Passive sonar signal detection algorithm with lower false alarm rate

A novel noise background normalization algorithm is proposed to deal with the multi-target signal detection problem of passive sonar.The key of the proposed algorithm is composed of three main parts,Dilation operator, Erosion operator and polynomial fitting method.The Dilation operator and Erosion operator are commonly used in Mathematical Morphology signal processing,and the polynomial fitting method is used to evaluate the mean value of the data.By using these three basic methods,the threshold of the noise is estimated accurately,and the proposed algorithm performs well in noise suppressing while preserving as much signal information as possible.Performance comparisons are given through computer simulations with the Split Three-Pass Mean(S3PM) algorithm and the Order Truncate Average (OTA) algorithm.Results showed that the proposed noise background normalization algorithm could depress the false alarm rate substantially when the cost of the detection probability is slightly.Real Bearing-Time Record(BTR) data of passive sonar is also used to check the capability of three algorithms mentioned above.The novel proposed algorithm presents a better performance than the other two methods in practical applications too.