electromagnetic ports associated with a complex system. This is usually the first ... integrated, environmental monitoring tool with application to. EMC problem ...
Application and Demonstration of a Knowledge-Based Approach’ to Interference Rejection for EMC Andrew Drozd
Anthony Pesta Donald Weiner, Pramod Varshney & Ilteris Demirkiran
ANDRO Consulting Services AFRL/IFSA P. 0. Box 543 525 Brooks Road Rome,NY 13442-0543 Rome, NY 13441-4505
Department of Electrical Engineering & Computer Science Syracuse University Syracuse, NY 13244
Abstract: This paper discusses the application and demonstration of a knowledge-based approach to performing interference cancellation for a collection of RF spread spectrum, tiequency hopped transceivers co-located on an airborne platform. An expert system pre-processor is used to set up the initial problem and in particular, to provide a fully-validated geometry which is used to compute geodesic losses in the frequency domain. The expert system is then used to “monitor” the signal environment in the time domain and select the interference rejection scheme(s) appropriate for mitigating the effects of interferers present at a victim receptor port. The problem is that a single interference rejection scheme cannot be expected to suppress all types of interference that may be present. Computer simulations were conducted to demonstratethe effectivenessof the approach for several interference scenarios using the Electromagnetic Environment Effects Expert Processor with Embedded Reasoning Tasker (E3EXPERT). This work was sponsored by the US Air Force ResearchLaboratory/IFSA under Contract F30602-97-C-0 162.
E3EXPERT was specifically designed to study the complex interference rejection problem for co-located spread spectrum transceivers. The expert system pre-processing stage employs rules to (a) generate a representative computational model; (b) invoke the intrasystem EMC analysis engine; (c) compute frequency-domain transfer functions and interference margins; and (d) relay results to the intelligent post-processor stage for the application ofan appropriate interferencerejection scheme.
Introduction
The pre-processor EMC analysis engine is based on a modified version of IEMCAP. IEMCAP is a conservative, system-level EMC analysis tool which computes coupling and interference in the frequency domain for antenna, wire. and equipment electromagnetic ports. Certain experimental modifications were made to this engine to more accurately calculate geodesic path isolation for RF antenna-to-antenna and external field coupling.
Spread spectrum communications systems are typically used to send/receive coded voice modulated RF signals and digital pulse information. Consequently, a spread spectrum transceiver can be both an emitter and a receptor of electromagnetic energy. The immediate problem of interest is the co-location of a number of these electromagnetic transceivers on an airborne vehicle. The vehicle in this scenario functions as a “surrogate” host platform that relays coded information to/tim other “nodes” in a communications network to support airspace dynamic information exchange. However, the airborne-mounted spread spectrum systems are not necessarily intended to communicate with each other over any given hop cycle. These systems, for the most part, are meant to send and receive voice or data signals with ground stations and/or other airspace vehicles. Undesired coupling among co-located spread spectrum systems may therefore compromise information integrity and desired processes between intentional communications nodes.
System EMC Culls A conservative EMC culling analysis is an effective means of quickly identifying EM1 problems for a large collection of electromagnetic ports associated with a complex system. This is usually the first step in the analysis methodology. Tools such as the Intrasystem Electromagnetic Compatibility Analysis Program (IEMCAP) can be used for this purpose [ 1. 21. The use of IEMCAP, for example, is meant to reduce the large matrix of electromagnetic interactions down to a manageablesubset.
E3EXPERT Features The concept underlying E3EXPERT is the application of artificial intelligence to solve sophisticated EMC problems such as the scenario of co-located spread spectrumtransceivers on an airborne platform. Since the resultant electromagnetic environment is highly complex eflicient methods are required to sift through and identify EM1 conditions in both the time and frequency domains, rank the severity of predicted interference. classify the dominant interference sources by type (i.e.-. broadband modulation, CW, harmonic. etc.), and select an appropriate interference canceller. The effectivenessof these steps first depends upon generating a valid electromagnetic analysis model.
A collection of pseudo-random frequency hoppers produces a very complex electromagnetic environment which in general, is comprised of CW and wideband noise, nonlinear signals (e.g., receiver and transmitter intermods, cross mods. etc.), harmonics. E’EXPERT is configured as a runtime capability operating on a and noise impulse responses in RF front-end filters. The complex Windows NT personal computer. It ef&tively integrates a and random nature of the problem presents a challenge to the commercial espert system; a Windows-based graphical user system designer and EMC analyst. The computer modeling and interface (GUI) containing pull-down menus and pop-up dialog simulation task for this type of problem is a formidable boxes for user data/command en”: and a 5D viewer/renderer undertaking. To address this. a knowledge base approach was incorporating agraphical editor which utilizes a 3D metafile data developed and implemented as a state-of-the-art prelpost- format. The editor provides a primary man-machine interface (MMl)to graphically manipulate displayed geometry models. processor called E’EXPERT.
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The system is totally operated from a Windows-based user control r, =(xX2+ yX2)‘” panel. This environment supports event-driven pre-processing where the user selects the menus, options, etc. during the r, = (xr2+ Y?)“~ modeling and simulation task. Pull-down menus are invoked to call the viewer/editor and the expert system knowledge base. For where the inexperienced user, an optional “Wizard-like” Assistant is madeavailable which can walk the user through the key sequence xX= Transmitter x-coordinate of steps involved in the modeling, simulation, and analysis tasks. yX = Transmitter y-coordinate xr = Receiver x-coordinate Neuron Data’s Intelligent Reasoning Element (IRE) expert system yr = Receiver y-coordinate. is the basis ofthe intelligent pre-processor stage. It automatically fires rules to assist in the model generation process using selected Next, each ofthe rx and rr computed values for every antenna and CAD file formats. The pre-processor also accepts user-specified all endpoints are comparedto a fuselage radius tolerance interval RF port characteristics, applies smart defaults, and performs basic defined as geometrical object integrity checks. +/-0. IO(rf) The post-processor or Embedded Reasoning Tasker (ERT) stage applies a knowledge-based signal processing philosophy to where rris the fuselageradius which is derived from the CAD file EMC for the interference rejection problem. A knowledge-based entity dimensions or which may be user defined. interference rejection test bed was implemented to demonstrate proof ofconcept and to illustrate the benefits to be achieved. The ERTRules for Inte@erenceRejection post-processor utilizes the Integrated Processing and Understanding of Signals (IPUS) Ci+ Platform (ICP) and the A fundamentalrule base was also implemented and tested within Signal Processing Workstation (SPW). ICP and SPW provide an the ERT post-processor knowledge base to focus on applying integrated, environmental monitoring tool with application to the appropriate interferencerejection schemesfor a representative range of spread spectrum communications transceivers and EMC problem solving. environmental signal types (known and estimated). This can be SPW is an integrated software package for designing signal extended to address various types of frequency-hopped processing systems. Its vast communications systems and digital transceivers, non-average power states, wideband jamming signal processing library, graphical design methodology, fast environments, intermods, and so on. The corresponding rules are simulator, test and analysis facility, and implementation options derived from the full IPUS/ICP system. These are further make SPW an acceptable choice for concept-to-prototype design. discussed below. SPW can be used to model and simulate a system, then debug, revise and resimulate it until optimal results are obtained. EMC Engineering Model Enhancementsfor Improved Accuracy Generating Validated GeometryModels Translators have been developed to convert Initial Graphical Exchange Specification (IGES) Computer-Aided Design (CAD) data into corresponding electromagnetic system models. Rules are applied to determine geometric “best-fit” routines in order to represent a CAD model within the limits prescribed by the “canonical” modeling guidelines of the electromagnetics engine. In all cases,resulting data descriptions conform to the 3D metafile format,structure, and internal storage requirements.
In developing the electromagnetics engine, certain modifications were made to the IEMCAP geodesic path loss models based on enhanced formalisms resident in the Aircraft Inter-Antenna Propagation with Graphics (AAPG) computer program [3-61. These geodesic models are based on methods rooted ln Geometric Optics (GO) per W. R Hamilton; the Geometrical Theory of Diffraction (GTD), an extension of GO attributed to J. B. Keller; and the Uniform Theory of Diffraction (UTD), an enhancementof GTD and my tracing formalisms by Kouyoumjian and Pathak, which is currently implemented in reduced form in IEMCAP. Take the equation for power density (Pu) which is expressedas:
Pre-Processor Knowledge Base Rules Po=(Pr+LCT+Gr)+(LWS+LCS+LKE+LBH) A basic rule set was written for the intelligent pre-processor stage to validate the system structure for intra-connectivity as well as verify the attachment of antennas to the exterior surface. Checks for antenna attachment are performed for both flat polygons (plates or wings) and curved surfaces (fuselage or circular cylinder). The rules determine (a) the type of surface, (b) its boundary dimensions, and (c) the relative placement of the ‘antennas in both Cartesian (x, y, z) and cylindrical (I; 8, z) coordinate systems. For each antenna element and its “endpoints” a series of calculations is performed to compare Cartesian coordinates and to rapidly determine offsets on the flat surfaces. Nest, the geometry problem is converted into the cylindrical coordinate system where the primary test is based on comparing the r, and r, for transmitter and receiver radial components.respectively. These terms are computed based on the formulas:
where = Transmitter power (dBm) LCT = Transmitter-to-antennacabling loss (dB) GT = Transmitter antenna gain (dBi) LWS = Wave-spreading loss LCS = Curved surfacediffraction loss (dB) LKE = Knife-edge diffraction loss (dB) LBH =Bulkhead diffraction loss (dB).
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These losses represent the diffracted ray amplitude change over the complete coupling path on the alrhame. relative to the amplitude change over a free space path. Several of these terms were incorporated within IEMCAP to improve its computational accuram *’
A more rigorous approach to enhance IEMCAP’s geodesic calculations and eliminate its inherent geometrical modeling limitations is to directly integrate a GTD code e.g., the GTD module that is part of the General Electromagnetic Model for the Analysis of Complex Systems (GEMACS) computer code. This approach will provide more accurate solutions to the surface reflection and diffraction losses. Rendering Geometric % Electromagnetic Structure Models
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Eliminating database management system speed and performance limitations associated with real time look-up, retrieval, and data transactions. Extensibility to include custom object definitions, attributes, and properties. Supports a wide range of relevant object types and data attributes in retained (persistent) mode as opposed to immediatemode (non-persistent). Theability to readily set relationships between metafile and expert systemobjects. Providing a basis for a “common” data environment for collaborative engineering applications.
A 3D viewer is opened ticm a pull-down menu to display the actual system model in a “Document Window”. A “Results Window” is automatically opened which simultaneously Knowledge-Based Interference Rejection Capability displays the corresponding electromagnetic structure model used for the electromagneticsengine. The latter is rendered and treated as asmooth GTD surfacemodel. This is shown in Figure 1. This When complete information regarding the interference is figure emphasizes the two views of the geometric model unavailable, the post-processor expert system provides the (Document left, Results right) inset within the main Windows receptor with a knowledge-based capability to monitor the environment. The result ofrule fling associated with model error environment and determinethe interferenceprocess along with all detection and correction is also shown where Antennas #l and #2 necessaryparameters. Based on a set of expert system rules, the are disconnected tirn the aircraft surface. The expert system knowledge-based processor selects one or more suitable suggests a built-in solution whereby these antenna are translated interferencerejection schemes.The selection is madefrom a library to the aircraft surface using distance minimization criteria The of preselectedtechniques, In effect, the system reacts to the EM Results Window also shows that the tail stabilizer regions are interferenceenvironment so as to maximize performance. IPUS is absent loomthe problem. This may be due to the user editing out primarily used for applications where uncertainties exist about the these geometry objects via the 3D graphical editor as these may signal environment. The IPUS expert system was developed with not have been considered crucial to the analysis. However, the support t?omthe US Air Force Rome Laboratory by Victor Lesser reasonfor eliminating the tail region stabilizers in this caseis that of the University of Massachusetts and Hamid Nawab of Boston their effectsare unaccounted for in the IEMCAP. University. l
Figure 1. User Control Panel & Dual Display Windows. Additionally, a rudimentary four of displaying antenna-t-antenna When the environment is unknown, attempts at measuring coupling vectors (geodesic paths), antenna maximum gain properties and/or parametersof signals can result in distorted pointing directions, and omni-directional gain patterns is outputs. For example,use of an FFT with inadequate resolution provided. The coupling vectors at the present time are not fully will result in the incorrect interpretation of two signals that are implemented. The graphical display is dependent upon vector closely spacedin frequency as a single signal. Use of an amplifier quantities that are computed by the IEMCAP-based engine. with inadequate dynamic range will result in distortion of a strong signal due to nonlinear effects. Use of a receiver with The ability to render and edit the model is based on manipulating inadequatebandwidth will result in distortion of a signal whose the internal 3D metafile data The 3D metafile format provides ‘spectrum is wider than the receiver bandwidth. Nevertheless, manv important advantages over the traditional hierarchical and traditional signal processing systems typically accept measured reiat’ional databasedesign approach. These include: signals without questioning whether or not they may have been
distorted by the front-end stages of the measurementsystem. IPUS not only allows the measurementsystem to interpret the essential characteristics of monitored signals, but also recognizes when uncertainties and/or distortions exist and reprocesses the monitored signals so as to reduce uncertainties and distortions. Once discrepancies are detected, IPUS selects strategies to reprocess the data by changing the parameters of the signal processing algorithms and/or selecting new algorithms. The process iterates until interpretations have been generated that resolve the discrepancies. This approach is well suited to determining the characteristics of incompletely known interfering signals. Even when the interference is completely known at the receptor, the knowledge-based processor can be used to select an appropriate interference rejection scheme. Time-Domain Signal Processing Code Although average power considerations are useful in the design of systemsfor EMC, they do not provide a complete picture with respect to interference rejection schemes. The knowledge-based approach assumes a detailed time-domain description of the interference at each receptor. Using SPW, systems can be completely characterized in the time domain. Receptors are modeled in terms of functional blocks such as mixers, amplifiers and detectors. Emitted signals are generated in the time domain as are the responses of receptors to both intended and unintended signals. This enables the system designer to realistically evaluate both system degradation due to interference and the effectiveness ofvarious rejection schemes.
manually inserted into the post-processor. It is intended that the next version of E3EXPERT will be more fully integrated with an automatic data transfer capability between processing stages. To demonstrate proof of concept, an EM1 problem was simulated using the SPW software discussed in the previous section. Communication between frequency hopping radios was selected as the scenario to be demonstrated. Frequency hopping radios are primarily used in military communications. They are also under consideration tir commercialapplications. These radios typically operate at very high radio frequencies, for example, between 30 and 88 MHz. However, in order to efficiently demonstrateproof of concept, the simulation made use of radio frequencies in the kHz range. In the next phase of this effort, analytical results will be obtained that will enable all radio transmitter and receiver operations to be translated to basebandwhile still maintaining an accuratesimulation of the RF equipments.
The communication system that was simulated is shown in Figure 2. It includes a desired frequency hopping FM transmitter communicating with an intended receiver. The transmitter sends random binary data with values of 21 at a rate offour messagebits per unit time. Frequency shift keying with a frequency deviation of 248 Hz is employed. In actual RF transceivers, a very large set ofhopping frequenciesis used. However, in the proof of concept demonstration, use is made of four hopping frequencies which were selectedto be 288 Hz, 528 Hz, 1,568 Hz, and 1,744 Hz. Four frequency hops are performedduring each messagebit. Thus, a fast hopping situation is simulated with a hopping rate of 16 chips per unit time. The hopping pattern is generated by PN code generators of order 3. The signal to be sent is passed through a Receptor Susceptibility Models nonlinear RF amplifier prior to transmission. The nonlinearity is Receptor susceptibility curves based only on average power of the form y = alx + a$’ so that a third harmonic is transmitted provide incomplete information with regard to performance along with the fundamental carrier. Assuming an impedance of degradation due to interfering signals and the effectiveness of 100 ohms, al was chosen to yield a desired transmitted signal at candidate interference rejection schemes. For example,the power 20 watts while a3was selected such that the third harmonic power spectral density of an ON-OFF frequency hopped sinusoidal was down f?om the carrier power by 60 dB. Hence, the carrier enables averagepower calculations for specitied frequency fundamental and third harmonic voltage levels were 63.2V and bands. However, it does not enable reconstruction of the time- O.O632V,respectively. domain waveform. Clearly, the performance of a receptor may depend upon both when the interference in ON and the carrier In the simulation two additional frequency hopping FM frequenciesat specific ON times. In addition, the effectivenessof transmitters were inserted to act as potential interferers. These are proposed interference rejection schemes may depend on the similar to the desired FM transmitter. However, they utilize Improved different sets of hopping frequencies along with a frequency detailed nature of the time-domain waveform. susceptibility models which are a function of the time-domain deviation of 264 Hz. As with the desired transmitter, their nature of the interference and the time-domain response of the hopping rates are 16 chips per unit time. However, their hopping receptor will be discussed. patterns are generatedby PN code generators of different orders so as to be asynchronized with each other as well as with the desired Proof of Concept Demonstration transmitter. Both interferers contain nonlinear RF amplifiers that give rise to potentially interfering signals of 20 watts at the As discussed previously, E3EXPERT is conceptualized in terms fundamentalhopping frequency as well as third harmonics which oftwo major functional blocks: (1) the pre-processor and (2) the are 60 dB down tirn the fundamental. post-processor. The pre-processor utilizes an expert system to analyze EMI/RF effects based upon an enhanced version of The preprocessor computes the path attenuations corresponding IEMCAP and Neuron Data’s inference engine. This analysis is to the path between the desired transmitter and desired receiver as done in the frequency domain with respect to transmitted and well as the paths between the interferers and the desired receiver. received average powers. A worst-case modeling approach is These computations are carried out for the signals at the adopted. Should an EMI problem be predicted, the post- fimdamentalfrequenciesas well as for their third harmonics. All of processor is invoked. The post-processor does its analysis in the these signals are attenuated according to the path attenuations time domain. An expert system signal processing methodology is provided by the preprocessor and are then added together to used to assessthe performancedegradation causedby the EMI and provide the input signal to the desired receiver. The attenuation “fixes” are proposed, as needed. by means of interference in the desired path is 70 dB for the tindamental and 80 dB for the cancellation algorithms, In the proof of concept demonstration. third h‘armonic. In contrast. the path attenuations for the the pre-processor and post-processor are weakly coupled in that interference sources to the desired receiver are 25 dB for the the pre-processor predicts path attenuation parameters that are fundamentaland 35 dB Ibr the third harmonic.
Each Source Includes a Nonlinearity
IEMCAP PATH Al-FENUATION (P.A..)
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Figure 2. Block Diagram of Simulated Frequency Hopping Communication System and Frequency Hopping Interferers for Proof of Concept Demonstration. The front end ofthe desired receiver is modeled as a nonlinear RF amplifier with a nonlinearity of the form y = alx + as? where al = 100 and a3= 1000. This amplified signal is downconverted to IF by meansof a mixer. To account for spurious responses, the local oscillator includes a third harmonic along with its fi.mdamental carrier component. The local oscillator signal is frequency hopped in synchronism with the frequency hopping ofthe desired transmitter signal so as to generatean IF signal with a constant IF eequency. The IF stage is modeled as a passband third-order Butterworth filter with a center frequency of 1,008 Hz and a bandwidth equal to 96 Hz. Finally, the FM demodulator yields the desired output data signal. In many practical situations, the desired signal at the receiver input is very much weaker than the interfering signals. This arises when the desired transmitter is remotely located tirn the receiver while the interferers are operating in close proximity. When the interferenceis strong enough, the receiver is not able to correctly demodulate the information and the preprocessor should predict this EMI occurrence. However, communication may still be possible ifinterference cancellation techniques are employed.
those signal tiequency components that do not belong to the allowable set of hopping frequencies of the desired transmitted signal. Although this simple schemeis effective in removing the interference at the receiver input, the input signal to the IF amplifier still contains spurious components due to the third harmonic ofthe local oscillator. Nevertheless, error-free reception ofthe transmitted data is achieved. This performanceis especially noteworthy in view of the relatively high interference voltage levels as itemized in Table 1 below. Without the interference cancellation schemein place, a very large number of message bit errors are detected. Table 1. Interference Signal Type and VoltageLevel Type Signal Desired and interferer signal outputs fundamentalfrequency third harmonic Desired signal at tbe receiver input fundamentalfrequency third harmonic Interferersat the receiver input fundamentalfrequency third harmonic
Voltage 63.2 V 0.0632 V 0.02 v 6.32 x 10-6V
The knowledge based interferencecancellation approach is useful 3.55 v in this context. The received signal is analyzed by the 1.12 x 10-3v environmental monitor. It determines the nature of the interfering signals and computes the associated signal parameters. A The proof of concept demonstration illustrates the benefits to be comprehensive signal analysis tool. such as IPUS, can be achieved by the post-processor when employed in conjunction employed for this purpose. However, for the proof of concept with conventional EMC analysis which predicts EMI. Since demonstration simple knowledge extraction algorithms, such as EMC analysis tools usually employ a worst-case approach, EMI the short-term Fourier transform, are utilized. Once the nature of may or may not exist even when predicted. By carrying out a the interference is known. relatively simple interference detailed modeling and time-domain analysis. the post-processor algorithms. such as notch filters, can be used. This is in Lieu of may determine that an interference situation does not actually complex adaptive filters which require extensive computation and exist. On the other hand. if EMI is confirmed. the post-processor arc used in unknown interference environments. In the prhof of uses a knowledge-based approach to detemline a suitable concept demonstration the interferenceis cancelled by nulling out inlcrfercnce cancellation algorithm that can be used to make the
system functional. The results of the post-processor can also be used to influence system design with regard to frequency assignments and placement of antennas. Also, it can be used to adapt transmitter communication strategies, such as frequency hopping patterns, in order to ensure electromagnetic compatibility. These issues are to be investigated in greater detail during the next phase of this researchand development program. Summary/Conclusions This paper discussed the application of a knowledge-based approach to generate valid electromagnetic structure models, automatefrequency and time domain EMC analyses,and determine interference cancellation requirements for the case of spread spectrum, frequency hopped RF transceivers. Computer simulations have demonstrated the effectiveness of the approach for several interferencescenarios using E3EXPERT, a moderatelyconservative, system-level culling tool. It is based on enhanced RF coupling models resident in the Intrasystem Electromagnetic Compatibility Analysis Program (IEMCAP). E3EXPERT uses expert reasoning techniques to validate geometry models then it predicts, ranks, and resolves EMI using a knowledge-based interference rejection scheme. E3EXPERT, by virtue of its IEMCAP kernel, can also be used to analyze embeddedport (i.e., internal electronics equipments and cables) and external port (antenna) EMC. The E3EXPERT concept and approach is the first known attempt to apply “synergistic artificial intelligence” formalisms and constructs for the purposes of demonstrating an integrated, smart pre/post-processing capability that addresses a particular class of EMC problems. References [l] G. Capraro, A Drozd, G. Brock, et. al., Intrasystem Electromagnetic Compatibility Analysis Program - Version 6.0 User’s Manual Engineering Section”, Technical Report RL-TR91-217, Vol. I of II, Prepared for the US Air Force Rome Laboratory, AFSC, September199 1. [2] G. Capraro, A. Drozd, G. Brock, et. al., Intrasystem Electromagnetic Compatibility Analysis Program - Version 6.0 User’s Manual Usage Section”, Technical Report RL-TR-9 l-2 17, Vol. II of II, Prepared for the US Air Force Rome Laboratory, AFSC, September199 1. [3] Klocko, W., et. al., “A Technical Description of the Aircraft Inter-Antenna Propagation with Graphics (AAPG) Computer Program”, ECAC-HDBK-90-083, IIT Research Institute, April 1990. [4] Widmer, H., et. al., “A Guide to Using the AAPG Program (Version 07)“, ECAC-CR-87-024, IIT Research Institute, July 1987. [5] Widmer, H., et. al., “A Technical Description of the AAPG Program (Version 07)“, ECAC-CR-87-03 1, IIT ResearchInstitute, July 1987. [6] Gaudine, D. and Kubina, S. J., “AAPG V07D (PC/VGA)“, TN-EMC-92-01, Concordia University for the Department of National Defence,Ottawa, CA, 23 January 1992.
Acknowledgments The authors wish to acknowledge the continuing support and contributions made on behalf of the E3EXPERT development program by Timothy W. Blocher of the US Air Force Research Laboratory/IFSB; and Clifford E. Carroll, Jr., JamesM. Allen, and Jason R Miller of ANDRO.
