# Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold
## 1 Introduction
The reported method in [8] focused on the performance of the normalized matched filter (NMF). The NMF is used when the noise covariance matrix is fast time-varying and is hard to estimate. The proposed method in [9] to handle the nonlinearity between target states and the raw bearing measurements, particle filter is employed to compute the joint multi target probability density (JMPD) recursively through a Bayesian framework. After detection, Target recognition (classification) and tracking are used. In frequency domain, [PERSON] performed LOFAR (Low Frequency Analysis and Recording) broadband analysis, estimates the noise vibration of the target machinery [10]. [PERSON] and et al. in [11] investigated analysis under water signal in frequency domain. [PERSON] et al. proposed an improved matched filter in Additive white Gaussian noise (AWGN) combined with the adaptive line enhancer by analyzing the output spectrum and the spectral spectrum of the matched filter, which is the frequency-domain adaptive matched filter. Note that this method is proposed for active sonar [12]. In this paper [13], Mel frequency Cestrum Coefficients feature extraction using sound pressure and particle velocity signals are researched. Firstly, Mel-Frequency Cepstral Coefficients (MFCC) feature, first-order-differential MFCC feature, and second-order differential MFCC feature can be used as the effective feature of the underwater target identification from the feature extraction and  
Figure 1: Basic of target detection in passive sonar. recognition results. Secondly, by calculating the Fisher-ratio and correlated distance, it can be found that the contribution of each dimension feature is different, and those three features that are fused by using fdc criterion can improve the recognition probability of underwater target signal. In fusion domain, [PERSON] et al. in [14] performed independent component analysis for detection and classification of signals against background noise by using time and frequency domain. [PERSON] et al. in [15] proposed new method for detecting a target in non-stationary clutter in active sonar using dynamic time-frequency localization. In [16; 17], authors try to estimate behavior of target motion by using Target Motion analysis (TMA) and Bearing Only Tracking (BOT) algorithms. Note that TMA and BOT are used after detection. Thus, accurate detection is vital for TMA and BOT algorithms. In this paper, a new method for detecting targets in passive sonars using adaptive threshold in fusion domain is proposed. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and posterior distribution is estimated by Maximum Likelihood algorithm. Finally, target was detected by combining the detection points in both domains using Least Mean Square (LMS) adaptive filter. The paper is organized as follows. In Section 2, we describe proposed novel algorithm for target detection with passive sonar in shallow water. In Section 3, we discuss a method of calculate detection point in time and frequency domain and fusion to determine adaptive threshold and advantages. In Section 4, we present the experimental results on vessel signals recorded in Persian Gulf (Figure 2). Finally, we conclude the proposed method in Section 5, and present an attempt a critical evaluation.