Ali Mansour

Modeling of a Complex-Shaped Underwater Vehicle for Robust Control Scheme

The two critical issues of robust control are stable controller synthesis and control performance guarantee in the presence of model uncertainties. Inside all robust stable solutions, small modeling parametric uncertainties lead to better performance controllers. However, the cost to develop an accurate hydrodynamic model, which shrinks the uncertainty intervals, is usually high. Meanwhile, when the robot geometry is complex, it becomes very difficult to identify its dynamic and hydrodynamic parameters. In this paper, the main objective is to find an efficient modeling approach to tune acceptable control design models. A control-oriented modeling approach is proposed for a low-speed semi-AUV (Autonomous Underwater Vehicle) CISCREA, which has complex-shaped structures. The proposed solution uses cost efficient CFD (computational fluid dynamic) software to predict the two hydrodynamic key parameters: The added mass matrix and the damping matrix. Four DOF (degree of freedom) model is built for CISCREA from CFD and verified through experimental results. Numerical and experimental results are compared. In addition, rotational damping CFD solutions are studied using STAR-CCM+ TM. A nonlinear compensator is demonstrated to tune linear yaw model for robust control scheme.

Modeling of a complex-shaped underwater vehicle

A control-oriented modeling approach is proposed for a low-speed semi-AUV (Autonomous Underwater Vehicle) CISCREA, which has complex-shaped structures. Due to the geometry of this AUV, it is very difficult to identify its dynamic and hydrodynamic parameters. Therefore, the main objective of this paper is to find an efficient modeling approach to tune acceptable control design models. The presented solution uses cost efficient CFD (computational fluid dynamic) softwares predicting the two hydrodynamic key parameters: The added mass matrix and the damping matrix. A complete model is built for CISCREA from CFD and verified through experimental results. The results indicate that the proposed computational approach seems to be desirable for the robust control scheme of many complex-shaped AUVs. Finally, Numerical and experimental results are compared.

Spectrum Sensing for Full-Duplex Cognitive Radio Systems

Full-Duplex (FD) transceiver has been proposed to be used in Cognitive Radio (CR) in order to enhance the Secondary User (SU) Data-Rate. In FD CR systems, in order to diagnose the Primary User activity, SU can perform the Spectrum Sensing while operating. Making an accurate decision about the PU state is related to the minimization of the Residual Self Interference (RSI). RSI represents the error of the Self Interference Cancellation (SIC) and the receiver impairments mitigation such as the Non-Linear Distortion (NLD) of the receiver Low-Noise Amplifier (LNA). In this manuscript, we deal with the RSI problem by deriving, at the first stage, the relation between the ROC curves under FD and Half-Duplex (HD) (when SU stops the transmission while sensing the channel). Such relation shows the RSI suppression to be achieved in FD in order to establish an efficient Spectrum Sensing relatively to HD. In the second stage, we deal with the receiver impairments by proposing a new technique to mitigate the NLD of LNA. Our results show the efficiency of this method that can help the Spectrum Sensing to achieve a closed performance under FD to that under HD.

Spectrum Sensing for Half and Full-Duplex Cognitive Radio

Due to the increasing demand of wireless communication services and the limitation in the frequency resources, the Cognitive Radio (CR) has been initially proposed (Mitolal, IEEE Personal Commun 6:13–18, 1999 [1]) in order to solve the spectrum scarcity. CR distinguishes between two types of users, the Primary (PU) and the Secondary (SU) Users. PU has the legal right to use the spectrum bandwidth, while SU is an opportunistic user that can transmit on that bandwidth whenever it is vacant in order to avoid any interference with the signal of PU. Hence the detection of PU becomes a main priority for CR systems. The Spectrum Sensing is performed by CR to monitor PU activities. In actual CR systems (Yucek and Arslan, IEEE Commun Surv Tutorials 11:116–130, 2009 [2]), SU should stop transmitting while Spectrum Sensing is performed. The transmission of SU can be only resumed if PU is still absent. This procedure means that the CR can only operate in a Half-Duplex (HD) mode. Recently, many works have been proposed in order to attend the Full-Duplex (FD) mode. In other words, the Spectrum Sensing should be performed while SU is being active. Our work deals with the HD and FD of CR. First, we develop two Spectrum Sensing algorithms, based on the Cumulative Power Spectral Density (CPSD) of the received signal, dealing with the HD mode. These algorithms outperform the traditional Energy Detector (ED), the well known Cyclostationary Detector (CSD) based on the Generalized Likelihood Ratio Test (GLRT) and the Autocorrelation Detector (ACD). Furthermore, our algorithms are robust against the noise variance, so that the dependence on Noise Uncertainty (NU) presented in ED is avoided. In addition, the proposed algorithms are blind as they don’t require any prior information on the PU’s signal, contrary to Cyclostationary or Waveform detectors. Our algorithms make a decision on the PU presence by comparing the form of CPSD shape to curves depending on the CPSD of the noise. Two algorithms based on hard and soft cooperative schemes are introduced. In these algorithms, the spectrum is divided into two parts. The first part corresponds to negative frequencies, while the second part deals with the positive frequencies. Hence, two test statistics are evaluated, based on the CPSD of each of those two parts, and they are then combined according to the considered scheme. The False Alarm and Detection probabilities of the two proposed algorithms are evaluated analytically under Gaussian and Rayleigh fading channels. We examine our proposed detectors at a low Signal to Noise Ratio. The performance of our detectors is compared to that of ED, CSD and the ACD. Our detectors outperform ED, even at low oversampling rate, where CSD and ACD provide poor performance. Increasing the oversampling rate enhances the performance our algorithms as well as that of ACD and CSD. However, our algorithms remain better than ED, CSD and ACD for all tested values of oversampling rate. Furthermore, our detectors are less sensitive to NU than ED. Besides that, our detectors can be modified to become independent from noise variance and no longer affected by NU problem. The FD transceiver has been proposed to double the channel efficiency by transmitting and receiving in the same band at the same time. The main challenge in FD consists in minimizing the Residual Self Interference (RSI) which represents the error of the Self Interference Cancellation (SIC) and the receiver’s impairments mitigation such as the Non-Linear Distortion (NLD) of the receiver’s Low-Noise Amplifier (LNA). In CR, FD concerns the Secondary User (SU) who can transmit while sensing the channel. Since the SU should be aware of the Primary User (PU) activity, the RSI represents an important challenge for the SU. An ideal situation is achieved by SU when the RSI is totally eliminated, leading the SU to establish a Spectrum Sensing process equivalent to that of HD. In our work, we deal with this problem by analyzing, in the first stage, the impact of the RSI power on the detection process. For that objective, we derive a relation among the RSI power and the probabilities of detection and false alarm under HD and FD modes. To mitigate the NLD of LNA, we analyze the NLD impact on the channel estimation and the Spectrum Sensing Performance. After that, a novel method is proposed to suppress the NLD of LNA without affecting the channel estimation process. Furthermore, our proposed method outperforms significantly other methods proposed in the literature. In addition, using our method, the receiver requires only one training symbol period to perform the estimation of the channel and the NLD estimation. Our results show that an accurate mitigation of the NLD can be reached, which leads to an accurate channel estimation and to reduce the RSI power. Finally, Receiver Operating Characteristic (ROC) curves obtained in FD mode, after applying our method of NLD elimination, are very closed to those of HD mode.

Spatial and time diversities for canonical correlation significance test in spectrum sensing

In this paper, we present a new detector for cognitive radio system based on the Canonical Correlation Significance Test (CCST). Unlike existing CCST approaches, which can only be applied on Multi-Antenna System (MAS), our algorithm can be extended for both Single Antenna System (SAS) and MAS. For SAS, the proposed algorithm exploits the time diversity of cyclostationary signals in order to detect the Primary User (PU) signal. Our simulation results shows that our algorithm outperforms well-known cyclostationary algorithm [9]. For MAS, our algorithm uses both spatial and time diversities to apply the CCST. Numerical results are given to illustrate the performance of our algorithm and verify its efficiency for special noise cases (spatially correlated and spatially colored). The simulation results show the superiority of the performance of the proposed detector compared to the recently CCST proposed algorithm [1].

Fusion of Swath Bathymetric Data: Application to AUV Rapid Environment Assessment

Building an accurate and large digital terrain model (DTM) of the seabed is a key issue in various applications, especially for covert rapid environment assessment using autonomous underwater vehicles (AUVs). New AUV generations are capable of acquiring bathymetry with multiple acoustic sensors: single-beam echosounder, Doppler velocity log, multibeam echosounder (MBES), interferometric sidescan sonar (ISSS), etc. As these sensors acquire the seabed with different geometries, they can be combined to produce a DTM in shorter time. For example, ISSS can reach a wide swath in shallow water but it shows an information gap at nadir, which is usually covered by completing an additional track. Simultaneously using the MBES to acquire the nadir removes the need for this additional track and reduces the energy consumption of the whole survey. This paper focuses on fusion algorithms to extract best information of the two sensors (MBES and ISSS) to feed DTM production software with optimal bathymetric information. This problem may be solved by taking into account the average information of the two sensors. However, the sensors do not always give accurate information and the average information therefore becomes biased. Another way to tackle the problem is to select a priori information given by the better of the two sensors based on a given geometric parameter (e.g., grazing angle). In this case, when the assumed best sensor fails, the information produced by the second sensor cannot be used to compensate for the erroneous information. Our approach consists in using all the available information and fuses it ahead of producing the DTM. Based on the theory of belief functions, this paper presents a framework to fuse the information coming from the two swath bathymetric sensors (MBES and ISSS). The belief theory, applied successfully to other fields, has been extended to handle the bathymetric information. The reliability and the uncertainty of each sonar are introduced in the fusion process to improve the estimation and the accuracy of the final terrain model. First, simulated sonar data, with perfectly known ground truth, are used to quantitatively assess the performance of the fusion process by comparing DTM obtained with and without fusion. Then, the experimental validation is conducted on actual data, acquired simultaneously by the two sonars systems (Klein K5000 ISSS, Reson 8125 MBES) mounted on the DAURADE AUV. Our evaluation of the fusion method shows significant quantitative and qualitative improvement in the production of the final DTM.

Spectrum Sensing and Throughput Analysis for Full-Duplex Cognitive Radio with Hardware Impairments

In Full-d uplex Cognitiv e Radios, the silen t period of the Secondary User (SU) during the Spectrum Sensing can be elimina ted by appl ying the Self -Interf erence Cancella tion (SIC). Due to the channel estima tion error and the hardw are imperf ections (the Phase Noise and the Non-Linear Distortion (NLD)) SIC is not perf ectl y perf ormed and resul ts in the Resid ual Self -Interf erence (RSI) which affects the Spectrum Sensing reliability . In this paper , the effect of RSI on Spectrum Sensing is anal yticall y deriv ed by expressing the detection (pd) and false alarm (pf a) probabilities under FD in terms of the ones under Half -Duplex (HD) (where SU shoul d remain silen t during the Spectrum Sensing period). In addition, an algorithm is proposed to suppress the NLD and improv es the Spectrum Sensing perf ormance. Hereinafter , the SU throughput under FD is anal ysed comparing to HD by deriving the upper and lower bounds to be respected by pf a and pd respectiv ely in order to make FD beneficia rela tiv e to HD. Receiv ed on 30 November 2016; accepted on 07 May 2017; published on 31 May 2017

Spectrum Sensing enhancement using Principal Component Analysis

In this paper, Principal Component Analysis (PCA) techniques are introduced in the context of Cognitive Radio to enhance the Spectrum Sensing performance. PCA step increases the SNR of the Primary Users signal and, consequently, enhances the Spectrum Sensing performance. We applied PCA as a combination scheme of a multi-antenna Cognitive Radio system. Analytic results will be presented to show the effectiveness of this technique by deriving the new SNR obtained after applying PCA, which can be considered a pre-processing step for a classical Spectrum Sensing algorithm. The effect of PCA is examined with well known detectors in Spectrum Sensing, where the proposed technique shows its efficiency. The performance of the proposed technique is corroborated through many simulations.

FPGA implementation of a parameterized Fourier synthesizer

Field-Programmable Gate Array (FPGA) offers advantages for many applications, particularly where missions are complex and time performance is critical. For small-production digital acoustic synthesizers, FPGA can achieve the above-mentioned tighter system requirements with low total system costs on single chip. In this manuscript, a real-time acoustic synthesizer is implemented using Fourier series algorithm on Alteras Cyclone II FPGA chip. This work emphasizes systematic designs and parallel computations. The proposed system includes a flexible processor and a parallel parameterized acoustic module. On one hand, the Nios II embedded processor, which is relatively low-speed component, is used to generate commands and configure high-speed acoustic module parameters. On the other hand, acoustic module which should require high-speed components contains 4 parallel architectures to gain high-speed simultaneous calculus of 4 independent digital timbres. Every timbre is equivalent to 16 parallel high-precision harmonic channels with 0.3 % frequency error. Experimental results corroborate the fact that a single FPGA chip can achieve complex missions and attain real-time performances.