In this study, a new noise model is presented to address the issue of finding an emitting target using time difference of arrival measurements, based on the range between the emitter and a known sensor. To improve the performance of the estimator under the proposed noise model, a weighted version of the spherical interpolation method is proposed and then two weighting matrices required in the method are derived in two different conditions. A detailed theoretical error analysis associated with this algorithm is presented and the Cramer–Rao lower bound is also derived. Simulation studies verify the validity of the proposed error analysis. In addition, in a two-dimensional space and in the case of a minimal number of sensors, the authors analytically determine the sensors layout in which the location solution is not unique. Via simulations, several placements in a covered region are studied to select the appropriate placement in which the root mean square error of the target position estimation is minimised. Furthermore, simulation results show that they can do this work by the derived expression of the error analysis, which leads to the same outcome.

Hybrid fast Fourier transform Adaptive LINear Element (FFT-ADALINE) algorithm for fast and accurate estimation of harmonics is proposed in this study. The FFT method can perform fast conversion from time domain to frequency domain, but it cannot respond immediately to any change of the measured harmonics due to the utilisation of buffer. Meanwhile, ADALINE has better capability to respond immediately due to its learning ability, but its settling time is about two cycles of the measurement signal. In the proposed method, both of the aforementioned algorithms are combined for harmonic estimation where it is able to respond immediately to any change of the measured harmonics and the settling time is reduced to half cycle of the measurement signal. The theory of the proposed algorithm is the application of FFT with weights updating rule to reduce the error of ADALINE instantaneously. The robustness of the proposed method is simulated via MATLAB Simulink. The validity of the simulation work is further proven by the experimental work, which has been done with Chroma programmable AC source model 6590 and non-linear load operations. The proposed algorithm operates in good and accurate performance with the settling time is within half cycle.

In this study, outage performance of orthogonal frequency division multiplexing (OFDM) based underlay cognitive radio (CR) network is analysed. By incorporating full duplex relay selection with amplify and forward relaying strategy, the CR network improves the throughput in the presence of primary user interference. The best relay selection per subcarrier is performed based on the maximum of minimum signal to interference plus noise ratio between source to relay node and relay to destination node. The primary interference signal is modelled as a sparse vector whose non-zero elements follow the Gaussian distribution. Closed form analytical expressions are derived for the end-to-end outage probability of the proposed network and compared with the network operates in half duplex relay selection. It is investigated that the OFDM based CR network with full duplex relay selection achieves higher data rates than the existing CR network. Simulation results are given to validate the derived analytical expressions.

This study proposes a new polarisation smoothing algorithm with multiple-input and multiple-output (MIMO) electromagnetic vector sensor array to address the problem of coherent source angle estimation in MIMO radar. This algorithm can be summarised as follows: (i) matched filtering for echoes is performed to get virtual MIMO array; (ii) the virtual array is divided into six spatially identical subarrays according to polarisation information offered by electromagnetic vector sensor; and (iii) the six subarrays covariance matrices are processed with weighted smoothing to obtain polarisation smoothing covariance matrix, which can restore the rank loss of the covariance matrix of coherent sources. When compared with spatial smoothing, the proposed algorithm is suitable for spatially arbitrary array configuration, without the price of reducing effective array aperture. Simulation results show that the proposed algorithm substantially outperforms the spatial smoothing algorithm.

In this research, the optimal estimation for networked control system with multiplicative noises (MN), one-step random delays and multiple packet dropouts is studied. Both the sensor-to-estimator and controller-to-actuator channels are affected by MN. The one-step random delays and packet dropouts are characterised by a series of Bernoulli variables. By state augmentation, the random circumstances in channels are transformed into random parameters, and the optimal estimators are derived utilising projection theory. For systems without MN, random delays or packet dropouts, the corresponding estimators can be derived as particular cases of the proposed algorithm. Then a sufficient existence condition of steady-state estimators is derived. Some numerical examples are given to validate the proposed estimation algorithms.

A major feature of the Gaussian mixture probability hypothesis density (GM-PHD) filter is that it does not require any measurement-to-track association to complete its update step. This, according to the authors, should constitute significant advantage over conventional data-association based methods, especially in presence of high false-alarm rate, frequent miss-detections and targets in close proximity. To test this hypothesis, a multi-target tracking (MTT) problem using Doppler radar is considered, where the performance of GM-PHD algorithm is compared against six data-association based MTT filters in aforementioned adverse tracking conditions. To handle the non-linearity due to Doppler, cubature Kalman filter (CKF) is used in the framework of all MTT algorithms. Detailed mathematical framework of a new non-linear variant of GM-PHD using CKF has been derived using fundamental principles of non-linear Bayesian filtering. It is named as CK-GM-PHD. CK-GM-PHD is formulated using approximated Gaussian mixture assumption and follows track-oriented approach. Cubature integration method is used to numerically compute mean and covariance of components in the Gaussian mixture. Simulation results support the hypothesis by revealing substantial performance improvement of CK-GM-PHD algorithm over conventional data-association based approaches while tested in moderate to heavy clutter rate with lower detection probability and closely spaced target scenarios.

Nowadays, wavelet packet (WP) based features have been used extensively to maximise the performance of automatic speech recognition in the complex auditory environment. However, wavelet features are less sufficient to represent the voiced speech. Recent researches on WP technique seek for complementary voicing information to overcome this problem. However, considering additional voicing features results in longer dimension and somehow affected the performance for unvoiced speech. This study presents a new analysis of variance technique to incorporate voicing information on WP sub-band based features without affecting its performance and dimension. It has been noticed that most of the voiced energy lies below 2 kHz. Thus, the proposed technique emphasises the lower sub-bands for additional voicing information. Harmonic energy features are combined with recently introduced auditory motivated equivalent rectangular bandwidth like 24-band WP cepstral features to enhance the performance of voiced phoneme recogniser. Primarily, a standard phonetically balanced Hindi database is used to analyse the performance of the proposed technique across a wide range of signal-to-noise ratios. Proposed features show a promising result in phoneme recognition experiment without affecting the feature dimension and performance.

In this study, important aspects concerning the stability of the QR decomposition (QRD) through the modified Gram-Schmidt (GS) orthogonalisation procedure with application in multiple-input–multiple-output (MIMO) detection are investigated. In particular, the numerical stability of GS-QRD is analysed through the condition number, considering a matrix with Gaussian entries, which is a very special class of matrix, especially for telecommunication systems in general and for MIMO system in particular. The condition number is analysed in the average sense, aided by random processes theory, including in special the central limit theorem, random variable transformation and moment generating functions. An analytical bound for the condition number is found and corroborated by numerical simulations.

In this paper, we make use of hidden Markov model (HMM) state alignment information in i-vector/probabilistic linear discriminant analysis (PLDA) framework to improve the verification performance in a text-dependent single utterance (TDSU) task. In the TDSU task, speakers repeat a fixed utterance in both enrollment and authentication sessions. Despite Gaussian mixture models (GMMs) have been the dominant modeling technique for text-independent applications, an HMM based method might be better suited for the TDSU task since it captures the co-articulation information better. Recently, powerful channel compensation techniques such as joint factor analysis (JFA), i-vectors and PLDA have been proposed for GMM based text-independent speaker verification. In this study, we train a separate i-vector/PLDA model for each sentence HMM state in order to utilize the alignment information of the HMM states in a TDSU task. The proposed method is tested using a multi-channel speaker verification database. In the experiments, it is observed that HMM state based i-vector/PLDA (i-vector/PLDA-HMM) provides approximately 67% relative reduction in equal error rate (EER) when compared to the i-vector/PLDA. The proposed method also outperforms the baseline GMM and sentence HMM methods. It yields approximately 51% relative reduction in EER over the best performing sentence HMM method.

The estimation of the time differences of arrival (TDOAs) is significant in passive source localisation systems. The TDOA estimation accuracy may directly affect the source location performance. For co-frequency interference environments, the authors address the problem of the passive blind estimation of time-delays for uncorrelated interference source signals based on wireless sensor networks. The received mixtures at the sensors are modelled as unknown linear combinations of the differently delayed versions of the communication signal and the interference signal. Blind source separation and secondary interference signal extracting are both introduced in the proposed method. The interference signals in the mixed receiving signals of all the sensors are extracted effectively and the effect of the mixed communication signals can be significantly reduced. Simulations show that the proposed method has a more accurate performance compared to other TDOA estimation methods, and is therefore valid and practical in the TDOA localisation systems.

This study presents a new method for the reconstruction of block-sparse signals with and without noisy perturbations, termed weighted double-backtracking matching pursuit (WDBMP). Unlike anterior block-sparse reconstruction algorithms, WDBMP requires no prior knowledge about block length and boundaries. It not only refines the current approximation based on energy, but also takes advantage of block structure to refine the chosen support set, and thus to improve the recovery performance. Moreover, the authors propose weighted proxy to select the candidates, which can increase the probability of selecting correct supports and improve the convergence speed. Experimental results show that the proposed algorithm owns better recovery quality and requires fewer iterations to converge compared with the existing block-sparse reconstruction algorithms without knowing the block-sparse boundaries.

The optimum bit allocation (OBA) problem was first investigated by Huang and Schultheiss in 1963. They solved the problem allowing the bits to be signed real numbers. Later, different algorithms were proposed for OBA problem when the bits were constrained to be integer and non-negative. In 2006, Farber and Zeger proposed new algorithms for solving optimum integer bit allocation (OIBA) and optimum non-negative integer bit allocation (ONIBA). None of the existing algorithms for OIBA and ONIBA problems end with an analytical solution. In this study, a new analytical solution is proposed for OIBA and ONIBA problems based on a novel analytical optimisation approach. At first, a closed form solution is derived for Lagrange unconstraint problem. Then, by removing the Lagrange multiplier, an analytical solution is obtained for OIBA and ONIBA problems. Using the selection and bisection algorithms, a low complexity algorithm is proposed for searching in a group of discrete functions which can reduce the computational complexity of the analytical solution. The complexity of computing the analytical solution is O(k) which is much lower than the complexity of existing ONIBA algorithms.

This study focuses on channel estimation scheme in multicarrier-interleave division multiple access (MC-IDMA)-based wireless communications. Specifically, a new adaptive algorithm is derived and proposed for implementation of the channel estimation in the MC-IDMA system. The proposed algorithm is named reweighted regularised variable step size normalised least mean square (R-RVSSNLMS). The proposed algorithm-based channel estimator exploits inherent sparsity in the orthogonal frequency division multiplexing channels in order to enhance its performance. Computer simulation results that show the comparison of the performance of the R-RVSSNLMS-based channel estimator with that of the channel estimators based on some families of least mean square algorithms are documented in this study. The results show that the performance of the proposed R-RVSSNLMS-based channel estimator is better than that of the other conventional estimators presented in this study. However, the proposed channel estimator exhibits negligible high computational complexity in comparison with other channel estimators considered in this study for the MC-IDMA system.

Optimisation based design of finite impulse response (FIR) filters has been an active area of research for quite some time. The various algorithms proposed for FIR filter design aim at meeting a set of desired specifications in the frequency domain. Evolutionary algorithms have been found to be very effective for FIR filter design because of the non-linear, non-differentiable and non-convex nature of the associated optimisation problem. The present work proposes two modified versions of a recently developed evolutionary technique i.e. artificial bee colony (ABC) algorithm for design of FIR filters. The applicability of the proposed approach has been evaluated by comparing its response with conventional reported filter design techniques. The proposed variants of ABC are found to outperform other non-convex algorithms in achieving the desired specifications. In addition to the simulation results, the designed filters have been implemented in hardware using Xilinx-xc7vx330t-3ffg1157 (Virtex-7) field programmable gate array. The hardware implementation allows validation of the proposed techniques for practical filtering applications by considering real time operation parameters.

Conventional multiband speech enhancement involves splitting the spectrum into various frequency bins and performing speech enhancement in each band independently. However, owing to the pole-interaction problem in the spectral domain, estimation of clean speech from the formants, suppressed by the influence of the formants in the neighbouring bands, may result in poor quality. To reduce the influence of stronger formants over the neighbouring bands, in the current work, clean speech is estimated by filtering unprocessed speech in the temporal domain into various equivalent rectangular bandwidth based subbands followed by discrete cosine transform (DCT) based spectral speech enhancement in each band using spectral subtraction/minimum mean square error (MMSE). To further enhance speech, a spectral subtraction-based approach that incorporates band-specific weighting factor obtained using respective band signal-to-noise ratio (SNR), and an MMSE estimator that calculates apriori speech presence/absence probability based on local and global apriori SNR rather than a fixed/equiprobable value are proposed. The performance of the algorithms is evaluated using perceptual evaluation of speech quality and composite speech quality measure. It is observed that DCT-derived spectrum based temporal-domain multiband speech enhancement algorithm outperforms the existing techniques for car, babble, train, white, and factory noise in the 0–10 dB SNR levels.

In disaster rescue, trapped survivors with regular respiration can be located, by detecting regular respiratory signals (RRSs) acquired with life-detection radar systems. RRSs are often weak in these scenarios, due to the attenuation of the electromagnetic waves that propagate through debris. Thus, detecting RRSs under low signal-to-noise ratio is a key challenge in this application. In this study, RRS detection in additive white Gaussian noise was investigated from a statistical signal processing viewpoint, and a modified generalised-likelihood ratio test (GLRT) was derived. With proper parameter settings, the modified GLRT (MG) could achieve a notable detection gain over the periodogram test and the harmogram test, two classical periodic signal detectors. Thus, the proposed MG could be used to improve the detection performance of the life-detection radar systems used in disaster rescue applications.

This study proposes a novel in-flight initial alignment scheme for the strapdown inertial navigation system (SINS) in the Arctic, aiming to solve the attitude divergence problem caused by the inherent SINS error characteristics. Considering the special geographical conditions in the Arctic, the authors establish the SINS mechanisation equations and radar equations in the grid frame in this work. In the coarse alignment stage, the radar information is employed to solve the nonlinear equations by using the multi-population genetic algorithm (MGA), and then the unscented Kalman filter is applied to diminish the noise influence on MGA results. During the fine alignment process, the attitude information is further corrected by the Radar/SINS integrated navigation system under the Arctic coordinate frame. At last, numerical simulations are performed, and the results demonstrate that the proposed scheme achieves better accuracy compared with traditional approaches.

In this study, the authors present GASG21 (Grassmannian adaptive stochastic gradient for L _2,1 norm minimisation), an adaptive stochastic gradient algorithm to robustly recover the low-rank subspace from a large matrix. In the presence of column outliers corruption, the authors reformulate the classical matrix L _2,1 norm minimisation problem as its stochastic programming counterpart. For each observed data vector, the low-rank subspace is updated by taking a gradient step along the geodesic of Grassmannian. In order to accelerate the convergence rate of the stochastic gradient method, the authors choose to adaptively tune the constant step-size by leveraging the consecutive gradients. Numerical experiments on synthetic data and the extended Yale face dataset demonstrate the efficiency and accuracy of the proposed GASG21 algorithm even with heavy column outliers corruption.