Integrated End-to-End Radar Signal & Data

This paper provides information related to integrating Knowledge Based (KB) techniques within the filtering, detection, tracking and target identification portions of an airborne radar’s processing chain. We will present multiple information sources and how they can be used to enhance a radar’s performance for end-to-end signal and data processing. Introduction In our previous paper we presented material for understanding some of the basic elements regarding knowledge bases and artificial intelligence (AI). In this paper we wish to present a design of an intelligent airborne radar system that processes information from the end-to-end, i.e. filter, detector and tracking stages of a surveillance radar. Can we build new radar systems that can dynamically change its processing given information from other sensors, outside sources, weather data, etc.? We believe that we can. The computing clock rates for computers have been doubling approximately every 18 months. Today’s commercial off the shelf computers have clock rates exceeding 3 GHz. We believe that the computing power is available to insert sophisticated “rules/logic” within radar signal and data processing. The following section will pick up where we left off in our first paper, dealing with ontologies. A global view of interfacing multiple platforms of sensors and the integration of sensors on one platform will be discussed. The next section will describe the major knowledge base components of an airborne intelligent radar system (AIRS). The next section will provide an overview of how the AIRS processes data within different states. The following section will provide a knowledge base tracking algorithm with memory thereby providing information helpful for target identification and terrain resolution. The last section provides our summary. A Global View The performance of our sensor systems can be enhanced by dynamically controlling a sensor’s algorithms dependent upon a changing environment. The sharing of information in real time with other sensors is also a major plus. It has been shown in this lecture series that if an airborne radar system knows about certain features of the Earth (e.g. land sea interfaces) and its surroundings then it can use this information intelligently and increase its performance. A radar system can perform better with information from other sensors, e.g. sensor fusion. It could perform better if it knew where potential jammers were located and their characteristics. However, if an airborne radar is going to share and receive information from multiple sources then it must be able to communicate and understand the information. A solution for the exchange of information between heterogeneous sensors is for each sensor to publish information based upon shared ontologies. In this manner when a sensor publishes its track data multiple sensors receiving this information will be able to interpret its contents without ambiguity. Accomplishing this will require that certain basics be established. We must have an accepted method of defining the Earth’s geometry such that every element on the Earth, air Paper presented at the RTO SET Lecture Series on “Knowledge-Based Radar Signal and Data Processing”, held in Stockholm, Sweden, 3-4 November 2003; Rome, Italy, 6-7 November 2003; Budapest, Hungary, 10-11 November 2003; Madrid, Spain, 28-29 October 2004; Gdansk, Poland, 4-5 November 2004, and published in RTO-EN-SET-063. Integrated End-to-End Radar Signal & Data Processing with Over-Arching Knowledge-Based Control 8 2 RTO-EN-SET-063 or space’s positions are all defined within the same coordinate system. That each element is time synchronized with the same clock and all communications are time stamped. Each transmission of information between sensors must depict its time and its coordinates. In addition if it is sharing track or target data it must specify their unique identifier, its velocity, pitch, yaw, and role and meta data describing the transmitted raw data along with encryption/decryption keys. The unique identifier will allow the receiving sensor to acquire, within its resident database management system (DBMS), all of the sender’s radar characteristics. The description of these data can be defined by ontologies such that all the sensor platforms will correctly understand the information provided. Sensor characteristics include such things as nomenclature, power output, bandwidth, frequency, antenna pattern, pulse width, pulse repetition frequency (PRF), etc. Platform characteristics as to the position of the antenna on the platform, number of elements, the pattern of the elements, the pointing vector of the radar, etc. We need an ontology for defining these data and numerous rules so that the information published by any sensor can be understood correctly by the receiving sensor to perform functions such as sensor fusion, track correlation, and target identification. Sharing information between sensors on the same platform is also required, especially if one or more sensors are adaptively changing its waveform parameters to meet the demands of a changing environment. Figure 1 depicts a hypothesized intelligent sensor system. Each of the sensors has its own signal and data processing functional capability. In addition to this capability we have added an intelligent processor to address fusion between sensors, communication between sensors, and control of the sensors. The goal is to be able to build this processor so that it can interface with any sensor and communicate with the other sensors using ontological descriptions via the intelligent platform network. The intelligent network will be able to coordinate the communications between the sensors on board and to off platform sensor systems. There are approaches we can exploit to build this system by using fiber optic or wire links on board the platform. Radio frequency (RF) links using Bluetooth or 802.11 technologies can be exploited for linking these sensors on board the platform. Between platforms other technologies may be exploited such as mobile internet protocol over RF communications links. The communications issues need to be addressed for the sharing of information and for minimizing the potential of electromagnetic (EM) fratricide. The intelligent platform should determine if there is EM interference (EMI) potential when a sensor varies their antenna’s main beam pointing vector, or changes its PRF and may thereby cause interference to a receiving sensor. Rather than have each sensor on a platform operate as an independent system we need to design our platform as a system of sensors with multiple goals managed by an intelligent platform network that can manage the dynamics of each sensor to meet the common goal(s) of the platform. This is one of the major goals we are pursuing under our sensors as robots initiative. This initiative is addressing attended and un-attended sensor platforms. Integrated End-to-End Radar Signal & Data Processing with Over-Arching Knowledge-Based Control RTO-EN-SET-063 8 3 KB Signal And Data Processing Intelligent Fusion Comm. Control Plug & Play snsor KB Signal And Data Processing Intelligent Fusion Comm. Control Plug & Play se ns or KB Signal And Data Processing Intelligent Fusion Comm. Control Plug & Play se ns or Off Platform Sensors Off Platform Sensors KB Signal And Data Processing Intelligent Fusion Comm. Control Plug & Play snsor RF Intelligent Platform Network An Intelligent Sensor System