In this study, the newly derived Student's t -based Kalman filter (STKF) is re-derived from Bayesian maximum a posterior perspective for linear systems with heavy-tailed measurement noises. This re-derivation reveals that the STKF is an M-estimator with Cauchy function as the robust cost function. The presented re-derivation can also be used as the unified procedure to derive robust Kalman-type filters by assuming the likelihood probability density function to be elliptical distributions.

This study proposes an approach for global positioning system (GPS) precise time transfer using the augmentation information and zero-differenced precise point positioning (PPP) models. The augmentation information can be real-time generated by constraining the coordinates of the reference stations, it is the difference between the observed distance and the real distance from the satellite to the receiver after considering the modelling errors. As the augmentation information contains the ionosphere delay, troposphere delay, receiver clock, phase ambiguities, ephemeris residuals and the other un-modelling errors, thus some common errors are eliminated and/or reduced after used the augmentation information, then the zero-differenced PPP model can be used for data solution and precise time transfer service. Two pairs of datasets were used to validate the feasibility and effectiveness of the proposed approach. The results show that the accuracy and stability of time transfer are greatly improved by the new approach, especially for the zero baseline. Compared with the traditional PPP time transfer approach, which relies on the precise ephemeris, the new approach used the augmentation correction which contains the ephemeris residuals, thus it can be operated by both precise ephemeris and broadcast ephemeris, and is hence more effective for real-time operation.

A space-time coding array (STCA) system transmits an identical waveform with a small time offset across the array elements. It shares the characteristics of high angular resolution and instant wide angular coverage, leading to the low probability of interception as seen in the traditional multiple-input and multiple-output radar. Angle-time two-dimensional matched filter is required in the STCA. However, the sidelobe level (SLL) of the multi-dimensional ambiguity function in the range and angle domains is not as low as that desired in practical applications and the range resolution decreases as the number of elements increases. Here, two kinds of subarray-based time-delay methods are proposed to improve the resolution and reduce the SLL of the ambiguity function for STCA. Both the regular and irregular subarrays are considered, which increase the degrees of freedom and thus enhance the range resolution. Moreover, the SLL of the ambiguity function is reduced. Simulation results are provided to demonstrate the effectiveness of the proposed methods.

Dynamic hand gesture recognition is of great importance in human–computer interaction. In this study, the authors investigate the effect of sparsity-driven time–frequency analysis on hand gesture classification. The time–frequency spectrogram is first obtained by sparsity-driven time–frequency analysis. Then three empirical micro-Doppler features are extracted from the time–frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time–frequency analysis techniques, sparsity-driven time–frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification.

The use of autonomous underwater vehicles (AUVs) cooperating in a network for anti-submarine warfare surveillance operations is of topical interest. Each AUV has to localise and track targets (submarines) robustly and precisely. This can be realised by a bistatic sonar configuration. The precise knowledge of the bistatic system parameters is mandatory for target tracking. These are in particular the positions of the acoustic sources, transmission times, and the position and heading of the AUV sonar sensor. However, these parameters often are not precisely known, e.g. for non-cooperative acoustic sources or due to navigation uncertainties of the AUV. Therefore, the authors consider the inverse problem, i.e. they estimate the bistatic system parameters by exploiting sonar echoes from known stationary ‘targets’ like wrecks or small islands. Their implementation of the estimation method is based on the multihypothesis tracking technique. Results are discussed for two applications: The first one is estimation of the parameters of a non-cooperative source. The second application focuses on the estimation of the receiver parameters; in particular they show that their approach can be used to increase robustness of AUV navigation. Their algorithms are tested with simulated and real data recorded by the Centre for Maritime Research and Experimentation.

Dynamic alignment is a challenging issue for the strapdown inertial navigation system (SINS), especially in the case of no Global Position System assistance. This study proposes a novel coarse alignment algorithm for b-frame velocity-aided SINS. The attitude is aligned with the optimisation-based alignment method, and the alignment process is treated in the inertial frame, so the Coriolis effect can be partly excluded. Meanwhile, the position of SINS is updated in real time by rigorous dead reckoning method. Simulation and experimental results prove that the proposed method has a good performance in the dynamic coarse alignment of SINS.

A spectrum analysis method is presented that can be used on high-resolution synthetic aperture radar (SAR) measurements to concentrate the returns from overhead power cables into discrete angles, alongside the resulting images and information obtained by applying the technique to spotlight SAR data obtained at Ka-band. The angular width and position of the specular and Bragg lobes are used to estimate the periodic length of the cable structure. The results presented also show the potential of detecting variations in the tautness of a power line along its length and hence make any vulnerabilities to the supply of electrical power detectable remotely. Detailed high-resolution images constructed also support peculiarities measured in these signatures. The presented results show that the described method has the ability to detect distribution line vulnerabilities and hazards to the surrounding area. As the cable structure is also related to temperature, this technique has the potential to observe changes in the climate.

As tracked vehicles play significant roles in a battlefield, effective jamming measures are necessary to protect them from being perceived by a hostile radar. Moving tracked vehicles usually exhibit strong Doppler and micro-Doppler signatures. Therefore, the jamming signal should include micro-Doppler modulation generated by metallic caterpillars for successful deceptive jamming. Based on detailed analysis of kinetic characteristics of tracked vehicles, this study proposes a novel deceptive jamming method for tracked vehicles against a continuous-wave ground surveillance radar. To guarantee the fidelity of the deceptive jamming, this method performs both translational modulation for rigid parts and micro-Doppler modulation for the caterpillars. Moreover, the translational modulation function is generated partly off-line to improve the computational efficiency. To evaluate the performance of the proposed approach quantitatively, evaluation indices, such as human visual system and wavelets weighted mean square error, are designed. Simulation results are presented to verify the validity of the proposed method.

This study proposes a bilinear recursive least square based adaptive multisensor data fusion technique for the precise localisation of railway vehicles and detection of an accidental train parting to be used with the train collision avoidance system (TCAS) in Indian railways. The accurate localisation of railway vehicles during the absence of global positioning system (GPS) is a challenging task for the TCAS. One of the reliable solutions for this task may be the augmentation of GPS with the onboard multisensor system. A bilinear recursive least square adaptive filter is used here to estimate and compensate the position error of the onboard multisensor system. The impact of slack in coupling is considered for the analysis of parting detection. The performance of the proposed technique is compared with the observation error based approach, bounded offset based approach and pseudo-measurement state constraining technique. The simulation results indicate that the proposed technique is superior in terms of positional accuracy and for the detection of an accidental train parting with a minimum parting distance.

Radar emitter classification (REC) is an essential part of electronic warfare (EW) systems. In REC tasks, after deinterleaving, the intercepted radar signals are classified into specific radar types. With new radar types arising and the electromagnetism environment getting complicated, REC has become a big data problem. Meanwhile, there exist inconsistent features among samples. These two problems can affect the performance of classification. In this work, first, the authors designed a novel encoding method to deal with the inconsistent features. High-dimension sequences of equal length are generated as new features. Then a deep learning model named unidimensional convolutional neural network (U-CNN) is proposed to classify the encoded high-dimension sequences with big data. A large and complex radar emitter's dataset is used to evaluate the performance of the U-CNN model with the encoding method. Experiments show that the authors' proposal gains an improvement of 2–3% in accuracy compared with the state-of-the-art methods, while the time consumed for identifying 45,509 emitters is only 1.95 s using a GPU. Specifically, for 12 indistinguishable radars, the classification accuracy is improved about 15%.

Ship detection is an important issue in both civil and military fields. There are many researches about ship detection in wide sea areas and inshore areas. Most of these researches use the image dataset. In this research, a novel framework for specific ship detection using the electronic reconnaissance dataset is proposed. In the new framework, the clustering methods based on time and trajectory are used to process the dataset. Then, several machine-learning methods are used to build detection models. The long short-term memory models are used to extract time-series features and the carefully designed one-dimensional (1D) convolution neural networks (CNNs) are introduced to extract local features. Experimental results on a large electronic reconnaissance dataset collected from real scenario show the model based on 1D CNN gets better performance than classic detection models and the authors’ system achieves a good detection accuracy of 92.5%. Above all, this research is a very valuable exploration for the detection of specific ship based on electronic reconnaissance dataset.

The uniform linear array (ULA) based modulated wideband converter discrete compressed sampling (CS) receiver is a recently introduced sub-Nyquist sampling scheme for effective acquisition of the carrier frequency (CF) and direction-of-arrival (DOA) of the received radar signal. However, the ULA based system needs to correct the phase differences of the CS data in order to estimate DOA, which would cause a low DOA estimation performance. In this study, the authors propose a modified ULA based system to improve the DOA estimation performance, where another similar branch is added in each antenna to construct the proposed symmetry system. First, the received signal is mixed to basebands. Then, the mixed signals are low-pass filtered and down-sampled to get the CS data. Second, the two branches of the CS data received in one antenna can be utilised to estimate CF. The CS data received in the added branches can be used to estimate DOA without correcting the phase differences. Finally, simulations illustrate the validity of the proposed modified ULA based system and show the proposed system outperforms the ULA based system in DOA estimation when signal-to-noise ratio is more than −10 dB and the number of snapshots is more than 20.

Infinite impulse response (IIR) filter generally used in estimation, positioning, etc. has problems in that, when there is a modeling error even if the system is completely observable, the estimates may converge to incorrect values or the varying values cannot be estimated quickly. The finite impulse response (FIR) filter, which has been investigated as a method to solve this problem, has faster estimation performance than the IIR filter in the presence of modelling error, but there is a limit to the convergence characteristic of the state variables. In this paper, a non-linear FIR smoothing (NFS) filter is proposed to overcome the limitation of the state variable convergence characteristic of the FIR filter. The proposed NFS filter adds the smoothing concept to the modified receding horizon Kalman FIR filter. In order to verify the performance of the NFS filter, this filter is applied to a delay-tolerant integrated navigation system using magnetic compass (MC) of which error is difficult to be modelled accurately. If an un-modeled jump or ramp error occurs in the MC measurement, it shows that the NFS filter can estimate the measurement error more accurately than the FIR filter as well as the IIR filter through a simulation.

The electronic support (ES) receivers require wide instantaneous bandwidth as a result of a wide-frequency range of modern radar signals. Thus, analogue-to-digital converters (ADCs) with high sampling rates are required for digital ES receivers. One of the bottlenecks in designing such systems is the high power consumption of the back-end ADCs at high sampling rates. In this study, a system-level approach with the goal of minimising the required digitisation rate is presented by exploiting compressive sampling. Using the proposed receiver structure, the location finding of pulsed radars in wideband scenarios is studied. To fulfil the need for frequency and position finding, the proposed receiver employs a three-dimensional antenna array, followed by radio-frequency back-end and ADC blocks, inspired by the modulated wideband converter technique. Furthermore, an algorithm based on Bayesian compressed sensing, incorporating off-grid techniques, is employed to jointly estimate the azimuth and the elevation angles of incoming signals, as well as their carrier frequencies. Simulation results are provided to support the theoretical results obtained in this study. The results show that the proposed off-grid Bayesian method has a significantly lower mean square estimation error than the conventional deterministic approaches, while its average computation complexity can be reduced in multi-snapshot scenarios.

The performance of a global navigation satellite system (GNSS) receiver can be significantly degraded in the presence of pulsed interference and jamming. In this study, the authors leverage on tools from robust statistics to enhance the receiver performance, with jamming signals treated as outliers to the nominal, interference-free model. Particularly, the signal samples are pre-processed with a zero-memory non-linearity (ZMNL), which limits the impact of pulsed inference in a principled way. A possible approach for the design of such ZMNL is provided by the M-estimator framework when the noise at the receiver input is modelled with a heavy-tailed distribution. This approach is adopted in this study and the complex signum non-linearity is analysed. This ZMNL is obtained by considering a complex Laplacian noise. This choice is discussed and analysed in the context of GNSS receivers under jamming. The impact of the complex signum non-linearity is theoretically analysed under nominal conditions, that is, in the absence of interference. Theoretical results are supported by Monte Carlo simulations. Real GNSS signals, collected in the presence of jamming, are used to demonstrate the advantages brought by the complex signum non-linearity. Theoretical and experimental results demonstrate the benefits of the proposed approach.