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A novel integrated waveform (IW) for continuous active sonar detection and communications is proposed in this study based on a generalised sinusoidal frequency-modulated signal. This study first introduces the mixed modulation of the IW and a model for the transmitting signal, and then it analyses the time and frequency resolution and anti-reverberation capability of the proposed IW. The performance of two underwater acoustic (UWA) communication receivers based on the proposed IW are compared and analysed under Gaussian noise and multipath conditions. A UWA communication experiment was carried out in Songhua Lake in December 2019. According to the numerical simulation and experimental results, it was concluded that the IW proposed in this study can meet the requirements of active sonar detection and UWA communications with satisfactory performance.

Sonar images are valuable in exploring underwater environmental information. As these images are generally limited by the viewing angle, sonar image mosaicking becomes an important research topic. By combining several frames consecutively acquired while the underwater vehicle is manoeuvring, an image with a wider view can be obtained. This study presents a fast sonar image mosaicking approach consisting of denoising, feature extraction, initial matching, splicing, and optimisation. Based on the Euclidean distance between initially matched points and dip angle of connection line, poorly matched feature point pairs are removed to avoid false matching. This way, the success rate of image mosaicking and quality of the resulting mosaicking is effectively improved.

Cubic phase (CP) signal is widely used in radar and sonar signal processing and the CP signal received by the radar or sonar sensors may miss part of discrete samples and be corrupted by noise. In this study, a novel end-to-end CP signal recovery method is proposed to recover the CP signal that experiences data missing and noise corruption based on deep convolutional neural network (DCNN). Two key techniques are used in the structure design of the proposed network. The first is dilated convolution, which has a larger receptive field than the standard convolution operation and can extract information more efficiently in the CP signal recovery problem. The second is denseNet, which connects all layers directly and can alleviate the vanishing-gradient problem. Experiment results show that the proposed DCNN-based method achieves superior performance to the traditional compressed sensing-based method both from the perspective of recovery error and time consumption.

The study presents an underwater acoustic signal processing approach for the identification of the material of an object using wideband chirp pulses. The echo from the target is formed by a number of processes that occur during reflection of the pulse from an object. The timing of these reflected components and geometry of their wave paths determine speed of sound in the wave propagating material. The calculated speed of sound is then used to identify the material. The novel method enables automated material identification without any training data. The object of the material identification is a two-layer metal spherical shell filled with liquid and placed in a fresh water tank. The presented approach identifies the shell and filler materials of the sphere. In this work, the sphere thickness is limited by 2% in relation to the radius. Results are evaluated for the sphere's radius in a range from 0.05 to 0.15 m. The approach yields 83.5% success rate for the shell material identification and 79.9% success rate for the filler material on the synthetic data. For the experimental data, 67.3 and 80% success rates were obtained for shell and filler material identification, respectively.

Adaptive line enhancers (ALEs) have been widely used in passive sonars for enhancing narrowband discrete components (known simply as lines or tonals) radiated by surface or underwater targets. Unfortunately, the performance of the conventional ALE based on the least-mean-square (LMS) adaptive algorithm is limited by the high steady-state misadjustment, which limits the output signal-to-noise ratio (SNR) of the conventional ALE. To break through the limit of the conventional ALE, this study proposes a sparsity-based ALE (SALE). The SALE takes into account the frequency-domain sparsity of narrowband discrete components and realises the adaption in the frequency domain. A l 1 -norm sparse penalty is incorporated into authors’ frequency-domain adaption to reduce the misadjustment dramatically. The simulations show that the SNR gain of the SALE is 6.3 dB higher than that of the conventional ALE. The results of processing the real data collected in the sea trial also demonstrate that authors’ proposed SALE outperforms the conventional ALE. To reduce the computational complexity of the SALE, a fast SALE (FSALE) is also proposed. In authors’ setup, the computational complexity of the FSALE is one order of magnitude lower than that of the SALE, with comparable SNR performance.

Human echolocation is a technique used by small group of people (normally blind persons) to observe their surrounding via emitting an active signal (usually tongue click) and analyse the return echo. This skill allows them to navigate safely by ‘seeing through sound’. Remarkable performances upon echolocating have attracted scholars’ attention from multidiscipline field including psychophysical and engineering. Study of waveform diversity has reported the signal is wideband, consisting of multiple frequency components with exponential decay factor that probably made up the entire unique signal. As such, wideband perspective is considered during ambiguity function analysis using wide-band ambiguity function method. The waveform analysis continues and reported optimum autocorrelation function is achieved by adopting a bio-inspired technique, which incorporated gammatone-filter processing. It will be interesting to synthesise the waveform using gammatone-filter approaches prior to relay it into ambiguity function process for analysis. Therefore, it is worth to explore its output results in this paper, which hold new findings that may help to understand how these humans achieve an optimum performance upon echolocating including ambiguity function characteristic. Thus, the new knowledge explored in this paper could be beneficial in emerging concept for new development in radar and sonar system application in near future.

Echolocating bats have evolved the ability to detect, resolve and discriminate targets in highly challenging environments using biological sonar. The way bats process signals in the receiving auditory system is not the same as that of radar and sonar and hence investigating differences and similarities might provide useful lessons to improve synthetic sensors. The Spectrogram Correlation And Transformation (SCAT) receiver is an existing model of the bat auditory system that takes into account the physiology and the neural organisation of bats that emit broadband signals. In this study, the authors present a baseband receiver equivalent to the SCAT that allows an analysis of target echoes at baseband. The baseband SCAT (BSCT) is used to investigate the output of the bat-auditory model for two closely spaced scatterers and to carry out an analysis of range resolution performance and a comparison with the conventional matched filter. Results firstly show that the BSCT provides improved resolution performance. It is then demonstrated that the output of the BSCT can be obtained with an equivalent matched-filter based receiver. The results are verified with a set of laboratory experiments at radio frequencies in a high signal-to-noise ratio.

The study introduces the concept of multiple input–multiple output (MIMO) radar or sonar arrays for short-range, high-resolution sensing in vehicular applications. The use of a MIMO architecture, which is becoming increasingly popular in this field, is selected to reduce the amount of physical elements in the array needed for beamforming, but also to allow signal processing approaches for forming narrow beams in the near-field of the array. The study analytically derives the proposed signal processing approach, and then verifies it via simulated and experimental data in a laboratory environment with scientific equipment assembled for this purpose.

Terrain aided navigation (TAN) is a promising approach to bound accumulated errors inherent to inertial navigation system by comparing terrain measurement with a reference map. Due to the non-linear nature of terrain, particle filters (PFs) are extensively studied for TAN because of its strong capability of dealing with non-linear problems. So far, most existing PFs for TAN manually select a fixed number of sampling particles during the entire filtering process. However, it can be highly inefficient, since the probability distribution of the state often varies drastically over time. To improve the efficiency, the Fox's adaptive PF based on Kullback-Leibler distance (KLD) is introduced for TAN, referred to as the normal KLD-PF here. In the normal KLD-PF, the number of sampling particles is adjusted online by KLD-sampling according to the size of state space. However, the normal KLD-PF has a fixed bin size, which easily causes the number of particles to surge at the early filtering stage. Thus, an improved KLD-PF with a variable bin size is proposed through limiting the total number of particles below certain value. Using a multi-beam sonar, simulation experiments with real underwater reference map demonstrate the efficiency of the proposed method.

Detecting low-speed quiet targets such as divers and underwater-unmanned-vehicles in littoral environment by using active sonar is becoming increasingly attractive. When using a Doppler insensitive pulsed linearly frequency modulated signal, the high-level clutters which might arise from underwater physical scatterers will lead to excessive false alarm rates and limit the detection performance. However, Doppler sensitive waveforms such as binary phase shift keying have the capability of filtering clutters and degrading false alarms. Thus, the question how to estimate the clutter-suppressing performance of waveforms is essential for sonar system design. In this study, the clutter-rejecting principle of waveform is theoretically introduced firstly. Then, based on the reverberation statistical features, this study proposes two methods, the Doppler-statistic method and envelope-statistic method, separately, to estimate and evaluate the clutter suppressing performance of waveforms. Finally, the methods are verified by lake experiments. It is proved that the first method has the capacity of calculating the confidence probability of suppressing clutters by a waveform, and through the second method, the clutter rejecting performance of waveforms can be evaluated and verified. The methods can be used for selecting and designing waveforms to reduce false alarms and improve detection performance.

The automatic classification of passive sonar signals in real time is an extremely useful tool for supporting sonar operators decision making during submarine missions. An appealing architecture for the development of accurate, flexible, modular and real-time classification systems is the class-modular multi-layer perceptron (CM-MLP). However, for this task, several design parameters have to be tuned and a more extensive guideline about how to conduct this process is missing in the literature. This work aims at discussing the main design phases related to the development of CM-MLP systems for passive sonar signal classification. For each phase, several approaches are discussed and their cost-effectiveness is experimentally evaluated using statistical significance tests. This analysis is based on real signals produced by 8 vessel classes and acquired by an omnidirectional hydrophone in a shallow water environment, during 263 experimental runs of 34 ships. Results show that some design parameters significantly affect the accuracy of the classification system, especially the complexity of each module. An accuracy of 84.4% is achieved by using tuned design parameters.

Marine vessel classification is complicated by the variability in the radiated signal of the marine vessel because of changing machinery configuration for the same class of vessels. Further, the radiated signal of the marine vessel propagating towards a distant receiver undergoes random fluctuations in phase, amplitude and frequency. The ambient noise at the receiver will further complicate the authors’ classification problem. The shallow underwater channel, in particular, where these classification systems are more likely to operate presents the most challenges because of severe time-varying multi-path. Cepstral approaches are proposed in this study, including cepstral features and average cepstral features to augment existing feature sets that are mostly based on spectral analysis. Analytical studies have been supported by simulation experiments and tests on real ship recorded data. The cepstral features with cepstral liftering process is able to significantly reduce the multipath distortion effects of shallow underwater channel whereas the average cepstral feature is able to notably reduce the time-varying channel effects.

This paper proposes a composite TFD based on a multiple-view Approach where the IMFs of an EMD are used to construct a series of views in the ambiguity domain by highlighting concentrations of energy in the ambiguity domain and overcoming interferences in TFDs. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated). This study proposes a novel, composite time-frequency distribution (TFD) constructed using a multiple-view approach. This composite TFD utilises the intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD) to generate each view that are then combined using the arithmetic mean. This process has the potential to eliminate the inter-component interference generated by a quadratic TFD (QTFD), as the IMFs of the EMD are, in general, monocomponent signals. The formulation of the multiple-view TFD in the ambiguity domain results in faster computation, compared to a convolutive implementation in the time-frequency domain, and a more robust TFD in the presence of noise. The composite TFD, referred to as the EMD-TFD, was shown to generate a heuristically more accurate representation of the distribution of time-frequency energy in a signal. It was also shown to have performance comparable to the Wigner-Ville distribution when estimating the instantaneous frequency of multiple signal components in the presence of noise.

Ensemble-averaged randomly scaled radar or sonar pulses are considered and the statistical theory for the corresponding phase and amplitude modulations are developed. Random scaling expresses varying radar cross-sections for scattering objects or varying antenna gain of a sweeping emitter. The noise variance of the modulations depends on the distribution function of the scaling, and how to minimise the variance by rejecting pulses below a certain amplitude threshold is shown. The theory is asymptotic in the sense that it is more accurate for increasing signal-to-noise ratios (SNRs). In a test case with uniformly distributed scaling, sufficient accuracy is reached for an average SNR larger than ∼5 for the phase average and ∼15 for the amplitude average.

Range sidelobe suppression in pulse-compression radars and sonars is conventionally performed using amplitude windowing in either the time or frequency domains. Using peak power limited transmitters, this inevitably causes a degradation in the signal-to-noise ratio available at the receiver output owing to shading or mismatch losses. Nonlinear frequency modulation (NLFM) chirps have been suggested as a solution to this problem and have been successfully implemented in a number of radar systems. Using appropriate NLFM functions it is possible to shape the energy spectrum of a chirp, achieving low-range sidelobes, without the need for any amplitude windowing and hence maintaining maximum efficiency. In the field of active sonar, NLFM chirps have been largely ignored because of their poor Doppler tolerance compared with conventional linear period modulation (LPM) chirps. It is shown that a combination of amplitude windowing and NLFM can be used to achieve a compromise between sidelobe suppression, Doppler tolerance and shading loss. Using a novel family of NLFM functions a significant performance advantage over LPM chirps can be attained over a useful range of target velocities. Experimental results support this argument.

A method for designing multilevel sequence, based on chirp and frequency hopping (FH) waveforms is presented. The main feature of the synthesised transmitter-receiver pair of sequences is the very low sidelobe level. The receiver sequences are considered as mismatched compression filter coefficients. The DIRLS (Doppler optimised iterative reweighted least square) algorithm is applied to the iterative optimisation of the receiver mismatched coefficients, as well as to the multilevel transmitter sequence.

In modern radar, sonar and wireless communication systems digital beamforming technology based on sensor arrays is widely used to suppress unwanted interfering signals and detect interested target signals. Beam pattern synthesis for arbitrary arrays and robust beamforming for imperfect arrays are two crucial technical issues associated with digital beamforming. In a complex volumetric underwater acoustic transducer array, due to structural shadowing and scattering, the practical beam pattern significantly deviates from its designed pattern, which would greatly degrade the detection and direction-finding performance of the sonar system. An efficient method is proposed to design beam patterns for arbitrary arrays and is applied to robust beamforming for this sonar system. Sea experimental results show that the proposed robust beamforming method achieves a much lower sidelobe level and a much higher direction-finding accuracy than the conventional beamforming technique.

To provide the data needed for testing sonar signal processing schemes, it is desirable to simulate the sonar environment and the received signals. The paper gives a description of a simulator developed for this purpose (Mansour et al., 1995). The simulator runs on a personal computer, which makes it an attractive alternative to more expensive machines, both for system evaluation and for training purposes. It consists of two parts, the environmental part (consisting of the target, reverberation and propagation models) and the system part (providing appropriate signal processing functions). The simulator employs a novel target echo model.

A practical, versatile approach to sonar performance prediction is presented based on addition of acoustic components. Often reverberation is written as a decibel sum of a propagation term and a scattering term. This separation is not possible when back-scattering strength is angle-dependent because the reverberation is an integral over angle. For this reason a linear form of the reverberation sonar equation is used. Examples are presented for a typical deep water problem including a calculation against frequency showing an optimum detection frequency which varies with the target range.

Ocean current profilers, based on spatial or temporal correlation analysis of sound backscattered from passive targets drifting with the current, have several potential advantages over the more common Doppler profilers. Correlation profilers can operate over a longer range with smaller, lighter and cheaper transducers. However, the signal processing is more complex and computationally intensive, increasing transmission intervals and leading to difficulties understanding when and why the performance is poor. Using data gathered from RRS Discovery in early 1995 the authors describe the performance of a 22 kHz acoustic correlation current profiler and compare its results with a lowered acoustic Doppler profiler. At low ship speeds, the signal to noise ratio was sufficient to allow profiling to 1 km depth, with the achievable range reducing as speed increased. Bias and decorrelation occurred in the upper 160 m owing to unknown causes and greatly reduced data quality

Digital signal processing techniques and architectures have evolved rapidly in recent years. As a consequence, the range of potential applications has broadened extensively in both existing areas such as radar and sonar signal processing and in relatively new fields such as communications and industrial condition monitoring. The article reviews the development and principal application areas for digital signal processing. The main architectural approaches to DSP design are then described with reference to practical examples where appropriate. Particular attention is given to programmable architectures, dedicated components and to ASIC-based systems.< >

The possibility of applying some random-signal radar proposals made by Poirier to a random-signal sonar is discussed A proposal for obtaining bearing information is presented, together with some experimental results. It appears that the limitations inherent in this type of random-signal system severely restrict the useful operation of a practical sonar.