[Objective]　Estimation　of　direction　of　arrival　(DOA)　is　critical　in　spatial　audio　coding,　speech　enhancement,　sound　field　synthesis,　and　sound　source　imaging.　Commonly　used　signal　model-based　DOA　estimation　methods,　such　as　the　multiple　signal　classification　method,　can　effectively　estimate　DOA　information　in　noise-free　and　anechoic　scenarios.　However,　real-world　environments　always　have　noise　and　reverberation,　particularly　in　far-field　speech　communication　scenarios　characterized　by　low　signal-to-noise　ratios　and　strong　reverberation.　Furthermore,　the　sound　source　may　be　in　motion.　These　factors　considerably　impair　the　performance　of　DOA　estimation　methods　based　on　signal　models.　To　address　this　issue,　this　paper　introduces　a　real-time　estimation　and　tracking　method　for　the　DOA　of　a　single　sound　source,　using　Kalman　filtering　and　frequency　focusing.　[Methods]　The　proposed　method　consists　of　three　procedures:　denoising,　dereverberation,　and　DOA　estimation.　With　regard　to　the　denoising　procedure,　an　objective　optimization　function　to　minimize　the　error　of　the　denoised　signal　is　established.　This　function　is　solved　using　a　Kalman　filter,　which　leads　to　obtaining　the　denoised　signal　through　Kalman　gain-based　posterior　estimation.　For　the　dereverberation　procedure,　based　on　the　autoregressive　coefficients　of　the　late　reverberation　components,　an　objective　optimization　function　to　minimize　the　error　of　the　multichannel　linear　prediction　(MCLP)　coefficients　is　established.　This　function　is　also　solved　through　another　Kalman　filter　to　obtain　the　MCLP　coefficients.　The　DOA　estimation　procedure　is　implemented　by　using　a　frequency　focusing　based　steered　response　power　(FF-SRP)　method,　which　can　circumvent　signal　component　diffusion　within　subspace　decomposition.　In　particular,　a　structure　that　effectively　intertwines　these　three　procedures,　enhancing　the　contribution　of　denoising　and　dereverberation　results　to　DOA　estimation.　In　this　structure,　a　propagation　matrix　is　utilized　to　integrate　the　denoising　and　dereverberation　procedures,　creating　a　causative　iteration　between　them.　Subsequently,　a　minimum　variance　distortionless　response　(MVDR)　beamforming　method　is　used　to　replace　the　multichannel　Wiener　filtering　method.　This　is　to　obtain　a　prior　estimation　of　the　covariance　matrix　of　the　target　signal.　The　MVDR　beamforming　method　offers　two　advantages:　it　reduces　the　distortion　of　the　target　signal　and　integrates　the　DOA　estimation　procedure　with　the　denoising　procedure,　thereby　promoting　a　causal　and　orderly　iteration　among　the　three　procedures.　[Results]　Experiments　were　conducted　using　a　microphone　array　signal　simulator　and　the　TIMIT　corpus.　The　mean　absolute　error　(MAE)　of　the　estimated　DOA,　along　with　the　DOA　track　of　the　moving　speaker,　served　as　the　evaluation　measures.　Experimental　results　revealed　several　key　findings:　(1)　As　RT60　increased,　the　MAE　of　all　methods　increased,　clearly　demonstrating　that　reverberation　significantly　affects　DOA　estimation　performance.　(2)　Compared　with　the　reference　methods,　the　proposed　method　consistently　delivered　the　lowest　MAE　values　under　different　RT60s　and　SNRs.　This　suggests　that　the　proposed　method　has　higher　accuracy　in　DOA　estimation.　(3)　In　terms　of　DOA　trajectory,　the　proposed　method　again　outperformed　the　reference　methods　by　producing　the　smallest　error.　This　indicates　that　the　proposed　method　has　better　performance　in　DOA　tracking.　[Conclusions]　By　integrating　denoising,　dereverberation,　and　DOA　estimation　through　a　causal　and　recursive　iteration　structure,　the　performance　of　DOA　estimation　and　tracking　can　be　significantly　enhanced.　The　proposed　method　effectively　mitigates　the　detrimental　impact　of　noise　and　reverberation　on　DOA　estimation　and　tracking　accuracy　in　single　sound　source　scenarios.　©　2024　Tsinghua　University.　All　rights　reserved.