (sitting, standing), and bounding box information simultaneously. The clothing status together with a key body points detector locate the person’s skin region and clothes region, allowing the measurement of skin temperature (Ts) and clothes temperature (Tc), and realizing the calculation of Icl from Ts and Tc. The key posture and the bounding box change across time can category the person’s activity intensity into a corresponding level, from which the M value is estimated. Moreover, we have collected a multi-person thermal dataset to evaluate the method. The tracking-by-detection framework achieves a mAP50 (Mean Average Precision) rate of 89.1% and a MOTA (Multiple Object Tracking Accuracy) rate of 99.5%. The Icl estimation module gets an accuracy of 96.2% in locating skin and clothes. The M estimation module obtains a classification rate of 95.6% in categorizing activity level. All of these prove the usefulness of the proposed method in a multi-person scenario of real-life applications.

5 citations

Journal ArticleDOI
TL;DR: Zhang et al. as mentioned in this paper proposed an IoU-aware Matching Adaptive Siamese Network (IMSiam) for visual tracking, which integrates multiple types of encoded feature maps and adaptively sample matching information to simultaneously perform target classification and bounding box regression.

5 citations

Journal ArticleDOI
TL;DR: In this article, an approximate dynamic programming approach is proposed to compute lower bounds on the optimal value function for a discrete time, continuous space, infinite horizon setting, iteratively constructing a family of lower bounding approximate value functions by using the so-called Bellman inequality.
Abstract: We describe an approximate dynamic programming approach to compute lower bounds on the optimal value function for a discrete time, continuous space, infinite horizon setting. The approach iteratively constructs a family of lower bounding approximate value functions by using the so-called Bellman inequality. The novelty of our approach is that, at each iteration, we aim to compute an approximate value function that maximizes the point-wise maximum taken with the family of approximate value functions computed thus far. This leads to a non-convex objective, and we propose a gradient ascent algorithm to find stationary points by solving a sequence of convex optimization problems. We provide convergence guarantees for our algorithm and an interpretation for how the gradient computation relates to the state relevance weighting parameter appearing in related approximate dynamic programming approaches. We demonstrate through numerical examples that, when compared to existing approaches, the algorithm we propose computes tighter sub-optimality bounds with comparable computation time.

5 citations


Network Information
Related Topics (5)
Robustness (computer science)
94.7K papers, 1.6M citations
85% related
Optimization problem
96.4K papers, 2.1M citations
85% related
Matrix (mathematics)
105.5K papers, 1.9M citations
82% related
Nonlinear system
208.1K papers, 4M citations
81% related
Artificial neural network
207K papers, 4.5M citations
80% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023714
20221,629
2021155
202075
201973
201850