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Application of Sparse Representation to Radio Frequency Emitter Geolocation from an Airborne Antenna Array

Compaleo, Jacob
http://orcid.org/0000-0002-7075-3118
http://rave.ohiolink.edu/etdc/view?acc_num=osu1669887350018341

Abstract Details

2022, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
This dissertation uses sparse representation to improve upon single platform geolocation from an airborne antenna array. Conventional geolocation involves drawing Lines of Bearing (LOB) along the DOA estimates obtained at each flight path observation. Emitter location estimates are obtained at the intersections of these lines on the ground. LOB geolocation often utilizes the MUSIC algorithm as its DOA estimation technique. Based on our earlier findings that spectral domain sparse representation (SDSR) outperformed MUSIC algorithm in DOA estimation, SDSR is utilized with LOB method and referred to as SDSR LOB. Through simulation, it is shown that SDSR LOB outperforms MUSIC LOB in terms of accuracy. Despite this improvement, emitter location error for both methods is still very large when accurate knowledge of the antenna array manifold is not available and therefore it is necessary to look for other methods for improved geolocation performance. When DOA spectra is available, one can map the spectra over the region of ground where the RF emitters are expected to be located. The average DOA spectra for all of the points on the flight path will yield a heat map. The peaks in the heat map then correspond to the emitter location. This is sometimes called the Direct Mapping Method (DMM). DMM uses all the information contained in the DOA spectra instead of just the peaks (DOA information) in the spectra. Thus, DMM leads to a better estimate of the location of the RF emitters. Currently, MUSIC DOA spectra is mostly used in DMM. An SR based approach for DMM is presented. In our approach, the covariance matrix obtained from the snapshots received at each observation point are used to generate DOA spectra using sparse representation (SR). This approach is referred to as SR based DMM (SR-DMM). It is shown that SR-DMM does not provide any accuracy advantage over MUSIC DMM. In addition to this, the computational efficiency of SR-DMM and MUSIC DMM are studied. SR-DMM is found to be not as computationally efficient as MUSIC DMM and thus takes much longer to run. Next, the SR based DMM approach that works to process the covariance matrices at all observation points jointly (SR-DMM-JC) is presented. Our previous approach, SR-DMM, individually processed observations obtained at each flight path point. Due to this, SR-DMM did not provide an accuracy advantage over the existing methods. By processing the observations jointly with SR-DMM-JC, it is shown that the method has much better accuracy than all previously discussed methods, including MUSIC DMM. Lastly, the computational efficiency of SR-DMM-JC is studied. While SR-DMM-JC is more efficient than SR-DMM, it is found that the method is still significantly computationally less efficient than MUSIC DMM. Finally, our SR based DMM with joint spectral domain processing (SR-DMM-JSD) is presented. SR-DMM-JSD uses the heat map obtained using Bartlett spectra over the whole flight path as observations. Working in the spectral domain, it is possible to average the observation over the entire flight path, leading to a reduced problem size compared to SR-DMM-JC. Through simulations, it is shown that SR-DMM-JSD is just as accurate as SR-DMM-JC. In addition to this high accuracy, the computational efficiency of SR-DMM-JSD is looked at. It is found that by averaging the observations, SR-DMM-JSD is much more computationally efficient than SR-DMM-JC when the number of flight path observations is large. Guidelines were then setup for the implementation of SR-DMM-JSD. Starting with the DOA estimation problem, guidelines are developed using our SDSR approach with the assumption that these guidelines will also be valid for the geolocation problem. The guidelines are based on how much of the Bartlett spectra angular region needs to be covered and how fine of a resolution is needed to generate the Bartlett spectra. Ultimately, it is found that only the major lobes of the Bartlett spectra and the first pair of sidelobes on each side of the major lobes need to be covered to obtain an accurate DOA estimate with SDSR. Also, it is found that the major lobes of the Bartlett spectra need to be generated with 6 points and each sidelobes need to be generated with 3 points to obtain an accurate SDSR estimate. These same guidelines were then applied to the geolocation problem for SR-DMM-JSD. By generating the Bartlett DMM heat map with only 12 points in x direction and 12 points in y direction, it is found that one could obtain accurate geolocation estimates with SR-DMM-JSD when there were only a few emitters in the scene. Lastly, the computational efficiency of SR-DMM-JSD is looked at again, now with our updated guidelines. Using the updated guidelines, it is shown that SR-DMM-JSD is more computationally efficient than all other methods, even MUSIC DMM.
Inder Gupta (Advisor)
Fernando Teixeira (Committee Member)
Robert Burkholder (Committee Member)
215 p.
Electrical Engineering
DOA estimation, geolocation, sparse representation

Recommended Citations

Citations

  • Compaleo, J. (2022). Application of Sparse Representation to Radio Frequency Emitter Geolocation from an Airborne Antenna Array [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1669887350018341

    APA Style (7th edition)

  • Compaleo, Jacob. Application of Sparse Representation to Radio Frequency Emitter Geolocation from an Airborne Antenna Array. 2022. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1669887350018341.

    MLA Style (8th edition)

  • Compaleo, Jacob. "Application of Sparse Representation to Radio Frequency Emitter Geolocation from an Airborne Antenna Array." Doctoral dissertation, Ohio State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=osu1669887350018341

    Chicago Manual of Style (17th edition)

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