The　Kalman　filter　performs　well　in　system　state　estimation　by　inferring　a　joint　probability　distribution　over　time　variables,　which　has　numerous　technological　applications　in　time　series　analysis.　However,　the　complex　Kalman　filter　parameter　settings　prevent　it　from　optimally　estimating　the　system　state,　and　this　suboptimal　estimation　makes　it　difficult　to　effectively　distinguish　between　normal　and　anomalous　system　states　in　anomaly　detection.　In　this　article,　we　propose　a　deep　embedding-optimized　Kalman　filter　for　unsupervised　time　series　anomaly　detection,　where　the　system　state　of　a　normal　time　series　can　be　fit　by　the　embedding-optimized　Kalman　filter　in　an　unsupervised　manner　and　anomalies　can　be　detected　from　data　points　that　deviate　from　the　normal　system　state.　Specifically,　we　use　an　autoencoder-enhanced　Kalman　filter　to　capture　the　normal　pattern　of　the　time　series,　where　the　original　time　series　signal　is　first　fed　into　the　Kalman　filter,　then　the　autoencoder　encodes　the　filtered　signal　and　reconstructs　the　original　signal,　and　the　learned　embedding　from　the　encoder　is　used　to　sequentially　optimize　the　Kalman　filter.　Finally,　the　optimized　filter　captures　the　normal　pattern　of　the　time　series,　and　the　reconstruction　error　from　the　filtered　signal　can　be　measured　to　detect　anomalies.　The　validity　of　the　method　is　verified　on　real-world　time　series　datasets.