This　paper　proposes　a　novel　approach　for　absolute　velocity　estimation　of　unmanned　aerial　vehicles　in　unknown　and　unmapped　GNSS-denied　environments.　The　proposed　method　leverages　the　advantages　of　Fourier-based　image　phase　correlation　and　utilizes　off-the-shelf　onboard　sensors,　including　a　downward-facing　monocular　camera,　an　inertial　sensor,　and　a　sonar.　The　non-matching　tracking　approach　is　particularly　appealing,　offering　accurate　estimation　while　remaining　robust　against　frequency-dependent　noise,　significant　intensity　variations,　and　time-varying　illumination　disturbances.　In　the　proposed　method,　the　first　step　involves　computing　global　pixel　motion　from　consecutive　images　using　phase　correlation,　which　utilizes　the　shift　property　of　the　Fourier　transform.　This　pixel　motion　estimation　serves　as　the　basis　for　creating　a　closed-loop　solution　for　absolute　velocity　estimation.　To　further　enhance　accuracy,　a　Kalman　filter　is　implemented　to　fuse　all　available　data　and　provide　a　reliable　velocity　estimate.　Validation　of　the　proposed　visual-inertial　technique　is　conducted　through　simulation　experiments　using　AirSim　and　real-world　flight　tests.　The　results　demonstrate　the　practicality　and　effectiveness　of　the　approach　across　a　range　of　challenging　scenarios.　This　paper　proposes　a　Fourier-based　image　phase　correlation　method　for　absolute　velocity　estimation　of　unmanned　aerial　vehicles　using　off-the-shelf　onboard　sensors,　including　a　downward-facing　monocular　camera,　an　inertial　sensor,　and　a　sonar　in　unknown　and　unmapped　GPS-denied　environments.　The　non-matching　tracking　approach　is　attractive　and　promising,　with　the　advantages　of　accurate　estimation,　robustness　against　frequency-dependent　noise,　significant　intensity　variations,　and　time-varying　illumination　disturbances.　image