In this study the authors introduce a novel method for passive source localisation that exploits phase variance. This method performs phase detection and estimation without relying on precise antenna geometry. The method uses a network of coherent receivers to estimate a continuous wave transmitter source in a two-dimensional plane with a network of statically and arbitrarily placed receivers with known positions. First the authors develop the mathematical formulation of the method. Next they establish the relationship between knowledge of the transmitter phase at arbitrarily placed receiver locations and a countable number of ambiguous transmitter locations. The authors then develop a more realistic scenario involving multi-path, noise and other signals in the receiver bandwidth and present numerical results for three different geometries. Finally, the authors compare the numerical results with those achieved with traditional phase difference of arrival.

Basis pursuit of sparse decomposition is adopted to discriminate and identify the target echo with two kinds of mainlobe jamming named time-delay repeater jamming and smeared spectrum (SMSP) jamming, which can produce multiple false targets. The main idea is to extract their pulsed differences. The signal model is first transformed to an underdetermined matrix equation, the dictionary is constructed by the function form of the target echo, and then the matrix equation is solved by replacing the l ₀ norm problem via the l ₁ norm problem. The identification of time-delay repeater jamming and the target echo is by the difference of the pulse width. Identifying SMSP jamming and the target echo is by the difference of the chirp rate. The proposed algorithm is particularly relevant to the case that the signals are overlapped. Finally, simulation results demonstrate the efficacy of the proposed method and they show that the method outperforms the conventional time–frequency representation method.

In this paper the authors apply the concept of cylindrical coordinate trajectory profiles to the accurate and efficient estimation of the launch parameter state of an intercontinental ballistic missiles from line-of sight observations that are assembled by a pair of geo-stationary satellite infrared sensors. To estimate the four-parameter launch state (geodetic latitude, longitude, direction, time) they introduce the notion of a two-coordinate projective coordinate representation of line-of-sight from observing sensor to observed target instead of the usual two angular coordinates such as local azimuth and elevation. They also introduce the notion of optimally combining launch parameter states, each state estimated from the observations of one of the pair. Finally, they investigate the performance of a nine-component inertial state (position vector, velocity vector, mass, mass rate, drag coefficient) extended Kalman filter that is primed by the optimal state estimate. The extended Kalman filter processes interleaved line-of-sight projective coordinate observations. A numerical experiment is presented with the simulated fly-out of a notional three stage North Korean intercontinental Ballistic Missile as observed by a dual viewing pair of geo-stationary satellite infrared sensors separated by approximately 90 degrees in longitude.

In large-scale distributed sensor networks, in the presence of the possible changing sensor biases, this study proposes a bias change detection-based sensor selection (SS) approach to address this multi-sensor target tracking problem. The authors only need to detect the bias change and select those reliable sensors (sensors with constant biases) for data fusion, rather than the traditional approach, which try to directly estimate the changing biases. The proposed method mainly contains the following three steps. First, the sliding window marginalised likelihood ratio test algorithm is proposed to detect the bias change. Then based on the bias change detection results, they adopt an SS strategy to select those reliable sensors. Finally, for these selected distributed sensors, the federated filter algorithm is used here for target tracking and bias registration. Simulation results show the effectiveness of these algorithms.

As the development of aerial mapping, obtaining the motion parameters of the mapping sensors mounted under the flexural wing of the aircraft is necessary to accomplish the mission. Airborne distributed position and orientation system (DPOS) could be applicable which is composed of master inertial navigation system (INS), several slave INSs and the global positioning system. The slave INS is mounted on the mapping sensor for measuring the motion parameters. However, the flexural deformation of the wing will impose critical influence on the transfer alignment between master and slave INSs. The formed flexural lever arm should be compensated precisely. A non-linear relation between the flexural angle and flexural lever arm variation is proposed, and a novel 27-state non-linear model is established for transfer alignment of airborne DPOS. The non-linear measurement equation of angular rate is derived based on three successive rotations of the body frame of the master INS. Unscented Kalman filter is utilised as the non-linear estimator for its capability of non-linear approximation. It is shown from the simulation results that the proposed method can estimate the flexural angles and flexural lever arm variations more accurately, and the attitude and position accuracy of slave INS is also improved correspondingly.

In this study, a novel algorithm called wideband sparse Bayesian learning (WSBL) is proposed for the power distribution and the direction of arrival (DOA) estimation of wideband signals. The received signals are firstly converted into the time–frequency domain by discrete Fourier transform, then the wideband DOA estimation problem can be constructed as a special multiple measurement vectors case of the sparse signal recovery problem. The WSBL algorithm, which exploits the source power distribution at different frequency bins is proposed for the sparse spectral estimation. It is shown that WSBL has good performance in low signal-to-noise ratio and small snapshot number scenario. Without the prior knowledge of source number and focusing matrices, WSBL can estimate the source power distribution and the DOAs of wideband signals simultaneously. In addition, WSBL can also avoid ambiguity even when the array does not meet the half-wavelength spacing condition. The sensor spacing can be larger than the half-wavelength at the lowest frequency of the wideband signals. Simulation results show the efficiency of the proposed method.

Radar coincidence imaging (RCI) is a recently developed concept of radar imaging, which introduces the optical coincidence imaging to traditional microwave imaging. Conventional RCI methods ignore the structure information of complex extended target, which limits its applications in high resolution imaging, thus an adaptive clustered sparse Bayesian learning algorithm is proposed in this study. To exploit the continuity of extended target, a hierarchical correlated Gaussian prior model is introduced to take into account both the sparse prior and the cluster prior, and then the algorithm alternates between steps of target reconstruction and parameter optimisation under the variational Bayesian expectation–maximisation framework. Therefore, the reconstruction of each coefficient involves its immediate neighbours, and the parameter indicating the pattern relevance among neighbouring scatterers is updated adaptively during the iterations. Experimental results demonstrate that the proposed algorithm could realise high resolution imaging effectively for extended target.

In manoeuvring target tracking, a primary tradeoff is the robust tracking of manoeuvres against the accurate tracking of constant velocity (CV) motion. To achieve this goal, an interacting multiple model (IMM) algorithm fusing input estimation (IE) and best linear unbiased estimation (BLUE) filter is presented. First, the constant input assumption of IE is modified to track possible manoeuvre fluctuation. Then, an innovation sequence modification technique is proposed so manoeuvre can be detected and estimated sequentially. With improvements above, a modified input estimator (MIE) is designed to track varying manoeuvres. In view of the optimal tracking of BLUE filter for CV motion, MIE and BLUE filter are fused within IMM framework to generate an overall adaptive tracking algorithm. Simulation results reveal the proposed approach yields better accuracy for CV tracking and robustness for manoeuvre tracking.

The authors address the estimation of the scatter matrix of a scale mixture of Gaussian stationary autoregressive (AR) vectors. This is equivalent to consider the estimation of a structured scatter matrix of a spherically invariant random vector whose structure comes from an AR modelisation. The Toeplitz structure representative of stationary models is a particular case for the class of structures they consider. For Gaussian AR processes, Burg method is often used in case of stationarity for its efficiency when few samples are available. Unfortunately, if they directly apply these methods to estimate the common scatter matrix of N vectors coming from a non-Gaussian distribution, their efficiency will strongly decrease. They propose then to adapt these methods to scale mixtures of AR vectors by changing the energy functional minimised in the Burg algorithm. Moreover, they study several approaches of robust modification of the introduced Burg algorithms, based on Fréchet medians defined for the Euclidean or the Poincaré metric, in presence of outliers or contaminating distributions. The considered structured modelisation is motivated by radar applications, the performances of their methods will then be compared with the very popular fixed point (FP) estimator and OS-CFAR detector through radar simulated scenarios.

The authors developed a technique to synchronise a bistatic radar that uses a chaotic system to generate and process either wideband amplitude modulated (AM) or frequency modulated (FM) waveforms thereby extracting high-resolution information from the targets. For chaotic AM bistatic radar, a generalised projective synchronisation approach that includes a driving oscillator at the transmitter and a response oscillator at the receiver is considered. The receiver accepts a scaled version of the transmitted waveform where the scaling factor α accounts for space propagation losses that may not be known a priori. However, AM synchronisation is highly susceptible to noise. Alternatively, FM bistatic radar is implemented with a chaos-based FM waveform. Demodulation of this waveform using a phase lock loop is required to recover the instantaneous frequency which represents a chaotic state variable. The recovered and transmitter instantaneous frequencies are synchronised by using either generalised projective synchronisation or complete replacement synchronisation. The synchronised output is then used to reconstruct the FM waveform. They show that the short-time cross-correlation of the transmitted and reconstructed waveforms is high and its self-noise is negligible. Through entropy analysis of the cross-correlogram, they found that the bistatic FM radar performs better than the bistatic AM radar.

Two-dimensional inverse synthetic aperture radar (2D ISAR) system can produce 2D electromagnetic images of a target, while failed to provide the 3D information. In this study, a novel method for 3D super resolution ISAR imaging is proposed via employing 2D U-estimation of signal parameters via rotational invariance technique (U-ESPRIT) and slice principle. First, the 3D scattered field data tensor is converted into a set of 2D matrix by stacking slices of the 3D data along one specific dimension. Then, 2D U-ESPRIT is applied to extract isolated scatterers from each slice. Combined with least-square method, complex scattering amplitudes are also estimated. Finally, 3D reconstructed scattering centres model with specific locations and complex scattering amplitudes is obtained, which can characterise the target to a great extent. The reverse process, reconstruction of the 3D ISAR image, can be readily done in real time by means of the extracted scattering centres. Experimental results are presented to demonstrate the effectiveness of the proposed algorithm.

The potential for using micro-Doppler signatures as a basis for distinguishing between aided and unaided gaits is considered in this study for the purpose of characterising normal elderly gait and assessment of patient recovery. In particular, five different classes of mobility are considered: normal unaided walking, walking with a limp, walking using a cane or tripod, walking with a walker, and using a wheelchair. This presents a challenging classification problem as the differences in micro-Doppler for these activities can be quite slight. Within this context, the performance of four different radar and sonar systems – a 40 kHz sonar, a 5.8 GHz wireless pulsed Doppler radar mote, a 10 GHz X-band continuous wave (CW) radar, and a 24 GHz CW radar – is evaluated using a broad range of features. Performance improvements using feature selection is addressed as well as the impact on performance of sensor placement and potential occlusion due to household objects. Results show that nearly 80% correct classification can be achieved with 10 s observations from the 24 GHz CW radar, whereas 86% performance can be achieved with 5 s observations of sonar.

This study presents the use of micro-Doppler signatures collected by a multistatic radar to detect and discriminate between micro-drones hovering and flying while carrying different payloads, which may be an indication of unusual or potentially hostile activities. Different features have been extracted and tested, namely features related to the radar cross-section of the micro-drones, as well as the singular value decomposition and centroid of the micro-Doppler signatures. In particular, the added benefit of using multistatic information in comparison with conventional radar is quantified. Classification performance when identifying the weight of the payload that the drone was carrying while hovering was found to be consistently above 96% using the centroid-based features and multistatic information. For the non-hovering scenarios, classification results with accuracy above 95% were also demonstrated in preliminary tests in discriminating between three different payload weights.

Clutter measurement density (CMD) is an environmental parameter required for target tracking algorithms, but it is a priori unknown and as such needs to be estimated for tracking accuracy. The authors propose an interactive CMD estimator which is based on a Gaussian mixture probability hypothesis density filter. The proposed algorithm estimates the intensity of the clutter generators which generate the clutter measurements in the surveillance space. Since the estimated intensity is evaluated from the set of the components that consists the kinematic information for the clutter generators, the proposed algorithm can be applied to the dynamic cluttered environments. Furthermore, the proposed algorithm utilises the clutter measurement probabilities calculated from the target tracker to eliminate the target originated measurements for CMD estimation. An improved multitarget tracking performance of the proposed algorithm is verified by a Monte Carlo simulation study.

Passive bistatic radars (PBRs) sensors are emerging technologies that have sparked interest due to the advantages derived from the use of opportunity illuminators instead of dedicated transmitters. An important research effort is being carried out, mainly focused on the analysis of the illuminators waveforms and the development of system demonstrators, being the detection and tracking of low flying aircrafts the most extended validation scenario. IDEPAR is a digital video broadcasting-terrestrial (DVB-T)-based multichannel PBR technological demonstrator, developed in the University of Alcalá. The main contributions of this study are the radar scenario, detection of terrestrial vehicles in semi-urban environments, and the development of a general and complete design methodology, that will allow the definition of system design requirements for any system and application. To validate system performance, real data were processed and detection and tracking results were evaluated using global positioning system data from cooperative targets. Reference signal processing algorithms were used. Results showed the feasibility of DVB-T-based PBRs for detecting and tracking low speed terrestrial vehicles in semi-urban scenarios.

In this study, the authors derive the ambiguity function (AF) of a narrowband and a wideband hyperbolic chirp. They calculate the second derivatives of the squared amplitude of the narrowband complex AF and use them to calculate the Fisher information matrix (FIM) of the estimators of the target range and velocity. The FIM is then used to calculate the Cramér–Rao lower bounds (CRLBs) of the variance of the estimators and to carry out an analysis of estimation performance and a comparison with the case of a linear chirp with a rectangular and a Gaussian amplitude modulation. The analysis and the calculations of the CRLB are also extended to a train of hyperbolic chirps. Results corroborate that at narrowband the hyperbolic chirp is less Doppler tolerant than the linear chirp and show that the hyperbolic chirp provides a comparable measurement accuracy to the linear chirp. Results at wideband corroborate the superior Doppler tolerance of the hyperbolic chirp with respect to that of the linear chirp.

Closed form expressions are derived for the calculation of the clairvoyant covariance matrix for radar sea clutter arising from individual clutter components with Gaussian spectra. Two forms are presented corresponding to a power series expansion of a sinc² antenna gain pattern and a Gaussian approximation to the mainlobe of the antenna pattern. The variation of covariance matrix eigenvalues due to factors such as coherent processing interval, spectra bandwidth, centre frequency and relative power is examined.

This study proposes a maximum likelihood (ML) estimator for parameter estimation for a complex noise amplitude modulation (NAM) signal. The NAM signal is a general expansion of the exponential signal in which the noise is not circularly symmetric. In the proposed estimator, a non-parametric modified interpolation on Fourier coefficients method is first applied to produce a coarse estimation. Then, a gradient descent method is applied to refine the parameters. The modulation noise to complex additive white Gaussian noise ratio, which is an implicit unknown parameter, is calculated and refreshed at each iteration. For signal-to-noise ratios above the threshold, the variances of the coarse estimation are derived and shown to be independent of the modulation index. The probabilities of a divergence in different gradient descent methods are derived; the pure Newton's method is adopted based on the consideration of both computational complexity and practical applications. Monte Carlo simulations verify the variances and the threshold effect of the coarse estimation; the variances of the ML estimator are demonstrated to asymptotically approach the Cramér–Rao bounds.

Synthetic aperture radar imaging at high resolution and highly squint angle is confronted with several difficulties, such as the spatial variance of range cell migration (RCM) and the azimuth-dependent property of high-order terms. In order to address the above-mentioned problems, an improved non-linear chirp scaling algorithm based on keystone transform (KT) and linear range walk correction (LRWC) is proposed. First, KT is applied to correct the second-order RCM in range frequency and azimuth time domain. Then the spatial variance of linear RCM (LRCM) is analysed, and range–azimuth coupling is alleviated through LRWC, in this way, azimuth-variant RCM can be removed by phase multiplication, which avoids the approximate error of RCMC in two-dimensional frequency domain. Taking both the spatial variance of LRCM and the difference of the minimum bistatic slant range caused by LRWC into consideration, high-precision approximation of azimuth-dependent coefficients for azimuth signal are deduced, based on which the Doppler centroid, azimuth FM rate and cubic phase can be equalised by selecting proper perturbation coefficients. The proposed algorithm leads to a large depth of focusing in both range direction and azimuth direction. Simulation results prove the effectiveness of the proposed method.

Traditional clutter subspace estimation algorithms try to solve the problem by estimating the clutter subspace eigenvectors from training samples, which are inevitably contaminated by noise. Actually, this can never be the best estimate, especially when the size of the training set is small. Furthermore, the omnipresent noise precludes the seeking of a pure clutter subspace. To cope with above drawbacks, the authors appeal to the sparse representation/recovery technique. From a mathematical viewpoint, the pure clutter subspace is spanned by the space-time steering vectors corresponding to the clutter component, and it can be constructed by a suitable set of space-time steering vectors selected from an overcomplete space-time steering dictionary. A criterion for selecting these space-time steering vectors is devised. Moreover, a clutter nulling type space-time adaptive processing algorithm is derived based on the proposed criterion. The resulting algorithm only needs the knowledge of the noise power rather than the clutter rank, which is quite troublesome to estimate in practice. Numerical results with both simulated and the Mountain-Top data demonstrate that the proposed algorithm has superior clutter suppression performance even with limited training samples.

The multi-dimension jamming resources allocation (JRA) problem is studied to enhance the jamming effectiveness for the cooperative jammers formation against radar net. The allocation strategies include the methods of allocating jamming resources and the mode selection for the jamming signal. First, jamming resource optimum allocation model is established based on the detection probability of the netted radar fusion centre. Second, a hybrid quantum-behaved particle swarm optimisation and self-adjustable genetic algorithm (HQPSOGA) is proposed to optimise the deployment of the jamming resource innovatively with multi-constrained conditions. Finally, the HQPSOGA is compared with the integer-value genetic algorithm, standard particle swarm optimisation (PSO) and quantum-behaved PSO in JRA problem regarding the solution quality, robustness, convergence rate and reliability by a general Monte Carlo simulation. Simulation results show that the proposed method is capable of developing better overall interference capacity efficiently for the jammers formation than any other tested algorithm.

Human target detection and tracking have great potential in surveillance, rescue and security applications. Traditional human detection and tracking are performed on the range profile. If targets are close or overlapped in range, it is difficult to distinguish these targets. As velocity provides another aspect to distinguish targets, a novel framework is proposed for human tracking using both range and velocity information. The tracking framework consists of six steps, including clutter reduction, range–Doppler (RD) calculation, target detection, measurement estimation, target localisation and target tracking. Primary attention is devoted to the middle four steps. The calculation process of the RD image is described in detail. The ordered statistics constant false alarm rate detector is extended to a two-dimensional scenario for the RD target detection. An efficient approach is given for automatic measurement estimation. A minimum root-mean-square error pruning algorithm is proposed for multi-target localisation. As the algorithm combines both range and velocity information for measurement association, it clearly shows a lower wrong association probability than the method using range information only. The effectiveness of the proposed tracking framework is evaluated by the experimental data in the foliage-penetration environment.

Aperture-dependent motion compensation (MOCO) is crucial for high-resolution airborne synthetic aperture radar (SAR) imaging, which can suppress image degradation especially for SAR systems working at a high-frequency waveband, such as millimetre-wave (MMW) SAR. The current precise topography and aperture (PTA) dependent MOCO algorithm aims to compensate the azimuth-dependent motion error by using a modified azimuth matched filtering. However, the matched filter is deduced with the principle of stationary phase without considering the effects from the azimuth-dependent motion errors, which limits its precision in focusing MMW SAR data involving severe motion errors. This study proposes a refined PTA algorithm with a precise post matched filter established by the method of series reversion. The range-variant characteristic of residual motion errors is also investigated for an improved precision. Simulated and real MMW SAR data sets are used to validate the effectiveness of the proposal.

Unmanned aerial vehicle (UAV) systems are experiencing a period of rapid growth as potential for industrial and commercial applications are arising. Conventional UAV technologies focus on outdoor large area navigation, utilising global positioning system, which has proven to be less effective in enclosed environments. The authors aim to develop an indoor navigation system, specifically for industrial applications which require custom sensing technologies to aid in pilot navigation. A custom sensing array, featuring ultrasonic transceivers, was developed to localise drone position in an enclosed known environment and provide pilot feedback. Six subjects were recruited to pilot the drone with and without the navigation system in an enclosed room to a pre-set target at a known location in two cases: (i) with a line of sight, and (ii) without line of sight. Flight duration, number of collisions, and distance from target were recorded and used to quantify performance. Using the navigation system, subjects were able to reduce their flight duration on average by 19.7% during an obstructed line of sight, illustrating the increased ability and confidence in piloting the drone using the navigation system. This study serves to prove the potential of this device as an essential tool for indoor drone localisation and commercial inspections.