The proposed method consists of a valid channel decision and MPEG-4 ALS. ... Keywords: Lossless compression, MPEG-4 ALS, Underwater acoustic sensor. 1.
International Journal of Image and Signal Systems Engineering Vol. 1, No. 1 (2017), pp. 21-28 http://dx.doi.org/10.21742/ijisse.2017.1.1.04
Underwater Acoustic Sensor Array Signal Lossless Compression Based on Valid Channel Decision Approach Yong Guk Kim1,2, Kwang Myung Jeon2, YoungShin Kim1, Chang-Ho Choi1, and Hong Kook Kim2 1
Maritime R&D Center, LIG Nex1 School of Electrical Engineering and Computer Science, GIST 1 {yongguk.kim, youngshin.kim, changho.choi}lignex1.com, 2{kmjeon, hongkook }gist.ac.kr 2
Abstract A lossless compression method for underwater acoustic sensor array signals is proposed here. The proposed method consists of a valid channel decision and MPEG-4 ALS. The valid channel decision is based on Root-Mean-Square Crossing-Rate (RMSCR)-based sensor fault detection for excluding faulty sensor signals from the encoding process. After the decision process, multi-channel sensor signals are encoded losslessly using the MPEG-4 ALS encoder. For performance evaluation of the proposed method, we measure the precision of detecting faulty sensor and compression ratio. Findings show that the fault detection of proposed method works correctly, and compression ratio increases by 0.5% compared to the MPEG-4 ALS reference software in faulty sensor signal send mode, and by 3.05% in non-send mode. Keywords: Lossless compression, MPEG-4 ALS, Underwater acoustic sensor
1. Introduction Various research projects on sonar technologies are currently ongoing for military and commercial applications [1]. Generally, a sonar system is composed of a single or several hydrophone arrays consisting of multi-channel underwater acoustic sensors. Therefore, the data rates of multi-channel sensor signals that should be acquired and processed in real time are huge. Especially, in the case of a system for remotely detecting and processing signals obtained from the sensors installed on the seabed and the shore, it is necessary to transmit the sensor signals obtained using a communication medium such as a wireless or long-distance optical cable. For this purpose, a compression technique is necessary for real-time operation in a limited bandwidth environment. Audio data compression has been intensively studied in the field of communications due to the proliferation of mobile phones, multimedia players, and the internet. Depending on whether the originally sampled signal can be accurately recovered from the compressed data or not, it can be categorized into two algorithms: lossy and lossless, respectively [2]. Unlike general audio systems for listening purposes, sonar equipment requires detection of weak acoustic signals masked by environmental noise. Therefore, a lossless coding technique is required for compression of sonar sensor signals in order to minimize deterioration of detection performance. However, the lossless compression scheme has a lower compression ratio and higher complexity than the lossy compression scheme. In particular, the amount of data to be processed increases sharply as the number of sensors increases.
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International Journal of Image and Signal Systems Engineering Vol. 1, No. 1 (2017)
Sonar systems are commonly composed of single or multiple sensor arrays with dozens or more underwater acoustic sensor, and occasionally either a sensor fault or failure occurs due to physical or electrical shocks. These sensor failures or faults degrade detection performance due to the influx of electrical noise or reduced directivity index [1, 3]. The transmitter transmits state information and excludes the sensor signal compression and transmission itself for the fault channel after detecting the failure of the sensor element constituting the array sensor in real time, thereby improving the performance in terms of compression efficiency and complexity of the encoder. Moreover, it is possible to prevent degradation of detection performance by eliminating the faulty sensor signal in the signal processing stage according to the faulty sensor information. Therefore, in this paper, we propose an MPEG-4 ALS based signal compression method for underwater acoustic sensor arrays using a valid channel decision approach, where channel decision is performed based on the analysis of Root-MeanSquare Crossing-Rate (RMSCR) [4]. Following this introduction, Section 2 briefly explains MPEG-4 ALS. Then, Section 3 describes the proposed method. Section 4 evaluates the performance of the proposed method. Finally, Section 5 summarizes and concludes the paper.
2. MPEG-4 ALS Control Data
Input
Frame/block partition Entropy decoding Short term prediction
Demultiplexing
Long term prediction
Multiplexing
Joint channel decoding
Long term prediction
Joint channel coding Short term prediction
Entropy coding Frame/block assembly
Encoder
Output
Decoder
Figure 1. The structures of MPEG-4 ALS encoder and decoder [5]
MPEG-4 ALS is a standard lossless compression method for audio signals based on linear prediction. It provides a joint channel coding that improves the compression ratio of multichannel audio streams by utilizing inter-channel correlation of multi-channel signals [5, 6], and ALS can support floating-point audio signal compression. The audio residual is encoded by means of Rice and block Gilbert–Moore codes [2]. MPEG-4 ALS provides compression
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International Journal of Image and Signal Systems Engineering Vol. 1, No. 1 (2017)
methods with arbitrary sampling rates up to 192 kHz and resolutions of up to 32-bit and up to 65536 channels, including IEEE754 32-bit floating-point signals [5]. Figure 1 presents the block diagram of the MPEG-4 ALS encoder and decoder [5]. First, the input signal is divided into frames, and each frame can be subdivided into several audio sample blocks for the subsequent prediction and encoding process. A residual is given by applying forward adaptive prediction to each block. ALS adopts forward adaptive prediction with up to 1023 prediction orders. As shown in the figure, this short-term prediction can be combined with subsequent long-term prediction for eliminating long range correlation. In addition, inter-channel redundancy can be eliminated by joint channel coding, either using difference coding between channel pairs or multi-channel coding (MCC). The prediction residual eliminated sample correlation is finally encoded by entropy coder Rice and block Gilbert–Moore codes.
3. Proposed Compression Method 3.1. Overall structure of proposed method The encoder and decoder structures of the proposed method are shown in Figures 2 and 3. As shown in Figure 2, the encoding procedure of the proposed method is composed of two steps. The valid channel decision is applied through sensor fault detection. Next, using detection results, signals from normal sensor and faulty sensor are clustered respectively. Normal sensor signals are encoded by the MPEG-4 ALS encoder, and the faulty sensor signals are excluded from the encoding process. There is an advantage in terms of reduction of encoding complexity and increased coding efficiency through valid channel determination and elimination of faulty sensor signals from the encoding process. Consequently, the encoded MPEG-4 ALS bitstream and side information from the sensor fault detector are merged into one bitstream. As shown in Figure 3, in the decoding process, the received bitstream is first divided into MPEG-4 ALS bitstreams and side information. If faulty sensor signals are excluded, then the beamformer [7, 8] of the signal processing unit excludes sensor signals corresponding to faulty sensors on beamforming for avoiding degradation of detection performance.
Signal acquisition
Array sensor signal
MPEG-4 ALS encoder
Faulty sensor signal Send mode
On
Short-time Preaverage processing
Off
MPEG-4 ALS encoder Send flag(0/1)
Sensor fault detection
Multiplexing
Array sensor input
Demultiplexing
Normal sensor signal
Bitstream
Sensor status
Figure 2. Proposed method (encoder)
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International Journal of Image and Signal Systems Engineering Vol. 1, No. 1 (2017)
Bitstream
Faulty sensor signal MPEG-4 PostALS decoder processing
Short-time average
Beamformer
Demultiplexing
Normal sensor MPEG-4 signal ALS decoder
Detection
Sensor status
Figure 3. Proposed method (decoder)
3.2. Valid channel decision The proposed method attempts to provide a reliable channel decision measure, even in a noisy underwater environment, by analyzing the features of channel signals. As shown in Figure 4, the proposed method compares ratio values calculated based on RMS for all the channels to distinguish sensor status. The proposed method investigates the degree of signal change with respect to a specific reference value. This is because the signal obtained from the faulty sensor tends to be biased, or nearly forms a DC signal. Based on these characteristics, the Root-Mean-Square crossing-rate (RMSCR) [4] is an effective measure for investigation of the degree of signal change. The RMSCR of the i-th sensor signal can be computed as 1 N T T Ri (6) g ( xi (n)) g ( xi (n 1)) , N 1 n2 1, x 0 where f ( x) 0, x 0 , g ( x) f ( x ri ) , and ri is the RMS of i-th channel. 1, x 0
The proposed valid sensor channel decision method is based on faulty sensor detection, which utilizes RMSCR analysis with thresholding. To do this, the proposed method computes the ratio between average RMSCR for all the channels and the RMSCR of each channel. Thus, the ratio at the i-th channel, ( Ri ) , is defined as ( Ri )
R , Ri
(7)
where R 1 M i0 Ri . After computation of ( Ri ) , it is compared with the pre-defined decision threshold. The i-th channel sensor is declared as a faulty sensor if the computed ( Ri ) is lesser ( lower ) or greater ( upper ) than a pre-defined threshold. The former means that the signal from a faulty sensor is biased to a negative value, and the latter means that the signal is biased to a positive value. In addition, the threshold parameter ( ) that determines both lower and upper is set to a user-defined fixed value. M 1
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International Journal of Image and Signal Systems Engineering Vol. 1, No. 1 (2017)
Array sensor signal Integration time T
Linear integration
RMS, ri , calculation for each channel i
RMSCR computation for each channel i
Threshold parameter,
Compute fault detection threshold,
( Ri )
R Ri
lower (Ri ) upper i 1,, M
lower 1 upper 1
No Determine that the i-th sensor is fault
Yes Determine that the i-th sensor is normal Figure 4. Procedure of valid channel decision
4. Performance Evaluation For the performance evaluation of the proposed method, we measured the precision of detecting faulty sensors and compression ratio. We conducted the experiments using two real sensor arrays being operated at different coastal areas; their sensor configurations are shown in Table 1. Each sensor array is composed of 80 or more sensors, and the number of faulty sensors differed depending on the array. We obtained sensor signals of 64 seconds from each sensor array. Sampling rate of each sensor array is 2048 Hz, and the resolution and data types are 16 bits integer types. Table 1. Sensor arrays and their configurations used for experiments Array A B
Normal 97 74
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The number of sensors Faulty 3 6
Total 100 80
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International Journal of Image and Signal Systems Engineering Vol. 1, No. 1 (2017)
4.1. Sensor fault detection Table 2. Experimental result of sensor fault detection, where =0.4 Sensor array
Average RMS [dB]
A
-21.6
A
-13.1
B
-16.0
B
-12.4
T [sec] 2 4 8 2 4 8 2 4 8 2 4 8
Precision [%] 100 100 100 100 100 100 100 100 100 100 100 100
Table 2 shows the precision of sensor fault detection with several linear integration times, T, and average RMS. As shown in the table, the proposed method shows perfect precision for fault detection. Therefore, the RMSCR-based ratio, ( Ri ) , could be a reliable decision factor for valid channel selection. 4.2. Compression performance In order to evaluate the compression efficiency, we measured the compression ratio. The compression ratio, C, is defined as [2, 5] C
Compressed file size 100 [%] . Original file size
(11)
In this experiment, we compare the compression ratios between our proposed method and an MPEG-4 ALS reference software encoder [9]. We set the prediction order as 20; both frame length and sampling frequency are set as 2048 for MPEG-4 ALS encoding. Table 3 shows the compression ratios for encoding three different array signals. The results show that our proposed method increases compression ratio by 0.5% compared to the MPEG4 ALS reference software in faulty sensor signal send mode and by 3.05% in non-send mode. Our proposed method achieved high compression performance proportional to the number of faulty sensors by excluding the faulty sensor signals from the encoding process in the nonsend mode. In addition, a reduction of processing time (i.e., of encoding complexity) for the faulty sensors was observed. In the send-mode, a small compression performance improvement is achieved through pre- and post-processing for faulty sensors. Table 3. Comparison of compression ratios for three different array signals Sensor array A B
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Compression ratio (C) [%] Proposed method MPEG-4 ALS RM Send mode Non-send mode 76.08 73.18 76.12 66.67 64.48 67.64
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International Journal of Image and Signal Systems Engineering Vol. 1, No. 1 (2017)
5. Conclusion In this paper, a lossless compression method for underwater acoustic sensor array signal is proposed for sonar systems. In particular, a sensor fault detection-based valid channel decision method was proposed in order to reduce complexity and compression efficiency during the MPEG-4 ALS encoding process. The evaluation result shows that the valid channel decision method achieved complete performances in terms of precision in detecting fault sensors. In the case of compression performance, compression ratio increases compared to the MPEG-4 ALS reference software encoder in by 0.5% faulty sensor signal send mode and by 3.05% in the non-send mode.
Acknowledgements This work was supported by the Research Fund of Signal Intelligence Research Center supervised by the Defense Acquisition Program Administration and Agency for the Defense Development of Korea.
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