IET Radar, Sonar & Navigation
Volume 11, Issue 12, December 2017
Volumes & issues:
Volume 11, Issue 12
December 2017
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- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1737 –1739
- DOI: 10.1049/iet-rsn.2017.0490
- Type: Article
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- Author(s): Gabriele Ferri* ; Andrea Munafò* ; Alessandra Tesei ; Paolo Braca ; Florian Meyer ; Konstantinos Pelekanakis ; Roberto Petroccia ; João Alves ; Christopher Strode ; Kevin LePage
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1740 –1761
- DOI: 10.1049/iet-rsn.2017.0074
- Type: Article
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Underwater surveillance has traditionally been carried out by means of surface and undersea manned vessels equipped with advanced sensor systems. This approach is often costly and manpower intensive. Marine robotics is an emerging technological area that enables the development of advanced networks for underwater surveillance applications. In contrast with the use of standard assets, these advanced networks are typically composed of small, low-power, and possibly mobile robots, which have limited endurance, processing and wireless communication capabilities. When deployed in a region of interest, these robots can cooperatively form an intelligent network achieving high performance with significant features of scalability, adaptability, robustness, persistence and reliability. Such networks of robots can be the enabling technology for a wide range of applications in the maritime domain. However, they also introduce new challenges for underwater distributed sensing, data processing and analysis, autonomy and communications. The main thrust of this study is to review the underwater surveillance scenario within a framework of four research areas: (i) underwater robotics, (ii) acoustic signal processing, (iii) tracking and distributed information fusion, and (iv) underwater communications networks. Progress in each of these areas as well as future challenges is presented.
- Author(s): Veronika Yordanova ; Hugh Griffiths ; Stephen Hailes
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1762 –1769
- DOI: 10.1049/iet-rsn.2017.0098
- Type: Article
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Multiple autonomous underwater vehicles (AUVs) are a potential alternative to conventional large manned vessels for mine countermeasure (MCM) operations. Online mission planning for cooperative multi-AUV network often rely on predefined contingencies of reactive methods and does not deliver an optimal end-goal performance. Markov decision process is a decision-making framework that allows an optimal solution, taking into account future decision estimates, rather than having a myopic view. However, most real-world problems are too complex to be represented by this framework. The authors deal with the complexity problem by abstracting the MCM scenario with a reduced state and action space, yet retaining the information that defines the goal and constraints coming from the application. Another critical part of the model is the ability of the vehicles to communicate and enable a cooperative mission. They use the rendezvous point (RP) method. The RP method schedules meeting points for the vehicles throughout the mission. The authors’ model provides an optimal action selection solution for the multi-AUV MCM problem. The computation of the mission plan is performed in the order of minutes. This quick execution demonstrates the model is feasible for real-time applications.
- Author(s): Daniel Angley ; Branko Ristic ; Sofia Suvorova ; Bill Moran ; Fiona Fletcher ; Han Gaetjens ; Sergey Simakov
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1770 –1775
- DOI: 10.1049/iet-rsn.2017.0076
- Type: Article
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Sonobuoy fields, consisting of many distributed emitter and receiver sonar sensors on buoys, are used to seek and track underwater targets in a defined search area. The authors seek a scheduling protocol, selecting both the emitter and its waveform in each time interval that optimises tracking performance. This study describes a stationary scheduling algorithm for sonobuoy fields called the continuous probability states algorithm. The algorithm replaces a full partially observed Markov decision process by a computationally feasible Markov decision process by focusing on probability of target detection. This approach is shown to result in high-quality tracks for multiple targets in a realistic simulation of a sonobuoy field.
- Author(s): Thomas Powers ; David W. Krout ; Jeff Bilmes ; Les Atlas
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1776 –1781
- DOI: 10.1049/iet-rsn.2017.0075
- Type: Article
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The authors develop a framework to select a subset of sensors from a field in which the sensors have an ingrained independence structure. Given an arbitrary independence pattern, the authors construct a graph that denotes pairwise independence between sensors, which means those sensors may operate simultaneously without interfering. The set of all fully-connected subgraphs (cliques) of this independence graph forms the independent sets of matroids over which the authors maximise the average and minimum of a set of submodular objective functions. The average case is submodular, so it can be approximated. The minimum case is both non-submodular and inapproximable. The authors propose a novel algorithm GENSAT that exploits submodularity and, as a result, returns a near-optimal solution with approximation guarantees on a relaxed problem that are within a small factor of the average case scenario. The authors apply this framework to ping sequence optimisation for active multistatic sonar arrays by maximising sensor coverage for average and minimum case scenarios and derive lower bounds for minimum probability of detection for a fractional number of targets. In these ping sequence optimisation simulations, GENSAT exceeds the fractional lower bounds and reaches near-optimal performance, and submodular function optimisation vastly outperforms traditional approaches and nearly achieves optimal performance.
- Author(s): Xionghou Liu ; Chao Sun ; Yixin Yang ; Jie Zhuo
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1782 –1789
- DOI: 10.1049/iet-rsn.2016.0557
- Type: Article
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A multiple-input multiple-output (MIMO) sonar can form a large-aperture virtual uniform linear array (ULA) from a small number of physical elements. However, the large-aperture MIMO sonar encounters a dilemma between the computation load and the imaging performance. Since the traditional phase-shift beamformer (PSBF) and shifted sideband beamformer (SSBF) cannot solve the dilemma individually, the authors proposed a hybrid beamformer (HBF) which combines PSBF and SSBF together. Specifically, the proposed HBF contains the off- and on-line processing. In the off-line processing SSBF is applied to the transmitting ULA to process transmitting waveforms. In the on-line processing PSBF is applied to the receiving ULA, and outputs of off-line SSBFs are utilised to matched filter outputs of PSBFs. Thus, in the proposed HBF the time delays of SSBF is calculated off-line; and the multi-beam processing is simplified as PSBFs on the receiving ULA. Consequently, the proposed HBF can reduce the computation load of the large-aperture MIMO sonar effectively while obtaining a fine imaging performance. Numerical simulations show the effectiveness of the proposed HBF.
- Author(s): Branko Ristic ; Daniel Angley ; Sofia Suvorova ; Bill Moran ; Fiona Fletcher ; Han Gaetjens ; Sergey Simakov
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1790 –1797
- DOI: 10.1049/iet-rsn.2017.0077
- Type: Article
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Sonobuoy fields, consisting of a large network of emitter and receiver sonar sensors on buoys, are increasingly being used for detection and tracking of underwater targets in a defined maritime area. This study presents a Gaussian mixture version of a multitarget–multisensor (MS) Bayesian-type tracker developed specifically for multistatic sonobuoy fields. Its foundation is the optimal Bayesian MS filter for a single target in clutter. The multi target feature is incorporated using the linear-multitarget paradigm, which is a fast and accurate approximation assuming the density of underwater targets is low. Reliable track initiation and false track discrimination for low signal-to-noise ratio targets are achieved using the amplitude feature of reported detections. The developed tracker is capable of processing measurements with different modalities, depending on the transmitted signal waveform. It is integrated and tested within a realistic multistatic sonar emulator developed by DST Group.
- Author(s): Liang Liu and Ping Wei
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1798 –1801
- DOI: 10.1049/iet-rsn.2017.0086
- Type: Article
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Multiple antenna systems with sub-Nyquist sampling technique have been applied to jointly estimate frequency and direction-of-arrival of narrowband far-field signals. Unlike the current some techniques, where only one signal can be handled in some certain subband, the authors propose a novel technique to deal with the case of multiple signals. The authors first build a new time-space union model for this case and analyse the identification of the model, and then provide the maximum number of signals which can be classified. The authors also prove that the Cramér-Rao Bound (CRB) of the model is lower than the CRB based on Nyquist sampling. Simulation results verify the identification capacity and show that the proposed algorithm has a superior estimation performance close to CRB especially in the case of the minimum redundancy array case.
Guest Editorial: Sonar Multi-sensor Applications and Techniques
Cooperative robotic networks for underwater surveillance: an overview
Rendezvous planning for multiple autonomous underwater vehicles using a Markov decision process
Non-myopic sensor scheduling for multistatic sonobuoy fields
Constrained robust submodular sensor selection with application to multistatic sonar arrays
Hybrid phase shift and shifted sideband beamforming for large-aperture MIMO sonar imaging
Gaussian mixture multitarget–multisensor Bernoulli tracker for multistatic sonobuoy fields
Joint DOA and frequency estimation with sub-Nyquist sampling for more sources than sensors
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- Author(s): Ding Cao ; Shenghua Zhou ; Hongwei Liu ; Jun Liu ; Huikai Zang
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1802 –1814
- DOI: 10.1049/iet-rsn.2017.0159
- Type: Article
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Decentralised detection using censored observations is an effective way to mitigate communication constraints where each sensor only transmits the ‘informative’ observations to a fusion centre (FC), and censors those deemed ‘uninformative’. In this study, a censoring rule is used at each spatial diversity channel such that only the observation exceeding a local threshold is transmitted, where the local threshold controls the communication rate. In particular, the authors consider a scenario where there exists a communication constraint at the FC. To design a proper communication bandwidth allocation (CBA) algorithm, two Ali-Silvey distance measures, i.e. the J-divergence (JD) and Bhattacharyya distance (BD), are utilised as objective functions to achieve a manageable optimisation procedure for designing local thresholds. The CBA algorithm is proposed by solving a constrained non-linear programming problem through the sequential quadratic programming method. Besides, the detection performance is analysed under the censoring scheme, given the optimised local thresholds. Numerical results indicate that the two Ali-Silvey distance-based algorithms can outperform the uniform bandwidth allocation strategy; meanwhile, the BD-based method performs better at high signal-to-noise scenarios but slightly worse at low, compared with the JD-based method.
- Author(s): Sufyan Memon ; Hungsun Son ; Kashif Hussain Memon ; Arsalan Ansari
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1815 –1821
- DOI: 10.1049/iet-rsn.2017.0019
- Type: Article
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An automatic target tracking algorithm must be capable of dealing with an unknown number of targets and their trajectory behaviour inside the surveillance region. However, due to target motion uncertainties, heavily populated clutter measurements and low detection probabilities of targets, the smoothing algorithms often fail to detect the true number of target trajectories. In this study, the authors discussed some deficiencies and insignificances of existing smoothing algorithms and proposed a new smoothing data association based algorithm called fixed-interval integrated track splitting smoothing (ITS-S). The proposed algorithm employ smoothing data association algorithm and compared with existing smoothing algorithms outperform in terms of target trajectory accuracy and false-track discrimination (FTD). However, existing algorithms fail to generate smoothed target trajectory and provides insignificant FTD performance in such difficult environments as illustrated in this simulation study. The ITS-S shows improved smoothing performance compared with that of existing algorithms for a manoeuvering target tracking in a heavily populated cluttered environment and low detection probabilities.
- Author(s): Jianhui Wang ; Ying Xu ; Ruidan Luo ; Yi Zhang ; Hong Yuan
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1822 –1830
- DOI: 10.1049/iet-rsn.2017.0164
- Type: Article
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Ground-based pseudolite positioning systems are valuable in environments where global positioning system (GPS) is not available. To mitigate near-far effect, pseudolite positioning systems often use pulsed signals instead of GPS-like continuous signals. The simple pulse scheme without slot-permutation cannot preserve the spectrum shape of the original un-pulsed signal, and this may result in false frequency-lock in the receiver. Complicated pseudorandom pulse schemes are often used to avoid false frequency-lock, which increases acquisition and tracking complexity. The synchronisation method presented in this study can acquire and track pulsed signals without false frequency-lock under the pulse scheme without slot-permutation, which enables the use of this pulse scheme in pseudolite positioning systems and is useful for reducing complexity of transmitter and receiver. Simulation results show that the presented method can track code phase, carrier phase and Doppler frequency accurately and correctly.
- Author(s): Jiangbo Liu ; Wei Xie ; Guan Gui ; Qing Zhang ; Yanbin Zou ; Qun Wan
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1831 –1838
- DOI: 10.1049/iet-rsn.2017.0245
- Type: Article
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Three novel adaptive beamforming algorithms are developed to mitigate the steering vector (SV) random errors of both the desired signal and interferences. With the use of iterative robust Capon beamforming technique, each interference's SV is estimated first, and then the interference-plus-noise covariance matrix is reconstructed according to its definition. To accurately estimate the signal of interest's (SOI's) SV, the authors introduce three new methods, which are based on the techniques of the second-order cone programming, the eigendecomposition and the oracle approximating shrinkage. For the proposed beamformers, only the prior information about the array geometry and the SOI's angular sector are needed. The main advantage is that the proposed methods are robust against a variety of SV mismatches. Simulation results are presented to demonstrate the robustness and effectiveness of the proposed adaptive beamformers.
- Author(s): Yifei Zhang ; Min Zhang ; Fucheng Guo
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1839 –1844
- DOI: 10.1049/iet-rsn.2017.0097
- Type: Article
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The recursive solution to passive localisation of a scanning emitter using time of interception (TOI) by a single moving observer is investigated in this study. First, the correlated measurement noise in the traditional pseudo-linear model for the scanning emitter localisation is decoupled by introducing the first noise corrupted direction of arrival as a nuisance parameter. A recursive pseudo-linear least square (RPLS) estimator is then developed by transforming the TOI of the scanning emitter into the direction difference of arrival model. However, there is bias in this method due to the correlation between the measurement matrix and the noise. To reduce the bias, the instrumental variable (IV) method is used, and two recursive IV (RIV) estimators, forgetting factor based RIV (F2RIV) and Taylor series expansion based RIV (TSRIV), are proposed. It approximates the IV matrix recursively using the Taylor series expansion in the TSRIV estimator. Simulations show that the TSRIV estimator alleviates the bias compared with the RPLS estimator dramatically and can reach the Cramer–Rao lower bound at moderate measurement noise levels. Moreover, it is more stable and has shorter convergence time than the F2RIV estimator.
- Author(s): Yi-lin Wang ; Shi-long Ma ; Zhan Fan ; Guo-Long Liang ; Qing Li
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1845 –1853
- DOI: 10.1049/iet-rsn.2017.0135
- Type: Article
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The discrete Fourier transform (DFT) of observation sequence is usually used in the detection of tonal signal. A tone without any prior information cannot be guaranteed to lie exactly on the discrete DFT frequency bin, which will degrade the performance of the tonal detector based on DFT. In this work, the authors study the detection problem for a tone deviated from the discrete frequency bin. A generalised likelihood ratio detector utilising the information of adjacent frequency bins is designed. Theoretical analysis, computer simulations, and lake experiment show that the proposed detector is robust against the deviation and is more suitable for the tone detection in practical circumstances.
- Author(s): Ebrahim Sabzikar ; Mohammad Hossein Ferdowsi ; Ali Pourmohammad
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1854 –1864
- DOI: 10.1049/iet-rsn.2017.0206
- Type: Article
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This study presents a subspace-based tracking algorithm called Bingham filter using the outputs of a sensor array. Given n sensors in the sensor array with spatially and temporally white Gaussian noise in the outputs of the array, the Bingham filter estimates the signal subspaces generated by the targets based on the phased array observations, a priori information and previous estimates. Assuming the complex Bingham distribution as the probability distribution of the target subspace, the authors propose a closed-form solution to the problem of updating the predicted subspace using the phased array observations. Furthermore, the authors offer a closed-form solution for the prediction step of the tracking algorithm based on the relation between the complex Bingham distribution parameters and its covariance matrix. Combining these two steps, a new subspace tracking algorithm is derived that is similar to the Kalman filter. The algorithm is applied to track underwater targets and its efficiency is investigated using simulated phased array observations. Simulation results show that compared with the multiple signal classification (MUSIC) algorithm alone, the combination of the Bingham filter and MUSIC algorithm has better performance in low SNR scenarios.
- Author(s): Wei-Lung Mao and Han-Yi Huang
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1865 –1876
- DOI: 10.1049/iet-rsn.2017.0145
- Type: Article
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Ionospheric scintillation caused by charged particles results in rapid phase and amplitude fluctuations of a received GPS signal. This obstacle can degrade and disable some signal processing in the GPS sensors. There is a significant interest in the development of global navigation satellite system sensors that can track through scintillations more reliably than current receivers. This study presents an adaptive proportional-integral-derivative (PID)-based carrier loop with a radial basis function (RBF) network identification for robust carrier phase tracking under scintillation circumstance. The incremental PID method that provides lower computational complexity and self-learning capability is employed to control the carrier loop based on online identification with gradient descent learning algorithms. The RBF network structure is utilised here to perform the system identification and it can be used to predict the Jacobian information of the controlled model in the tracking loop system. The proposed architecture is compared with conventional tracking loops under diverse scintillation strength and loop noise bandwidth conditions. The simulation results show that the authors method indeed achieves better tracking capability in terms of carrier phase average mean square error, phase error standard deviation, and sum absolute error when the severe scintillation conditions are encountered.
- Author(s): Yuankai Wang ; Zelong Xiao ; Li Wu ; Jianzhong Xu ; Huichang Zhao
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1877 –1885
- DOI: 10.1049/iet-rsn.2017.0236
- Type: Article
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The chirp sequence waveform is one of the most commonly used waveforms for automotive frequency-modulated continuous wave radars. When a chirp sequence waveform is applied, the range–Doppler processing method based on two-dimensional fast Fourier transform can be used to extract the range and velocity parameters of multiple targets from the beat signals. However, conventional chirp sequence waveforms suffer from the Doppler ambiguity problem, which results in ambiguities in velocity measurement. In this study, the authors propose a novel jittered chirp sequence waveform. In the proposed waveform, the initial times of the chirps are randomly jittered. According to the compressed sensing theory, they establish the parameter design principle for the new waveform, and apply sparse recovery method to achieve unambiguous Doppler processing. Subspace pursuit algorithm is recommended as the suitable sparse recovery method for Doppler processing due to its low complexity and stable recovery performance. The effectiveness of the proposed waveform and its corresponding signal processing method are verified by simulation and experimental results.
- Author(s): He Tian and Daojing Li
- Source: IET Radar, Sonar & Navigation, Volume 11, Issue 12, p. 1886 –1891
- DOI: 10.1049/iet-rsn.2017.0087
- Type: Article
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Microwave radar 3D imaging with high resolution generally requires a great number of samples. The authors aim at accurate reconstruction of microwave radar images while significantly reducing the required number of samples. A novel algorithm is proposed which realises sparse sampling with nearly 50% data reduction and high-quality restoration, based on interferometry and principal component analysis (PCA) in frequency domain. Interferometric processing is utilised to concentrate the frequency spectrum into low-frequency stage, thereby reaching an effective sparse representation of radar image. Furthermore, PCA is introduced to reform radar image according to its principal characteristics in frequency spectrum, without side-lobe artefacts and receiver noise. Experimental data in anechoic chamber demonstrates the great potential of the proposed approach.
Signal censoring and fusing with system-level communication constraints in multistatic radar: a J-divergence and Bhattacharyya distance-based approach
Multi-scan smoothing for tracking manoeuvering target trajectory in heavy cluttered environment
Synchronisation method for pulsed pseudolite positioning signal under the pulse scheme without slot-permutation
Adaptive beamforming algorithms with robustness against steering vector mismatch of signals
Recursive instrumental variable method for locating a scanning emitter by a single observer using time of interception
Robust DFT-based generalised likelihood ratio test for underwater tone detection
Subspace tracking in a sensor array using complex Bingham distribution
Mitigation of ionospheric scintillation effect using adaptive PID-based tracking loop in GPS receivers
Jittered chirp sequence waveform in combination with CS-based unambiguous Doppler processing for automotive frequency-modulated continuous wave radar
Sparse sampling-based microwave 3D imaging using interferometry and frequency-domain principal component analysis
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