MS Unmanned Systems - Embry Riddle Aeronautical University
7.4 - Research Assignment: Sense and Avoid Sensor Selection
US Air Force Multi Sensors Data Integration for Autonomous Sense and Avoid
US
Air Force, through its sponsored Northrop Grumman Corporation, have developed
and tested autonomous systems with sense and avoid (SAA) capacity. The design
is scalable and based in “a comprehensive sensor suite comprising Traffic Alert
and Collision Avoidance System (TCAS) and Automatic Dependent Surveillance -
Broadcast (ADS-B) for detecting cooperative intruders as well as radar and electro-optical
(EO) sensors for detecting non-cooperative intruders” (US Air Force, 2011).
The
research is focused on the integration of data of the sensors, as part of the
whole sense and avoids system.
Integration
of sensor data
“Multi-Sensor
Integrated Conflict Avoidance (MuSICA) and consists of four major modules:
Sensor Input Management (SIM), Sensor Data Integration (SDI), Jointly Optimal
Conflict Avoidance (JOCA), and Flight Control Interface (FCI)” (USAF, 2011)
The
module SIM consists in a pre-processing stage from the sensor of SA; the module
SDI receives the data from the SIM and after processing, the output is an
integrated tracking data about intruder. The module JOCA receives the processed
data from SDI to analyze the situation and generate maneuver commands if it’s
necessary to control the collision avoidance. These commands are received by
the FCI which acts as a human pilot over the Flight control system of the
vehicle. The architecture of the SAA
shows that the sense is addressed by the modules SIM and SDI, and the modules
JOCA and the flight control interface addresses the avoid process.
Characteristics
of the sensors for SAA
The
suite of sensors for SAA involves different types of sensors such as
“cooperative and non-cooperative as well as active and passive” (USAF, 2011). Cooperative
sensors are considered omnidirectional and includes sensors TCAS and ADS-B,
Non-cooperative are considered directional and at the same time can be divided
in active (Radar/ LIDAR) or passive (EO/IR). “TCAS is an airborne secondary
surveillance radar (SSR) system with surveillance and collision avoidance functions;
An ADS-B-equipped aircraft broadcasts its own position and associated accuracy
and integrity information to other ADS-B-equipped aircraft and ground receivers”
(USAF, 2011).
Normally,
the positions of UAVs are determined by a Global Positioning System (GPS) and
altimeter by sensors of pressure; therefore, ADS-B is not strictly required for
all national space of the USA. TCAS expects for acknowledgements from the
receiver vehicle unlike the ADS-B, but both are cooperative sensors. At
commercial level, the active radar sensors for UAVs are not available yet, but
some laboratories such as MIT Lincoln Laboratory, has developed the “Airborne
Sense and Avoid (ABSAA) Radar Panel” (MIT, 2014). EO/IR sensors are
non-cooperative passive sensors which are mainly used for ground
operations.
SDI-
Objective of the Design
The
objective of the design of sensors data integration, as its name states, is the
integration of the multiple sensors with dissimilar characteristics with the
purpose to provide a high precision and hardy tracking of intruder for conflict
and avoidance of collision. Sub goals or specific objectives are focused on
integration of the all-suite of sensors, generation of integrated tracking,
getting better “features of dissimilar sensors in terms of accuracy, integrity,
continuity, and availability (AICA) in the integrated tracks, and Achieve
modular and efficient real-time software implementation” (USAF, 2011).
SDI
- Architecture
Sense
data integration architecture is composed of four functional modules “Extended
Kalman Filter (EKF), Data Association, False Track Filtering (FTF), and Track
Manager“(USAF, 2011).
The
internal flow starts with the input of measurements from multiple sensors to
the Track Manager; the module EKF is the responsible to propagate the tracking
which were predicted and update them by addition of the updated tracking; the
data association module synchronize measures with predicted tracks; then, the
Track Manager analyze and establish the new data base of tracks and the
initiation of new tracks or termination of old tracks; the FTF track
establishes if the detection or sensing of some track is true or false; and
finally the output of the updated track is declared to the function of
avoidance of the collision.
References
MIT Lincoln Lab's
Airborne Sense and Avoid (ABSAA) Radar Panel Wins R&D Award - UAS VISION.
(2014). Retrieved July 18, 2016, from
http://www.uasvision.com/2014/08/27/mit-lincoln-labs-airborne-sense-and-avoid-absaa-radar-panel-wins-rd-award/
Multi-Sensor Data
Integration for Autonomous Sense and Avoid. (2011). Retrieved July 18, 2016, from
http://arc.aiaa.org/doi/pdfplus/10.2514/6.2011-1479
