SkyWatch: A Passive Multistatic Radar Network for the Measurement of Object Position and Velocity

Abstract

Quantitative three-dimensional (3D) position and velocity estimates obtained by passive radar will assist the Galileo Project in the detection and classification of aerial objects by providing critical measurements of range, location, and kinematics. These parameters will be combined with those derived from the Project’s suite of electromagnetic sensors and used to separate known aerial objects from those exhibiting anomalous kinematics. SkyWatch, a passive multistatic radar system based on commercial broadcast FM radio transmitters of opportunity, is a network of receivers spaced at geographical scales that enables estimation of the 3D position and velocity time series of objects at altitudes up to 80[Formula: see text]km, horizontal distances up to 150[Formula: see text]km, and at velocities to [Formula: see text][Formula: see text]km/s ([Formula: see text][Formula: see text]Mach). The receivers are designed to collect useful data in a variety of environments varying by terrain, transmitter power, relative transmitter distance, adjacent channel strength, etc. In some cases, the direct signal from the transmitter may be large enough to be used as the reference with which the echoes are correlated. In other cases, the direct signal may be weak or absent, in which case a reference is communicated to the receiver from another network node via the internet for echo correlation. Various techniques are discussed specific to the two modes of operation and a hybrid mode. Delay and Doppler data are sent via internet to a central server where triangulation is used to deduce time series of 3D positions and velocities. A multiple receiver (multistatic) radar experiment is undergoing Phase 1 testing, with several receivers placed at various distances around the Harvard–Smithsonian Center for Astrophysics (CfA), to validate full 3D position and velocity recovery. The experimental multistatic system intermittently records raw data for later processing to aid development. The results of the multistatic experiment will inform the design of a compact, economical receiver intended for deployment in a large-scale, mass-deployed mesh network. Such a network would greatly increase the probability of detecting and recording the movements of aerial objects with anomalous kinematics suggestive of Unidentified Aerial Phenomena (UAP).

Enhancing Home Security with AI-Driven Drone Surveillance

“…In order to detect and respond to threats more accurately, we may also use sophisticated AI algorithms. [9] To process data more efficiently, we can do so by utilizing affordable cloud-based services. By addressing these major issues, the system will become more responsive and effective in smart home security with drone surveillance.…”

“…Garg A. et. al.,[9] proposed a shadow preservation framework enhancing the content-aware image retargeting process, contributing to improved image quality and visual appeal. The proposed framework, validated through quantitative evaluations and visual comparisons, demonstrates its efficacy in advancing content-aware image retargeting applications.…”

“…The main goal of the project is to use AI-powered drones to identify and react to security threats in real-time, thereby addressing the shortcomings of conventional home security systems. [9] The drones, which are outfitted with advanced sensors and cameras, are able to independently detect irregularities and possible invasions while patrolling certain areas. The drones identify security threats and immediately notify homeowners and security staff, allowing for focused and quick response.…”

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

“…Multistatic radars expand on the bistatic concept by utilizing multiple receivers and, in some cases, multiple transmitters [77]. This configuration offers enhanced coverage and detection capabilities, as multiple perspectives on the target can reveal its position and movement with greater accuracy and robustness against countermeasures [78][79][80][81][82]. Multistatic setups can effectively detect stealth aircraft, which are designed primarily to evade monostatic radars, by exploiting the varied angles of incidence and reflection captured by the dispersed receivers.…”