The model-based approach is applied in the shallow water ocean acoustic signal detection problem. Based on a state-space representation of the normal mode ocean acoustic propagation model and a vertical linear array measurement system, the extended Kalman filter (EKF) is used to accomplish the shallow water ocean environment identification process, in which one of the outputs is the innovation sequence. When the model does not match the environment, the innovation sequence becomes nonzero mean and/or non-white. Several statistics for testing the properties of the innovation sequence are outlined and analyzed, composing an innovation-based detector which will declare a model mismatch if an anomaly (possibly a target) emerges. Simulations under a typical shallow water ocean environment are performed, giving the receiver operating characteristic (ROC) curves with regard to different SNRs and parameters in the test statistic weighted sum squared residuals (WSSR), showing the overall detection performanc es of these test statistics of the innovation sequence.
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