Showing papers in "Iet Radar Sonar and Navigation in 2020"

Intra-pulse modulation radar signal recognition based on CLDN network

Abstract: Automatic modulation classification of radar signals, which plays a significant role in both civilian and military applications, is researched in this study through a deep learning network. In this study, a novel network combined a shallow convolution neural network (CNN), long short-term memory (LSTM) network and deep neural network (DNN) is proposed to recognise six types of radar signals with different signal-to-noise ratio (SNR) levels from -14 to 20 dB. First, raw signal sequences in the time domain, frequency domain and autocorrelation domain are as input for a shallow CNN. Then the features extracted by CNN will be the input of LSTM network. Finally, DNNs will output the signal modulation types directly. The simulation results demonstrate that the accuracies in autocorrelation domain are all more than 90% at -6 dB and close to 100% when SNR > -2 dB. The recognition performances of the three domains are compared. Compared with other recognition methods, the proposed method has higher average accuracy and better performance under low SNR condition. The measured results show that the proposed method has achieved high accuracies of common four kinds of measured radar signals.

Parameter estimation of underwater impulsive noise with the Class B model

Abstract: The statistical characteristic of ocean ambient noise plays an important role in developing underwater signal processors. Considering that the noise in shallow water shows the impulsive nature, non-Gaussian noise models are usually applied to model the ocean ambient noise. In this study, the ocean ambient noise is modelled by using the Middleton Class B model, which can be decomposed into Gaussian and non-Gaussian models. Then, the parameters of Class B model are estimated based on the least-square estimation method, which can be deduced by using the characteristic function of the Middleton Class B model. The processing results of simulated data and real data indicate that the presented method well estimates the parameters of Class B modelled noise. Besides, it further shows that the Middleton Class B model is suitable for modelling the impulsive noise in shallow water.

Classification of drones and birds using convolutional neural networks applied to radar micro-Doppler spectrogram images

Abstract: This study presents a convolutional neural network-based drone classification method. The primary criterion for a high-fidelity neural network-based classification is a real dataset of large size and diversity for training. The first goal of the study was to create a large database of micro-Doppler spectrogram images of in-flight drones and birds. Two separate datasets with the same images have been created, one with RGB images and others with greyscale images. The RGB dataset was used for GoogLeNet architecture-based training. The greyscale dataset was used for training with a series of architecture developed during this study. Each dataset was further divided into two categories, one with four classes (drone, bird, clutter and noise) and the other with two classes (drone and non-drone). During training, 20% of the dataset has been used as a validation set. After the completion of training, the models were tested with previously unseen and unlabelled sets of data. The validation and testing accuracy for the developed series network have been found to be 99.6 and 94.4%, respectively, for four classes and 99.3 and 98.3%, respectively, for two classes. The GoogLenet based model showed both validation and testing accuracies to be around 99% for all the cases.

Quantum two-mode squeezing radar and noise radar: covariance matrices for signal processing

Abstract: Recently, the authors have built and evaluated a prototype quantum radar in the laboratory which operates at microwave frequencies. This radar, which they call a quantum two-mode squeezing radar (QTMS radar), generates a pair of entangled microwave signals and transmits one of them through free space, using the other signal as a reference to perform matched filtering. The specific type of entanglement is called a two-mode squeezed vacuum, a type of continuous-variable entanglement between two frequencies. Motivated by the success of these experiments, they try to better understand the entangled QTMS radar signals in this study. They do so by comparing it to a simpler, more conventional radar system, which they call a two-mode noise radar (TMN radar). They also show how both types of radars are related to standard noise radars as described in the literature. They find that the signals for QTMS radar signals and TMN radar signals have the same mathematical form and that they are related to noise radar by a simple mathematical transformation. This shows that QTMS radar signals can be emulated by a fictional, idealised TMN radar and that it is possible to apply results from the noise radar literature to QTMS radar.

Compact CRB for delay, Doppler, and phase estimation – application to GNSS SPP and RTK performance characterisation

Abstract: The derivation of tight estimation lower bounds is a key tool to design and assess the performance of new estimators. In this contribution, first, the authors derive a new compact Cramer–Rao bound (CRB) for the conditional signal model, where the deterministic parameter's vector includes a real positive amplitude and the signal phase. Then, the resulting CRB is particularised to the delay, Doppler, phase, and amplitude estimation for band-limited narrowband signals, which are found in a plethora of applications, making such CRB a key tool of broad interest. This new CRB expression is particularly easy to evaluate because it only depends on the signal samples, then being straightforward to evaluate independently of the particular baseband signal considered. They exploit this CRB to properly characterise the achievable performance of satellite-based navigation systems and the so-called real-time kinematics (RTK) solution. To the best of the authors’ knowledge, this is the first time these techniques are theoretically characterised from the baseband delay/phase estimation processing to position computation, in terms of the CRB and maximum-likelihood estimation.

Doppler effect and micro-Doppler effect of vortex-electromagnetic-wave-based radar

Abstract: The vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM) has attracted much attention in radar applications. Aimed at the moving target detection application of vortex EM waves, the Doppler effect and the micro-Doppler (m-D) effect, both including the linear Doppler shift and the angular Doppler shift, are investigated. The theoretical analyses demonstrate that the Doppler effect and the m-D effect in vortex-EM-wave-based radar are significantly more complicated than those in traditional non-vortex-EM-wave-based radar, and can provide additional features for target identification and recognition. Simulations are given to validate the proposed theoretical analyses.

Work modes recognition and boundary identification of MFR pulse sequences with a hierarchical seq2seq LSTM

Abstract: Recognition of multi-function radar (MFR) work mode in an input pulse sequence is a fundamental task to interpret the functions and behaviour of an MFR. There are three major challenges that must be addressed: (i) The received radar pulses stream may contain an unknown number of multiple work mode class segments. (ii) The intra-mode and inter-mode knowledge of a modern MFR may be too flexible and complicated to be represented and learned through traditional hand-crafted features and learning models. (iii) The variable duration of each enclosed work mode makes the identification of the transition boundaries of adjacent modes difficult. To address these challenges and implement automatic recognition of MFR work mode sequences at a pulse-level, this study develops a novel processing framework based on a time series representation of MFR work mode sequence and sequence-to-sequence (seq2seq) long short-term memory network. The proposed method can not only automatically recognise multiple complexes modulated work mode classes in a pulse sequence. Still, it can also accurately identify the transition boundaries between each class by labelling the class information for each pulse. The experimental results showed the extended capabilities and improved performance of the proposed method over the state-of-the-art work mode classification methods.

Radar cross-sections of pedestrians at automotive radar frequencies using ray tracing and point scatterer modelling

Abstract: Simulation of radar cross-sections of pedestrians at automotive radar frequencies forms a key tool for software verification test beds for advanced driver assistance systems. Two commonly used simulation methods are the computationally simple scattering centre model of dynamic humans and the shooting and bouncing ray technique based on geometric optics. The latter technique is more accurate but computationally complex. Hence, it is usually used only for modelling scattered returns of still human poses. In this work, the authors combine the two methods in a linear regression framework to accurately estimate the scattering coefficients or reflectivities of point scatterers in a realistic automotive radar signal model which they subsequently use to simulate range-time, Doppler-time and range-Doppler radar signatures. The simulated signatures show a normalised mean square error 81% with respect to measurement results generated with an automotive radar at 77 GHz.

DopplerNet: a convolutional neural network for recognising targets in real scenarios using a persistent range–Doppler radar

Abstract: In the past few years, the commercial use of drones has exploded, since they are a safe and cost-effective solution for many kinds of problems. However, this fact also opens the door for malicious use. This work presents a novel system able to detect and recognise drones from other targets, allowing the police and security agencies to deal with this new aerial thread. The proposed system only uses a persistent range-Doppler radar, avoiding the restrictions of the optical sensors, usually required for the recognition part. The processing is based on constant false alarm rate detection stage, followed by a convolutional neural network that performs the recognition. This network takes as input raw range-Doppler radar data and predicts their class (car, person, or drone). For this purpose, an extensive controlled trial test campaign has been performed, resulting in a novel dataset with more than 17,000 samples of drones, cars, and people, acquired in real outdoor scenarios. As far as authors' knowledge, this is the first range-Doppler radar database for the recognition of drones and other targets. The high-accuracy results (99.48%) suggest that this system could be successfully used in security and defence applications to discriminate between drones and other entities.

Human identification based on natural gait micro-Doppler signatures using deep transfer learning

Abstract: Gait-based human identification aims to identify individuals by their walking style. In this study, the authors investigate the use of micro-Doppler (m-D) signatures retrieved from a frequency-modulated continuous-wave radar sensor to identify individuals based on their natural gait characteristics. The gait dataset of 20 persons has been collected in an indoor environment where each subject was allowed to walk naturally and freely, which is absolutely more realistic and challenging than most existing works based on limited walking behaviour. Then, they perform identification using a transfer learned ResNet-50, which was fine-tuned on the gait m-D dataset based on the deep transfer learning technique. Through experiments, they first determined the optimal observation window length of m-D samples, and with this input, they achieved an average identification accuracy of 96.7% on the test set for 20 subjects, which highly outperforms the state-of-the-art methods. The presented work provides prospects in developing a solution to automatically identify persons based on gait characteristics using a simple and cost-efficient radar device.

300 GHz radar object recognition based on deep neural networks and transfer learning

Abstract: For high-resolution scene mapping and object recognition, optical technologies such as cameras and LiDAR are the sensors of choice. However, for future vehicle autonomy and driver assistance in adverse weather conditions, improvements in automotive radar technology and the development of algorithms and machine learning for robust mapping and recognition are essential. In this study, the authors describe a methodology based on deep neural networks to recognise objects in 300 GHz radar images using the returned power data only, investigating robustness to changes in range, orientation and different receivers in a laboratory environment. As the training data is limited, they have also investigated the effects of transfer learning. As a necessary first step before road trials, they have also considered detection and classification in multiple object scenes.

Prediction-discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks

Abstract: In this paper, a novel prediction-discrepancy based on innovative particle filter (PDIPF) is proposed to solve the unmanned aerial vehicle (UAV) positioning problem in the presence of the global positioning system (GPS) spoofing attack, supposing that the GPS spoofing effects are in the form of unknown but bounded errors. To cope with the GPS spoofing attacks as unknown sudden changes of system state variables, the compensation of the GPS spoofing effects is adaptively done in two basic parts of PDIPF algorithm including particle weighting and covariance matrix adaption. In addition, a theorem is developed which verifies that the output estimation error is upper bounded by a given probability with the help of the adapted covariance matrix. Besides, the particle weight calculation in PDIPF is done with respect to the prediction discrepancy of generated particles from the GPS measurements. The proposed PDIPF is used to decrease the effects of any GPS spoofing errors with different probability density functions and estimate true position of UAV in the presence of the GPS spoofing attacks. The algorithm is applied to the inertial navigation system/GPS/Loran-C integration systems. Simulation results demonstrate the effectiveness of the proposed PDIPF algorithm in terms of accuracy and redundancy.

Fast ship detection combining visual saliency and a cascade CNN in SAR images

Abstract: In order to realise the fast detection of ships in synthetic aperture radar (SAR) images, a detection method combining visual saliency and a cascade convolutional neural network (CNN) is proposed. First, based on visual saliency, a multiscale spectral residual model is designed for realising the fast detection of ship candidate regions. Then, a cascaded CNN is designed, which consists of two convolution networks, namely, the front-end shallow CNN, which is used to quickly exclude obvious non-ship candidates and classify the ship candidates according to the ship orientation, and the back-end deep CNN, which is used to detect high-probability candidate regions with rotatable boundary boxes. The whole structure can realise the fast detection and precise positioning of ships with an arbitrary orientation. Finally, the authors conduct detailed experiments on the SAR ship image dataset. The experimental results show that the proposed method can effectively improve the detection accuracy of ships, ensuring the detection efficiency in SAR images.

DVB-T based passive radar for simultaneous counter-drone operations and civil air traffic surveillance

Abstract: The effectiveness of DVB-T based passive radar (PR) in counter drones operations is investigated in this study aiming at monitoring airport terminal areas. In particular, the authors demonstrate that such sensors could be effectively employed to provide simultaneous short-range surveillance against drones and long-range monitoring of aircraft from civil air traffic. To this purpose, several experimental tests have been performed with the DVB-T based AULOS® passive sensor developed by Leonardo S.p.A. using very small RCS drones as cooperative targets along with conventional air traffic as targets of opportunity. An appropriate signal processing architecture is proposed for the two search tasks to be accomplished simultaneously. This is extensively applied against the collected datasets, based on the algorithmic solutions devised by the research group at Sapienza University. The reported results clearly prove the capability of a DVB-T based PR of simultaneously detecting and localising drones flying around the airport area as well as the typical civil aircraft at longer distances.

Bayesian information fusion and multitarget tracking for maritime situational awareness

Abstract: The goal of maritime situational awareness (MSA) is to provide a seamless wide-area operational picture of ship traffic in coastal areas and the oceans in real time. Radar is a central sensing modality for MSA. In particular, oceanographic high-frequency surface-wave (HFSW) radars are attractive for surveying large sea areas at over-the-horizon distances, due to their low environmental footprint and low power requirements. However, their design is not optimal for the challenging conditions prevalent in MSA applications, thus calling for the development of dedicated information fusion and multisensor-multitarget tracking algorithms. In this study, the authors show how the multisensor-multitarget tracking problem can be formulated in a Bayesian framework and efficiently solved by running the loopy sum-product algorithm on a suitably devised factor graph. Compared to previously proposed methods, this approach is advantageous in terms of estimation accuracy, computational complexity, implementation flexibility, and scalability. Moreover, its performance can be further enhanced by estimating unknown model parameters in an online fashion and by fusing automatic identification system (AIS) data and context-based information. The effectiveness of the proposed Bayesian multisensor-multitarget tracking and information fusion algorithms is demonstrated through experimental results based on simulated data as well as real HFSW radar data and real AIS data.

FDA-MIMO for joint angle and range estimation: unfolded coprime framework and parameter estimation algorithm

Abstract: The frequency diverse array multiple-input multiple-output (FDA-MIMO) radar provides range estimation capability by exploiting a small frequency offset across the transmit sensors, which has been utilised in numerous applications. However, the estimation performance is basically limited by the array geometry and signal bandwidth. In this study, the authors propose a new FDA-MIMO framework, i.e. the unfolded coprime array with `unfolded' coprime frequency offsets (UCA-UCFO) framework, for joint angle and range estimation without ambiguity. The array aperture and signal bandwidth are obviously expanded by employing UCA in the spatial domain and frequency domain, which results in significantly enhanced estimation accuracy and resolution. In addition, we construct the joint angle and range estimation problem as a two-dimensional (2D)-multiple signal classification spatial spectrum and transform 2D total spectrum search into a 1D local spectrum search by introducing a successive iteration (SUIT) algorithm. The SUIT algorithm can significantly relieve the computational burden but without performance degradation. The Cramer-Rao bounds of angle and range are provided as a performance benchmark. The analysis and simulations have validated the superiority and advantages of the UCA-UCFO framework and SUIT algorithm with respect to location accuracy, resolution, and computational complexity.

Implementation of IoT analytics ionospheric forecasting system based on machine learning and ThingSpeak

Abstract: Nowadays, using the Internet of Things (IoT), several real-time forecasting systems have been developed. The primary challenge of this system is to utilise an appropriate prediction model that can predict various space weather parameters as accurately as possible. In this study, an ionospheric IoT analytical system with variational mode decomposition (VMD) based on kernel extreme learning machine (KELM) is proposed. The ionospheric signal delay/total electron content (TEC) data from Continuous Reference Stations (CORS) Port Blair (2.03°N, 165.25°E, geomagnetic), Bengaluru (4.40°N, 150.77°E, geomagnetic), Koneru Lakshmaiah Education Foundation (KLEF) – Guntur (7.50°N, 153.76°E; geomagnetic) and Lucknow (17.98°N, 155.22°E; geomagnetic) are used for the analysis during the period of 2015. The ionospheric signal delays of four CORS are computed from ThingSpeak (IoT) with the channel ID and the Application Programming Interface key. ThingSpeak data is given to the ionospheric forecasting model (VMD-KELM). The results predicted from the proposed model are able to achieve the faster training process and obtain a similar accuracy to that of the VMD-artificial neural network. The proposed VMD-KELM application is adopted when a cloud-based forecasting system requires fast learning speed and good accuracy. As a result, the cloud paradigm offers the possibility without web development skills or highly specific statistics.

Sparse long-time coherent integration-based detection method for radar low-observable manoeuvring target

Abstract: Low-observable manoeuvring target detection is a challenging problem for radar signal processing, due to the complex environment, target manoeuvrability, limited observation time etc. The study proposed a novel long-time coherent integration (LTCI) method for radar manoeuvring target, especially for low-observable unmanned aerial vehicle targets. The fast non-parametric searching LTCI via a non-uniform (NU) resampling and scale processing (SP) technique is proposed, where the high-order signal phase is reduced to linear term using NU resampling. After that, the SP method is applied to realise the range migration compensation. The sparse Fourier transform (FT) and inverse FT are performed for the final integration. The manoeuvring target would appear as peaks in the non-uniformly resampling and SP-sparse LTCI domain. Experiments using two sets of real radar data indicate that the proposed method can achieve a good balance between computational cost and integration gain. In addition, detailed detection performances are compared with the traditional methods, e.g., moving target detection, fractional FT, fractional ambiguity function, Radon-based LTCIs, and sparse fractional FT.

Main lobe jamming suppression via blind source separation sparse signal recovery with subarray configuration

Abstract: The performance of the modern radar systems will degrade significantly in the presence of main lobe jamming. In this study, a new methodology is introduced to suppress multiple main lobe jammings while accomplishing the directions of arrival (DOAs) estimation of the multiple targets. Specifically, the signal model of a specific subarray configuration accounting for jamming and target signals is developed. Then, blind source separation method is utilised to separate the target and jamming signals in each subarray. Furthermore, the separated target signals of all subarrays are jointly imposed to estimate DOAs of targets through resorting to sparse signal recovery approach. Finally, numerical simulations are provided to assess the performance of the proposed algorithm exhibiting the favourable capabilities in terms of the strong main lobe jamming suppression and the DOAs estimation of multiple targets.

Bernoulli track-before-detect filter for maritime radar

Abstract: In this work, the authors study the problem of detecting and tracking small targets using high-resolution maritime radar, where sea clutter is correlated in range, and its amplitude fluctuations are characterised by occasional spikes. To tackle this problem, they develop a Bernoulli track-before-detect filter, as the optimal recursive Bayesian detector/estimator of target state and its presence in noise. A realistic clutter model, in the form of the K -distribution with unknown distribution parameters, is adopted. Target amplitude fluctuations are also included in the model. The detection and tracking improvement are demonstrated by simulations and compared against a conventional point target tracking algorithm.

Multi-sensor fusion SLAM approach for the mobile robot with a bio-inspired polarised skylight sensor

Abstract: A multi-sensor fusion approach for simultaneous localisation and mapping (SLAM) based on a bio-inspired polarised skylight sensor is presented in this study. The innovation of the proposed approach is that a newly designed bio-inspired polarised skylight sensor, which is inspired by the navigation principle of desert ant, is introduced to improve the accuracy of SLAM. The measurement equations based on a polarised skylight sensor and a lidar are derived to obtain the orientation and position of the mobile robot and landmarks. Three kinds of non-linear filters, extended Kalman filter (EKF), unscented Kalman filter, and particle filter, are adopted and compared to fuse the polarised skylight sensor, lidar, and odometry to estimate the position, orientation, and map in the experiments. Simulation tests and experiments are conducted to validate the effectiveness of the proposed method. The simulations show that the EKF-SLAM with the polarised skylight sensor reduces the error of localisation about 30% and the error of mapping about 25%. Experiments indicate that the proposed EKF-SLAM approach can reduce the error of position and the heading angle, which verifies the proposed EKF-SLAM method, can be used for the outdoor with the low-cost multi-sensor.

Radar signals classification using energy‐time‐frequency distribution features

Abstract: In this research, the authors extract features from intermediate frequency band radar signals in the time-frequency domain for classification. The extracted features are classified via support vector machine and K-nearest neighbour classifiers. They show the accuracy of classification is above 99% for different classes of radar signals except for frequency shift keying signal with accuracy 83% in negative signal-to-noise ratio (SNR). To identify the radars with the same class, the classification accuracy is 91% for SNR between 5 to 15 dB and 64% in the worst case for SNR between -1 to 10 dB. The proposed method is compared with some methods based on the empirical mode decomposition (EMD), cumulant and Zhao Atlas Mark Distribution (ZAMD). The results show that the classification error in the proposed method is less than that of EMD method 55% in the best case and 9% in the worst case. The performance of the cumulant-based method is weaker than that of the proposed method in common designed scenarios becoming almost similar only in one scenario. The ZAMD-based method could only distinguish the signals with different modulations in high SNR while it is unable to classify the signals with the same modulation but different parameters.

Methodology of classification and recognition of the radar emission sources based on Bayesian programming

Abstract: This study considers the Bayesian programming methodology for recognition and classification of radio emission sources. A mathematical model of Bayesian programming proposes forming a family of probability distributions based on known parameters contained in a training sample (database). The correlations between the object classes have been estimated according discussed methodology. The received assessment has been used to separate procedures of recognition and classification of radar emission sources. The simulation of methodology has carried out for four parameters of radar signals (frequency range, pulse width, pulse repetition interval and radar rotation frequency) by used database with 346 classes and 16 types of radar. Based on the existing database of radar emission sources, it is possible to predict the probability of class recognition for the general population of objects, if its distribution is known. This study demonstrates the consistency of the Bayesian programming methodology for object identification in ELINT systems.

Target detection using features for sonar images

Abstract: Robust object detection in sonar images is an important task for underwater exploration, navigation and mapping. Current methods make assumptions about the shape, highlight or shadow of an object, which may be invalid for some environments or targets. We focus on the area of feature extraction-based detection, which does not rely on information about the shape of the target, towards a robust framework for target detection for a variety of seabed structures and target types. The proposed framework first estimates the seabed type from the spatial distribution of features to determine the set of optimal parameters, and then obtains a set of features which are filtered according to intensity and distribution to yield a detection decision. The proposed method also provides a means to determine the seabed type, and a machine-learning based methodology to choose the feature detectors' parameters to match the evaluated seabed type. We report the performance of a variety of feature detectors for a simulated environment and of one feature detector for real sonar images. Results show the importance of choosing the parameters of the feature extractors based on the current environmental conditions and the proposed method obtains a favourable tradeoff between detection and false alarm rates.

Evaluation of GBAS flight trials based on BDS and GPS

Abstract: Ground-based augmentation systems (GBASs) are widely used augmentation systems using satellite navigation. GBAS can improve the performance of BeiDou navigation satellite system (BDS) in civil aviation application. Evaluation is necessary before using a GBAS. On the basis of the flight trials conducted at the Dongying airport, evaluation results show that based on both the global positioning system (GPS) L1 signal and BDS B1I signal, ground accuracy designators (GADs) of four reference receivers are consistent with GAD-C levels. The improvement in accuracy obtained with GBAS differential process is ∼60%, and the differential position error based on the BDS B1I signal is 40% greater than that of the GPS L1 signal, because currently incomplete BDS satellites distribution is worse than that of GPS. Airborne protection levels assessments show that the approach trials based on the BDS B1I signal exhibit several false alarm events. Further analysis indicates that BDS vertical protection levels were larger because of larger pseudoinverse S result from low elevation angle and unsatisfactory geometric distribution of BDS satellites. Besides, monitors show that BDS B1I signal is more affected by ionospheric delay than GPS L1 signal, which can also contribute to false alarm events.

Frequency-modulated diverse array transmit beamforming with bat metaheuristic optimisation

Abstract: Frequency diverse array (FDA) as a new array technology with range-, angle- and time-dependent beamforming has received much attention, especially in the application of radar systems. In FDA, the element indices employed frequency increments to produce angle–range beamforming. This study proposes bat metaheuristic algorithm-based synthesis technique to uncouple the FDA angle–range beamforming. Specifically, the authors optimise the frequency increment, amplitude weighting and phase weighting with bat metaheuristic algorithm to obtain single-dot and multi-dot shape transmit beamforming. In addition, they take into account the real scenario, i.e. the propagation time of the radiated signal arriving at the target position. It is demonstrated that the proposed bat algorithm FDA achieves more focused beamforming (i.e. narrower mainlobe) and better sidelobes levels (peak to sidelobe ratio) compared to other frequency increment techniques in the literature.

CFAR detection algorithm for objects in sonar images

Abstract: The authors introduce a constant false alarm rate (CFAR) detection algorithm, called K-CFAR, for automatic detection of underwater objects in sonar imagery. The K-CFAR adopts the K-distribution as a statistical model of the background. An efficient closed-form estimator for the K-distribution parameters is derived by the second-order approximation of the Polygamma function without involving a numerical iterative solution. A closed-form expression for the CFAR detection threshold is obtained by exploiting the first-order Laguerre approximation of the K-distribution. Then, to increase the probability of detection, a non-CFAR refinement to the K-CFAR, based on the spatial feature of the objects, is proposed. Experimental results obtained from 270 real sonar images of diverse environments demonstrate the superiority of the proposed algorithm compared to the state-of-the-art in terms of receiver operating characteristic curves.

Joint power and time width allocation in collocated MIMO radar for multi-target tracking

Abstract: Collocated multiple-input multiple-output radar can track multiple targets simultaneously by transmitting multiple orthogonal beams and adopting the digital beamforming technology. In this scenario, the authors propose a joint power and time width allocation approach, which combines a cognitive tracking model based on the posterior Cramer-Rao lower bound (PCRLB) and the square-root cubature Kalman filter. The aim of the optimisation model is to improve the velocity estimation accuracy by minimising the sum of the PCRLBs of the velocity of multiple targets, which are predicted based on the feedback information from the cognitive tracking model. However, there are two finite working resources in the optimisation model: the total transmit power of multiple beams and the total effective time width of each corresponding signal. The resource allocation problem can be transformed into a non-convex optimisation problem, which can be converted into a standard convex optimisation problem by the linear relationship between the optimal power and the optimal time width. In this way, the joint power and time width allocation scheme is established as an adaptive closed-loop system. Numerical results demonstrate that the velocity tracking accuracy can be improved efficiently by the proposed algorithm.

Xiaomi Mi 8 smartphone GNSS data quality analysis and single-frequency RTK positioning performance evaluation

Abstract: In this study, the authors mainly explore the relationship between the data quality of smartphones equipped with global navigation satellite system (GNSS) modules and the accuracy and reliability of single-frequency real-time kinematic (RTK) positioning. Specifically, the visible satellite number, code minus carrier and the signal-to-noise ratio of the GNSS raw observations were evaluated through static experiments and dynamic experiments, and the performance difference between single-frequency global positioning system (GPS) RTK and single-frequency BeiDou Navigation Satellite System (BDS) RTK positioning was compared and analysed. In the static experiment, the average values of visible satellites of BDS and GPS in Xiaomi Mi 8 smartphone are 7.4 and 8.4, respectively. The average code minus carrier error of GPS is 5.234 m, the BDS is 5.518 m, and the average signal-to-noise ratio of GPS is ~29.092 dB Hz and the BDS is ~28.718 dB Hz. The data quality of the dynamic experiment is similar to the static experimental result. The positioning accuracy obtained by the static experiment from the single-frequency GPS RTK and single-frequency BDS RTK in the east/north/up (E/N/U) directions is 0.146/0.555/0.464 m and 0.843/0.287/0.317 m, respectively. The instability of BDS-locked satellite in dynamic experiment results the positioning accuracy of BDS RTK is lower than that of GPS.

Radar-based human identification using deep neural network for long-term stability

Abstract: Human identification plays a vital role in daily lives. A majority of biometric technologies require the active cooperation of humans, while gait recognition does not. Compared with other identification technologies, radar-based technology can monitor the human body around the clock without being affected by light/weather, and is not easy to be forged while protecting privacy. Previous researches have revealed that gait signatures acquired using radar can be used for human identification, but there is almost no literature on the long-term stability of gait signatures. Due to the long-term interval observation, the human micro-Doppler will change according to the subject (such as slight differences in walking posture). In this study, a novel network is proposed to realise stable identification of humans by extracting long-term stable features. The micro-Doppler data is processed by a short-time Fourier transform and finally classified by the proposed neural network. Data acquisition was carried out within more than a month. The experimental results demonstrate that the recognition accuracy of the validation set can reach about 99%, and the recognition accuracy of the test set can reach 90% (improved 3% compared with the network without a recurrent neural network), showing the potential of the proposed method in long-term stable identification.