Analysis of target detection performance for a single vector hydrophone windowed histogram algorithm

Analysis of Target Detection Performance for a Histogram Algorithm of Single Vector Hydrophone 

The histogram algorithm of single vector hydrophone has good robustness and target azimuth estimation performance. This paper analyzes and summarizes the target detection performance of the histogram algorithm, and an autonomous detection and tracking algorithm for underwater acoustic transducer based on the estimated azimuth of the target was proposed. Computer simulation and anechoic tank test results show that the signal-to-noise ratio required by the windowed histogram algorithm for autonomous tracking needs to be greater than 7 dB. Under this condition, the estimated azimuth error and 3 dB beam width are about 8◦ and 20◦, respectively. The sea trial results show that under good hydrological conditions in the deep sea, the windowed histogram algorithm can achieve target detection and tracking for a surface ship with a speed of 8.4 kn within a range of 13.8 km. The optimal estimated azimuth error can reach 5◦, and the 3 dB beam width can reach about 10◦ at a distance of 2 km.

The vector channel of the vector hydrophone has frequency-independent dipole directivity, and has the ability to resist isotropic noise interference.A vector hydrophone can achieve full-space blur-free orientation, which provides a solution for target detection on a small underwater platform equipped with acoustic sensors.

The advantage of space. In recent years, with the continuous improvement of vector hydrophone sensor technology, vector signal processing technology is also being applied powerfully.Driving by demand, it has developed rapidly. Compared with conventional sound pressure hydrophones, vector hydrophones provide more comprehensive sound field information. It can not only measure the scalar quantity of the sound field, but also obtain the vector characteristics of the sound field, which greatly expands signal processing space. There are many target azimuth estimation algorithms based on single vector hydrophones, but in general, they can be divided into two categories according to the principle of direction finding: one is azimuth estimation based on sound energy flow; the other is to regard each channel of the vector hydrophone as It is a multi-element array, each piezo element is located approximately at the same point in space, and the existing array signal processing method is applied to the single vector hydrophone by using the characteristics of the array flow pattern of the single vector hydrophone itself. Various target direction finding algorithms of vector hydrophones have their own advantages and disadvantages. Among them, the histogram algorithm has better robustness and target azimuth estimation performance than other algorithms, and has the ability to suppress narrowband and strong line spectrum interference. It is especially suitable for engineering application. This paper analyzes and summarizes the histogram direction finding algorithm based on a single vector hydrophone, and proposes an autonomous detection and tracking algorithm for underwater targets based on target azimuth 

Fig. 6 is the curve of target autonomous tracking flag with signal-to-noise ratio according to the target autonomous detection and tracking algorithm proposed in Section 1. Target tracking flag 1 represents that the algorithm achieves target tracking, and it means that target tracking is not achieved. It can be seen from Figure 6 that when the signal-to-noise ratio is greater than 7 dB, the histogram algorithm can achieve autonomous target tracking.

2.2 Tank test analysis

In order to master the target detection performance of the single vector hydrophone histogram algorithm, the target detection performance of the single vector hydrophone was carried out in the anechoic pool.

In the verification test, UW350 was used as the sound source target during the test, and the depth was 3 m underwater. The signal used in the test is the width of the signal source output.With Gaussian white noise, the output peak-to-peak value is set to 10 mV, 20 mV, 25 mV, 50 mV, 100 mV, 1 V, and 10 V respectively.

The signal transmission time is 60 s, and the small signal emission sound source level is calculated by the formula 20 lg (A1/A2), where A1 and A2 are the peak-to-peak value of the signal source setting output. The signal-emitting sound source level can be calculated according to the distance between the vector hydrophone and the sound source to obtain the signal-to-noise ratio of each channel of the vector hydrophone. Table 1 shows the results of the broadband average signal-to-noise ratio of the sound source signal received by each channel of the vector hydrophone, and gives the average value of the signal-to-noise ratio of each channel under different sound source emission intensities. It can be seen that the peak-to-peak value of the signal source output is At 10 mV, 20mV, 25 mV, 50 mV, 100 mV, 1 V and 10 V, the broadband average signal-to-noise ratio of the sound source signal received by the vector hydrophone is 13 dB, 7 dB, 5 dB, respectively , 1 dB, 7 dB, 27 dB and 47 dB. The seven signal-to-noise ratio signals are processed separately using the histogram algorithm. The calculated azimuth estimation results change with time as shown in Figure 7. The figure also marks the peak-to-peak value of the signal output and the vector hydrophone in each time period. Receiver signal-to-noise ratio. It can be seen from Figure 7 that the estimated azimuth of the sound source target gradually stabilizes as the received signal-to-noise ratio increases and basically coincides with the true azimuth. Figure 8 and Figure 9 respectively show the azimuth estimation error and 3 dB azimuth spectrum width of the signal-to-noise ratio signals emitted by the seven sound sources by the histogram algorithm. The ratio increases and gradually decreases. The direction finding error increases when the sound source emits a peak-to-peak noise signal of 10 V compared to 1 V peak-to-peak. This is due to the sound source emitting a high sound source level signal.

The acoustic pool is incompletely attenuated in the low frequency band and there is strong interface reflection; when the signal-to-noise ratio is 7 dB, the direction finding error is about 8 ◦, 3 dB square

The bit spectrum width is about 23◦; when the signal-to-noise ratio is greater than 1 dB, the direction finding error and the 3 dB azimuth spectrum width are less than 4◦ and 19◦, respectively.Figure 10 is the curve of the target tracking mark calculated according to the target autonomous detection and tracking algorithm with the intensity of the sound source emission signal, which can be seen.When the signal-to-noise ratio is 7 dB, the histogram algorithm can realize autonomous tracking of the sound source target.

2.3 Marine test analysis

Using data from the underwater acoustic sensor buoy target detection performance verification test data carried out in the northern waters of the South China Sea in August 2019, the histogram algorithmv of single-vector hydrophone was used to analyze the detection performance of maritime targets. The depth of the test sea area is about 1500 m. During the test, the weather conditions are good and the wind speed is about level 2. The measurement results of the ship-borne abandonment thermo-salt depth meter show that the sound velocity profile is a uniform layer within a depth of 40 m and a depth of 40 200 m. Inside is the main transition layer of sound velocity, and the vocal tract axis is at a depth near 1000 m. During the test day from 12:33-14:02, a surface ship with a length of 42 m, a width of 6 m, and a speed of 8.4 kn passed near the underwater acoustic buoy at a heading of 301°. During the period, the surface ship and underwater acoustics. The distance of the buoy is about 2 km at the shortest time and 13.8 km at the farthest time. The situation diagram is shown in Figure 11. Figure 12 shows the comparison between the estimated azimuth results of the surface ship’s target azimuth calculated by the histogram algorithm and the real azimuth. It can be seen that the histogram algorithm can achieve the goal of the surface ship during the entire 12:33-14:02 time period.

Figure 13 and Figure 14 respectively show the histogram algorithm for surface ship target direction finding error and the 3 dB azimuth spectrum width versus time curve in the time period of 12:33-14:02. It can be seen that the direction finding error is the best It can reach within 5 ◦, and the 3 dB azimuth spectrum width can reach about 10 ◦ near the close location point; in addition, due to the deviation of the underwater position of the underwater acoustic buoy, the distance between the surface ship and the buoy platform is relatively close. The error of direction finding at time increases. Figure 15 is the curve of the target tracking mark over time calculated by the target autonomous detection and tracking algorithm. It can be seen that the algorithm can achieve autonomous target tracking throughout the entire range for a surface vessel with a speed of 8.4 kn within a distance of 13.8 km.

3 Conclusion

Aiming at the engineering application requirements of single-vector hydrophones on underwater unmanned platforms, this paper proposes a method for autonomous detection and tracking of underwater ultrasonic sensor, and uses simulation calculations, anechoic tank tests, and marine test analysis to summarize based on single-vector water .The histogram algorithm of the listener had standard detection performance. The results of computer simulation and anechoic pool test data show that the signal-to-noise ratio required by the histogram algorithm to achieve autonomous tracking needs to be greater than 7 dB, at this time the direction finding error is about 8◦, and the 3 dB azimuth spectrum width is about 20◦. The marine test data shows that under good hydrological conditions in the deep sea, the histogram algorithm can achieve full target detection and tracking within a distance of 13.8 km for a surface vessel with a speed of 8.4 kn, and the best direction finding error can reach 5◦. The 3 dB azimuth spectrum width can reach about 10◦ near the near position point.