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Satellite image processing has been widely used in recent years in a number of applications such as land classification, Identification transfer, resource exploration, super‐resolution image, etc. Due to the orbital location, revision time, quick view angle limitations, and weather impact, the satellite images are challenging to manage. There are many types of resolution, such as spatial, spectral, and temporal. Still, in our case, we concentrated on spatial image resolution to super resolve the images from low‐resolution images. For remote sensing image super‐resolution fast wavelet‐based super‐resolution (FWSR), we propose a novel, fast wavelet‐based plexus framework that performs super‐resolution convolutional neural network (SRCNN)‐like extraction of features based on three hidden layers. First, wavelet sub‐band images are combined into a pre‐defined full‐scale data training factor, including approximation and interchangeable stand‐alone units (frequency sub‐bands). Second, to speed up image recovery, mapping the sub‐band image of the wavelet is then measured using its approximate image. Third, the added sub‐pixel layer at the end of the network model is intended to reproduce image quality using a plexus framework. The approximation sub‐band images obtained after discrete wavelet transform wavelet decomposition are used as input rather than the original image because of their high‐frequency data and preserved characteristics. Five current super‐resolution neural network approaches are compared with the proposed technique and tested on three pubic satellite image datasets and two benchmark datasets. The experimental findings are well compared qualitatively and quantitatively.

Super-resolution (SR) has received extensive attention in recent years for satellite image processing in a wide range of application scenarios, such as land classification, identification of changes, the discovery of resources, etc. Satellite images from satellite sensors are mostly low-resolution (LR) images, so they do not completely fulfill object detection and analysis criteria. SR has multiple residual network frameworks in deep learning that have improved performance and can extend thousands of layers in the system. However, each layer improves accuracy by doubling the number of layers, although training thousands of layers are too expensive, the process is slow, and there are functional recovery issues. We proposed a transferred wide residual Single Image Super-Resolution (SISR) remote sensing deep neural network model (WRSR). By increasing the width and reducing the residual network depth, the proposed approach has dramatically reduced memory costs. As a result, our model reduced memory costs by 21% in Enhanced Deep Residual Super-Resolution (EDSR) and 34% in SRResNet as a direct consequence of the in-depth reduction. The proposed architecture improves the efficiency of training loss by performing weight normalization instead of augmentation technology. We compared our method to five recent existing super-resolution (SR) deep neural network methods, tested over three public satellite image datasets and a standard reference (PRIM) dataset. Experiment analysis is evaluated in peak to signal noise ratio (PSNR) and structural similarity index measure (SSIM).

The object identification within an image captured during rough weather conditions (such as haze, fog) poses difficulty due to the reduction of an image. The rough weather conditions lead not only to the variation of the image's visual effect but also to the disadvantage of post‐processing of an image. Furthermore, it causes inconvenience of all types of instruments that rely on optical imaging, such as satellite remote‐sensing systems, aerial photo systems, outdoor monitoring systems, and object identification systems, respectively. Hence, the improvement and restorement of the visual effects and enhanced post‐processing are needed. This research introduces a new image enhancement approach for image dehazing based on dark channel prior and piecewise linear transformation; also, the histogram equalisation technique, i.e. contrast limited adaptive histogram equalisation is applied. A dark channel prior is well known for its simplicity and productivity. In this work, the dark channel prior to a new angle is analysed in the first step, where average patch sizes are estimated for the computation of haze densities. Furthermore, the sky is approximated up to 5–10% of the hazy images, which has a good effect in removing the haze from the image. Using the dark channel, the proposed algorithm significantly boosted the effects of the dark images as well as reduced the influence of haze and noise. Eventually, for colour correction, the piecewise linear transformation technique is applied, which enhances the colour close to the original image. Experimental results demonstrate that the proposed method significantly improves the visibility of the algorithm on dark remote‐sensing images as well as on hazy natural images.

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.

The extensive deployment of wireless infrastructure provides a low-cost approach to tracking of mobile phone users in indoor environments using received signal strength (RSS). Crowdsourcing has been promoted as an efficient way to reduce the labor-intensive site survey process in conventional fingerprint-based localization systems. Despite its stated advantages, use of crowdwourcing for localization has issues of accuracy and reliability in indoor applications, in large part because of multipath propagation. This paper discusses and evaluates a Bayesian approach to localization of mobile users based on a crowdsourced fingerprint, in which environmental constraints as well as dynamic property of the mobile are incorporated as priors. Both a Markov chain and a semi-Markov chain approach are applied for modeling the transition and duration statistics of the mobile user across different location cells. Field test results demonstrate the effectiveness of introducing this additional information for localization in a real-world wireless network.

It has been an important technique to reduce sidelobes for processing nonzero Doppler target returns under Golay complementary waveforms. While the pointwise minimization processor in our early work shows an enhanced performance on the suppression of range sidelobes compared with existing approaches, it has a reduced detection probability to target with location uncertainties. In this paper, we present an alternative processing method which combines the radar returns in two separated pairs of Golay complementary waveforms through a pointwise addition processor. Our simulation result shows that this alternative method has a similar performance to that of the pointwise minimum processor for range sidelobes suppression and computational complexity but results in an improved target detection probability, with an acceptable decrease on the peak to peak-sidelobe ratio.

Absolute localization of a flying UAV on its own in a global-navigation-satellite-system (GNSS)-denied environment is always a challenge. In this paper, we present a landmark-based approach where a UAV is automatically locked into the landmark scene shown in a georeferenced image via a feedback control loop, which is driven by the output of an aerial image registration. To pursue a real-time application, we design and implement a speeded-up-robust-features (SURF)-based image registration algorithm that focuses efficiency and robustness under a 2D geometric transformation. A linear UAV controller with signals of four degrees of freedom is derived from the estimated transformation matrix. The approach is validated in a virtual simulation environment, with experimental results demonstrating the effectiveness and robustness of the proposed UAV self-localization system.

This paper considers the two-dimensional (2D) anchorless localization problem for sensor networks in global positioning system (GPS)-denied environments. We present an efficient method, based on the multidimensional scaling (MDS) algorithm, in order to estimate the positions of the nodes in the network using measurements of the inter-node distances. The proposed method takes advantage of the mobility of the nodes to address the location ambiguity problem, i.e., rotation and flip ambiguity, which arises in the anchorless MDS algorithm. Knowledge of the displacement of the moving node is used to produce an analytical solution for the noise-free case. Subsequently, a least squares estimator is presented for the noisy scenario and the associated closed-form solution derived. The simulations show that the proposed algorithm accurately and efficiently estimates the locations of nodes, outperforming alternative methods.

Work of Pezeshki, Calderbank and others [1, 2] has shown that choice of transmission order of Golay complementary waveforms in radar pulse trains can significantly improve Doppler resilience in detecting nonzero Doppler targets. For multiple targets a Weighted average Doppler (WD) algorithm [3] based on our earlier work has been shown to provide improvements. In this paper, we demonstrate that the Weighted Sidelobe Minimization procedure, developed in our recent work that combines the outputs of WD and the existing Binominal Design algorithms for ordering transmitted complementary waveform sequences using a point-wise minimization process (PMP), is also effective for the elimination of false target returns arising from sea clutter. A detailed analysis based on a Swerling II target model in terms of target detection probability is presented to validate the use of PMP. Our validation is strengthened by numerical simulations for both fixed and randomized scenarios.

We describe a method for processing radar signals, combining the outputs of two – a Weighted average Doppler algorithm and an existing Binominal Design algorithm via a point-wise minimum processor. The method renders a better performance in range sidelobe suppression for the detection of multiple moving targets using Golay complementary waveforms than the existing Binominal Design algorithm. The effectiveness of the method is justified by analyzing the corresponding ambiguity functions. We also show the performance of the new signal processing method by simulations under a realistic scenario.

Image dehazing is a fast-growing research area in image processing and computer vision. Due to the extreme fog, haze, and air dispersion within an environment, the hazy image raises several challenges in retrieving original image information type. However, past techniques endure massive computation complexity and even the distortion of original images such as halos and over-saturation. In this research work, a new wavelet Hybrid (Local-Global Combined) Network is proposed for single image dehazing using a convolution neural network (CNN) in the wavelet domain (WH-Net). It is observed that low-level features such as edges are more important than high-level features such as texture. So we have used 2-DWT to decompose the single image model into the frequency subbands, which performs more quickly. It is demonstrated that the estimation of wavelet sub-bands reformulates the trainable end-to-end learning with a special architecture where DWT and IDWT are the feature extraction layers instead of Conv and Deconv, distinguishing it from classical CNN networks. The WHNet method is designed to achieve multi-level representations of hazy images to provide local and global information. The proposed network prominent features are designed with fewer convolution layers without decreasing performance relative to the commonly observed deeper learning models. Compared to several state-of-the-art algorithms, our proposed WHNet neural network surpasses in visual and quantitative performances on three public datasets.

The Chinese remainder theorem (CRT) is often used to find unwrap signal phase over multiple wavelengths as a means of estimating total travel time of a signal. Existing approaches, implemented efficiently in closed form using either standard CRT or methods derived from lattice theory, require the wavelengths involved to be coprime. This can be a limiting factor in real applications, especially where the available signal bandwidth is limited. In this letter, we derive a new algorithm to extend the closed-form lattice algorithm to accommodate non-coprime wavelengths. The benefit of this development is demonstrated with a Wi-Fi wireless device localization example.

We propose an iterative nonlinear estimator based on the technique of variational Bayesian optimization. The posterior distribution of the underlying system state is approximated by a solvable variational distribution approached iteratively using evidence lower bound optimization subject to a minimal weighted Kullback-Leibler divergence, where a penalty factor is considered to adjust the step size of the iteration. Based on linearization, the iterative nonlinear filter is derived in a closed-form. The performance of the proposed algorithm is compared with several nonlinear filters in the literature using simulated target tracking examples.

Optimal trajectories for two platforms with similar dynamics are calculated using a game theoretic formulation. Each platform makes noisy observations of the kinematic state of the other. The objective of each is to maximise observable information about the other while minimising the information the other is able to acquire about it. That is to say, each platform maximises the mutual information between the expected future measurement of the opposing platform and the current likelihood of the state whilst minimising the estimated mutual information between potential measurements of itself by the other and its actual state. The multi-objective optimisation problem for each platform is converted to a single optimisation using the Pareto parameter to weigh the relative importance of the two information measures. The relationship between the two Pareto parameters, and different initial track initialisations is investigated. Remarkably this complex coupled system of two platforms exhibits, for suitably chosen values of the Pareto parameters, interesting cyclical behaviours that are worthy of further exploration.

Multipath propagation makes the use of received signal strength (RSS) unreliable as a signal propagation model for localization of a radio source based on RSS data. An approach to mitigation of this problem is the use of a Hidden Markov model (HMM) to represent the relationship between RSS and the radio source location by incorporating an environment prior and RSS source dynamics. The HMM structure forces a geometric form for the distribution for the sojourn time. This, combined with missing data problems, reduces confidence in location estimation. It is found, in this paper, that Hidden semi-Markov Models (HsMMs), with a more flexible sojourn time distribution are more able to represent source dynamics while retaining the advantages of HMMs for environmental constraints and improving resilience to missing measurements. The performance of the proposed HsMM algorithm is compared with a standard HMM via experiments and simulations involving indoor radio source localization using RSS data. Simulations use a ray tracing software-based simulator and the experiments for transmitter localization with RSS data are collected by software defined radios.

Golay complementary waveforms can, in theory, yield radar returns of high range resolution with essentially zero sidelobes. In practice, when deployed conventionally, while high signal-to-noise ratios can be achieved for static target detection, significant range sidelobes are generated by target returns of nonzero Doppler causing unreliable detection. We consider signal processing techniques using Golay complementary waveforms to improve radar detection performance in scenarios involving multiple nonzero Doppler targets. A signal processing procedure based on an existing, so called, Binomial Design algorithm that alters the transmission order of Golay complementary waveforms and weights the returns is proposed in an attempt to achieve an enhanced illumination performance. The procedure applies one of three proposed waveform transmission ordering algorithms, followed by a pointwise nonlinear processor combining the outputs of the Binomial Design algorithm and one of the ordering algorithms. The computational complexity of the Binomial Design algorithm and the three ordering algorithms are compared, and a statistical analysis of the performance of the pointwise nonlinear processing is given. Estimation of the areas in the Delay–Doppler map occupied by significant range sidelobes for given targets are also discussed. Numerical simulations for the comparison of the performances of the Binomial Design algorithm and the three ordering algorithms are presented for both fixed and randomized target locations. The simulation results demonstrate that the proposed signal processing procedure has a better detection performance in terms of lower sidelobes and higher Doppler resolution in the presence of multiple nonzero Doppler targets compared to existing methods.

We propose an alternative waveform scheme built on mutually-orthogonal complementary sets for a distributed multistatic radar. Our analysis and simulation show a reduced frequency band requirement for signal separation between antennas with centralized signal processing using the same carrier frequency. While the scheme can tolerate fluctuations of carrier frequencies and phases, range sidelobes arise when carrier frequencies between antennas are significantly different.

The analysis of dynamic laser speckle is widely known as an effective non-contact technique for detecting micro-activity on a sample surface. However, the analysis obtained using conventional statistical methods is heavily influenced by the properties of the sample, laser beam and ambient lighting. Recently, we proposed a new normal vector based dynamic speckle analysis method and demonstrated its ability to remove interference due to non-uniform reflectivity, illumination, and time-varying ambient lighting. In this paper, the normal vector based statistical technique for dynamic speckle analysis is further justified by the application to leaf surface micro-motion detection for monitoring plant water status. We carried out experiments for surface activity detection of attached leaves to monitor variation of leaf water status. The presented results highlight the advantage of the normal vector based analysis over image intensity based methods and demonstrate the potential of measuring plant water status via dynamic laser speckle analysis.

Dynamic laser speckle analysis is a useful measurement technique which can be used to analyze micro-motion on a sample surface via temporal statistics based on a sequence of speckle images. The existing analysis methods in the literature are based on intensity variation of the speckle images over time. It is observed that the robustness of these methods can be significantly influenced by the presence of non-uniform incidence and reflectivity, and unrelated image intensity variations. Hence, their applicable circumstances are limited. In this paper, a novel method for dynamic speckle analysis is proposed to address the interference of non-uniform incidence and reflectivity, and unrelated intensity variations. The dynamics of the speckle pattern at a pixel is measured by the temporal statistics for the directions of local normal vectors (rather the intensities) on every speckle image over time. The proposed method can effectively eliminate the aforementioned impacts in a dynamic speckle analysis process. Moreover, the local normal vector at a particular pixel in a speckle image also carries local geometry information regarding to its neighboring pixels. Three algorithms for computing the statistics of the directions of local normal vectors over time are subsequently presented. The effectiveness of the proposed method is demonstrated via the analysis of dynamic speckle patterns measured from a paint drying experiment. The robustness of the proposed method is compared with that of the typical existing methods through processing the dynamic speckle of simulation experiments under non-uniform and varying lighting conditions.

Measuring a distance using phase measurements is a common practice in many areas of engineering. Almost inevitably these measurements are accompanied by noise, and are always subject to ambiguity resulting from the phase of modulo $2\pi$ . In the presence of phase ambiguity, where for instance the unknown distance is far longer than the wavelength of the signal carrying the phase measurement, the distance cannot be uniquely determined. One way to resolve this phase ambiguity is to measure the signal phase at multiple frequencies, converting the phase ambiguity problem into one of solving a family of Diophantine equations. Typically, under some reasonable assumptions, the Diophantine problems can be solved using the Chinese Reminder Theorem as documented in the literature. However, the existing algorithms can experience significant computational overhead for a given application because an exhaustive search is required. In this paper, a novel method addressing the phase ambiguity issue using lattice theoretic ideas is proposed and a closed-form algorithm is presented for the estimation of the number of wavelengths in the unknown distance using the phase measurements taken at multiple wavelengths. The algorithm is extremely efficient as the Diophantine equations are solved without searching. The unknown distance can then be estimated via a maximum likelihood method using the unwrapped phase measurement. A statistical bound of the measurement noise which ensures that the number of whole wavelengths in the unknown distance can be found with a probability close to unity is derived. The robustness, efficiency and estimation accuracy of the proposed method are demonstrated by the simulated results presented.

Inverse synthetic aperture radar (ISAR) imaging is conventionally addressed by means of fast Fourier transform (FFT) method. In this paper, a frame processing method derived based on frame theory for ISAR imaging is proposed as an alternative to FFT based approaches. In this method, a finite set of K consecutive target returns after a front-end processing by radar receiver are treated as a vector in a Hilbert space where a frame structure is defined. A frame and its dual are constructed as the functions of waveform and processing parameters and the target rotation rate. The latter can be estimated online. For certain classes of high resolution radar (HRR) waveforms, we show that it is possible to use standard methods of frame analysis and synthesis to reconstruct the ISAR image from radar returns. This method is analogous to those of reflective tomography, and does not suffer from the “range walk” problem exhibited by the FFT based approaches which poses constraints on the feasible aperture size for observing a target. Both simulated and experimental results are presented to demonstrate the effectiveness of the proposed method. Practical implementation and issues of the proposed frame processing technique are also discussed.

In target detection and tracking, the resolvability of multiple closely spaced targets of the sensor measurement is an important criterion for measuring the sensing ability of sensor systems like radar and sonar. The concept of information resolution for a sensor measurement system, which is defined in the framework of information geometry, is introduced. In particular, the information resolution of radar/sonar systems is generalised from the work on existing radar resolution pioneered by Woodward, et al. [1, 2] and defined on statistical manifolds where the intrinsic geometrical structure of both waveform, measurement, and noise models of the underlying sensing devices are conveniently characterized in terms of the Fisher information metric. This work is motivated by the fact that existing radar resolution identifies the ambiguities of the underlying waveforms regardless of the influence of noise but the latter often has significant contributions to outcomes such as target detection and tracking, radar imaging, etc. To this end, information resolution provides a unified statistical measure for the capability of sensing devices for a given application. Examples of basic radar measurements are presented to highlight the importance of information resolution for measurement systems in practical applications. Refereed/Peer-reviewed

Automatic optical and infrared (IR) image registration is a crucial requirement in the estimation of plant canopy temperature. The latter is used to compute the plant water stress status which can potentially be applied to an automated control system for optimal crop irrigation management. Due to the nature of image sources and plant scene, it is difficult to find enough common features between the pair of images to register them using a simple method. In this paper an improved registration algorithm is described, where a modified Sobel edge detector is adopted and a variable resolution scheme is applied. The algorithm can improve the registration efficiency considerably compared to our previous developed registration method. Experiment results show that the modified algorithm can achieve the same registration accuracy and a better success rate compared to that from the original algorithm with a substantial reduction of computational complexity.

This paper deals with the problem of sensor localization when the localization error is nonlinearly propagated over the sensor network as occurs in Radio Interferometric Positioning System (RIPS). A noise model that takes the location uncertainties of anchor nodes into account in the node localization process has been derived based on a generic nonlinear measurement model that encapsulates the use of many popular node localization techniques. These include circular, parabolic and hyperbolic methods. This development enables the use of a distributed stochastic estimation method for node localization of wireless sensor networks with nonlinear measurements in the presence of anchor node uncertainties. The effectiveness of the derived equivalent noise model is demonstrated via a simulated progressive localization of a mote network using the RIPS measurements. Refereed/Peer-reviewed

In many radar and sonar tracking systems, the target state typically includes target position and velocity components that are estimated from a time sequence of target position and Doppler measurements. The use of measured Doppler information directly in the target trajectory estimation leads to a nonlinear filter implementation such as the extended Kalman filter (EKF), particle filter etc. We investigate a method for including Doppler measurements as part of the data association process and then assess the benefits of this approach. It is well understood that data association performance can dominate the total performance of a tracker that is designed to track targets in the presence of clutter. In this case, the Doppler component of the measurements may be used in combination with the target position measurement as an additional discriminant of measurement origin. We have developed a simple but efficient Doppler data association (DDA) method which utilises both position and Doppler measurements for single and multi-target tracking. If the Doppler measurements are not used in trajectory state estimation, then the nonlinear filters for the incorporation of Doppler measurements are not required, however a significant improvement in tracking performance is still observed. The proposed DDA method is demonstrated using both the linear multi-target integrated probabilistic data association algorithm (LMIPDA) and the linear multi-target integrated track splitting algorithm (LMITS) in an active sonar underwater multi-target tracking scenario.

We describe here a method for the analysis of materials on a conveyor belt using the natural gamma spectra collected with a BGO (Bismuth Germanate) gamma ray detector. This detector collects gamma ray emissions from the Potassium (K), Uranium (U), and Thorium (Th) atoms in the materials. Based on these data, and using a Poisson model for the data generation, a statistical model is proposed and an approximate maximum likelihood (ML) technique based on the expectation-maximization (EM) algorithm is then used to estimate the amount of each of the three elements in the material. The statistical model is further refined to incorporate parameters of drift in the detector and an estimation technique for this is developed and tested against real data. The Cramer-Rao lower bounds for the estimators are calculated.

When detecting a target using radar in the presence of multipath fading, the signal to noise ratio (SNR) at receiver can be dramatically reduced at certain ranges due to multipath signal cancellation, which leads to a significant drop of the detection probability at those ranges. The detection of a low elevation sea-surface target (LESST) is one example. For target tracking, this special case contrasts to the usual situation where we may often assume a constant probability of detection. Automatic target tracking requires a measure of track quality to discriminate between true and false tracks. This track quality measure should take both the existence of target and the SNR of the sensor into account, and be consistent with tracking performance. This paper investigates automatic target tracking using the integrated probabilistic data association (IPDA) type filters in the presence of multipath fading. Several existing single-scan probabilistic data association (PDA)-based filters, which calculate the probabilistic measure of track quality while estimating the track state, are evaluated in the scenario of tracking a nonmaneuvering LESST in the presence of both multipath fading and clutter. In addition, a variable Markov chain (VMC) IPDA tracker (IPDA-VMC), which has joint merits of both IPDA and the IPDA with a three state Markov chain (IPDA-M2), has been designed and evaluated here. In the derivation of track quality measure of both IPDA-M2 and 1PDA-VMC, the possibility that a target is present but is temporarily unable to be detected has been explicitly considered. A tracker using this track quality measure for false track discrimination has significantly improved LESST tracking performance. A simulation study verifies that both IPDA-M2 and IPDA-VMC have better capability to handle the LESST tracks and have an improved tracking performance over other PDA-based tracking algorithms under consideration.

Recovering a distance from ambiguous phase measurements observed at multiple signal frequencies involves the estimation of both integer and scalar quantities simultaneously. The problem is optimally addressed by existing methods only when the measurement noise is IID. A noise ‘whitening’ procedure is derived with a least-square estimation structure inherent to the Lattice method proposed previously which thus extends this Lattice-based algorithm to cope with coloured phase measurement noise in an optimal manner demonstrated via simulation.

The variable structure multiple model (VSMM) approach to the maneuvering target tracking problem is considered. A new VSMM design, the minimal submodel-set switching (MSMSS) algorithm for tracking a maneuvering target is presented. The MSMSS algorithm adaptively determines the minimal set of models from the total model set and uses this to perform multiple models (MM) estimation. In addition, an iterative MSMSS algorithm with improved maneuver detection and termination properties is developed. Simulations results demonstrate that, compared with a standard interacting MM (IMM), the proposed algorithms require significantly lower computation while maintaining similar tracking performance. Alternatively, for a computational load similar to IMM, the new algorithms display significantly improved performance.

Gating techniques for maneuvering target tracking using an IMM-PDA filter are considered in this paper. Existing gating techniques, namely, centralized gating and model based gating are reviewed and new gating techniques, called model probability weighted gating and two-stage model probability weighted gating, are proposed. A benchmark trajectory and a random scenario are considered for performance evaluation of the gating techniques in terms of RMS errors, percentage of track loss and computational load.

Frame processing method offers a model-based approach to Inverse Synthetic Aperture Radar (ISAR) imaging. It also provides a way to estimate the rotation rate of a non-cooperative target from radar returns via the frame operator properties. In this paper, the relationship between the best achievable ISAR image and the reconstructed image from radar returns was derived in the framework of Finite Frame Processing theory. We show that image defocusing caused by the use of an incorrect target rotation rate is interpreted under the FP method as a frame operator mismatch problem which causes energy dispersion. The unknown target rotation rate may be computed by optimizing the frame operator via a prominent point. Consequently, a prominent intensity maximization method in FP framework was proposed to estimate the underlying target rotation rate from radar returns. In addition, an image filtering technique was implemented to assist searching for a prominent point in practice. The proposed method is justified via a simulation analysis on the performance of FP imaging versus target rotation rate error. Effectiveness of the proposed method is also confirmed from real ISAR data experiments.

When tracking a target using phase-only signal returns, range ambiguities due to the modulo 2π in measured signal phases are a major issue. Standard approaches to such problems would typically involve the use of Diophantine equations. In this paper, a simpler and robust solution is examined which uses look-up tables defined between the phase measurement and target location spaces to determine the phase measurement mapping. We show, first, that when the target motion is significant between data sampling intervals the location ambiguity can be resolved over time via known target-in-cluster tracking techniques. This method determines the optimal allocation of location with respect to phase measurements relative to the quantization of look up table values. Second, when the target is undergoing micromotions (jitter) which results in the same collection of candidate locations from phase measurements over time, the location ambiguity can be resolved using a novel phase distribution discrimination method. In this method a probability density function of the ambiguous phase-only measurement is derived that takes both sensor noise and target motion distributions into account based on directional statistics. Optimal locations are inferred from such distributions. Examples are given to demonstrate the effectiveness of these proposed methods. Refereed/Peer-reviewed

A method is proposed for estimating the target rotation rate for ISAR imaging based on the frame processing technique. The method utilises the intrinsic structures of the frame which span the Hilbert space of radar return from which the rotational information of the target is extracted. For a prominent point, the intensity of the projection is maximum when the frame component corresponding to the prominent point is built with the target rotation rate.

Probabilistic Multiple Hypothesis Tracking (PMHT) is an algorithm for multi-target tracking in clutter with computational requirements, which are linear in the number of targets and the number of measurements. In order to achieve this, the PMHT removes the point target constraint, and uses the expectation maximisation procedure to optimise both data association probabilities and the target trajectory state estimates. However, PMHT is known to have high track-loss percentage in comparison with Probabilistic Data Association, at least in point target tracking. The authors propose a new PMHT-like algorithm to solve some problems of PMHT. In this study they revert the point target constraint. The authors call the new algorithm Point target PMHT. Simulation results show the efficiency of this method.