Unmanned aerial vehicles (UAVs) have significantly disrupted the aviation industry. As technology and policy continue to develop, this disruption is only going to increase in magnitude. A specific technology poised to escalate this disruption is UAV swarm. UAV swarm has the potential to distribute tasks and coordinate operation of many UAVs with little to no operator intervention. This paper surveys literature regarding UAV swarm and proposes a swarm architecture that will allow for higher levels of swarm autonomy and reliability by utilizing cellular mobile wireless communication infrastructure. This paper chronicles initial testbed development to meet this proposed architecture. Focused development of UAV swarms with UAV-to-UAV communication autonomous coordination ability is central to advancing the utility of UAV swarms. The use of cellular mobile framework alleviates many limiting factors that hinder the utility of UAVs including range of communication, networking challenges, and size-weight-and-power considerations. In addition, cellular networks leverage a robust and reliable infrastructure for machine to machine communication proposed by 5G systems
Small unmanned aircraft systems (sUAS) have become an attractive vehicle for a myriad of commercial uses. The ability of sUAS to bring payloads for utility, sensing, and other uses into the sky without a human pilot on board is an attractive proposition. With manned aviation, there is the risk of injury or fatality should a critical error occur in flight. With an unmanned aircraft system, these concerns are alleviated. Manned aviation is expensive. The price to purchase or rent a general aviation aircraft is prohibitive. Pilot labor, fuel costs, and maintenance are prohibitive expenses to the use of general aviation aircraft for widespread commercial applications. For these reasons, the utility of sUAS has been an attractive alternative. There are also many advantages for unmanned aircraft in military applications; however, this paper focuses predominantly on private sector commercial applications.
Though the utility of sUAS has budded a growing industry, the capability of swarms of UAVs is an intriguing development that is only in its infancy. Limitations to traditional operation of sUAS are that they have a limited payload, limited flight time, and require a remote pilot to operate them through a handheld transmitter or computer with appropriate control software. Coordinating multiple UAVs to perform tasks in a swarm environment is attractive because it addresses the limitations of. a single sUAS while adding more functionality.
Unmanned Aerial Vehicles (UAVs) with acceptable performance are becoming commercially available at an affordable cost. Due to this, the use of drones for real-time data collection is becoming common practice by individual practitioners in the areas of e.g., precision agriculture and civil defense such as fire fighting. At the same time, as UAVs become a house-hold item, a plethora of issues—which can no longer be ignored and considered niche problems—are coming of age. These range from legal and ethical questions to technical matters such as how to implement and operate a communication infrastructure to maintain control over deployed devices. With these issues being

addressed, approaches that focus on enabling collectives of devices to operate semi-autonomously are also increasing in relevance. In this article we present a nature-inspired algorithm that enables a UAV-swarm to operate as a collective which provides real-time data such as video footage. The collective is able to autonomously adapt to changing resolution requirements for specific locations within the area under surveillance. Our distributed approach significantly reduces the requirements on the communication infrastructure and mitigates the computational cost otherwise incurred. In addition, if the UAVs themselves were to be equipped with even rudimentary data-analysis capabilities, the swarm could react in real-time to the data it generates and self-regulate which locations within its operational area it focuses on. The approach was tested in a swarm of 25 UAVs; we present out preliminary performance evaluation
The relatively young field of UAV-technologies has not yet agreed on a unified classification system encompassing all devices and there are many ways to categorize UAVs. For the foreseeable future regulatory bodies will continue to impose different regulations and classifications on UAVs and those regulations may be subject to frequent change and amendment. The use of UAVs in public airspace touches on a number technical and societal concerns and challenges. The available devices differ in manufacturer, type and capabilities and have been used for a wide variety of applications and missions. Some fixed wing UAVs can remain airborne for hours circling over certain areas while cheap off-the-shelf quadrotor currently have an expected flight-time of about 15 to 20 min. Special purpose UAVs can remain in the air for up to two weeks or longer and as technologies continue to improve these performance values are only expected to get better
Drones, although their almost persistent presence are still somewhat of a revolutionary technology in that their use and availability is spreading considerably faster than awareness about potential concerns or legislative frameworks to address these concerns.
For the foreseeable future different countries will continue to impose different regulations regarding the use of UAVs and those regulations may be subject to frequent change and amendment. We expect that these growing pains will be overcome in the years to come. Once there are solid regulations in place, the use of UAVs can become a regular and wide-spread practice. We believe that once that is the case, the benefits of operating swarms of devices will quickly become evident. This will lead to wide¬spread use of swarming applications for autonomously operating devices. The presented approach enables a swarm of devices to collaboratively coyer an area and provide continuous data quality for, for example, video coverage, even if the resolution requirements for individual locations are subject to change. The approach is scalable and the swarm used for the evaluation is already large enough to deliver good results; The success of the algorithm in real-world problems will depend critically on a good definition of device capabilities, task-properties and -synergies and these seem to be completely problem-dependent. Due to the mentioned legal and practical considerations

CLAIMS
1. UAV - unmanned aerial vehicles system methodology is to be launched from any space or Platform where vision sensor provides detail images during flying to Target on continuously and military capability to destroy the target,
2. UAV is attached with map-reading component like sensor for weapons direction-finding and weapons routing
3. UAV is attached with Environment sensors provides database of that identify territory features which support to identify the nature of bomb material.
4. UAV is provides information related to Target location, speed, position and miscalculation estimate.
5. UAV is attached with best finding morphology filter which provide the information related to target and weapon information along with their thoughts and mapping of weapon settings.
6. Drone swarm technology—the ability of drones to autonomously make decisions based on shared information—has the potential to revolutionize the dynamics of conflict
7. swarms will have significant applications to almost every area of national and homeland security
8. Drones could disperse over large areas to identify and eliminate hostile surface-to-air missiles and other air defenses. Drone swarms could potentially even serve as novel missile defenses, blocking incoming hypersonic missiles. On the homeland security front, security swarms equipped with chemical, biological, radiological, and nuclear (CBRN) detectors, facial recognition, anti-drone weapons, and other capabilities offer defenses against a range of threats.