Detecting weak motion targets in complex environment by using radar sensors is of great importance for Internet of Things (IoT) applications. However, the complex environment with strong clutter and low-signal-to-clutter ratio (SCR) usually poses formidable challenges for achieving effective detection. To deal with this problem, different from the conventional energy-based method, this article proposes a novel detection technique based on the theory of information geometry (IG), which captures the nonlinear correlation feature (CF) of observed signals to effectively distinguish the target from clutter. Specifically, inherit the advantages of IG, we formulate a CF manifold and the geometric distances are derived to measure the dissimilarity of target and clutter. Then, a CF-based IG (CF-IG) detector is proposed. Moreover, we consider a multiframe detection strategy to further enhance the detection performance. A multiframe track-before-detect (TBD) method on the CF manifold is performed. Experimental results conducted on IPIX radar data and real measured drone target data validate the effectiveness and superiority of the proposed method.

In this letter, a novel radar forward-looking imaging technique based on beam pattern modulation and beam scanning is presented. First, the chirp beam, which presents quadratic varying phases within the main lobe, is generated and scans as a chirp pulse propagating along the azimuth direction by differentially exciting each element of a uniform linear array (ULA). Second, the target distribution is successfully reconstructed using 2-D pulse compression, and a theoretical analysis of the azimuth resolution is conducted. Finally, the sparse representation (SR) technique is employed to enhance the imaging performance. Simulation and experimental results validate the effectiveness and potential of the proposed method for acquiring high-resolution forward-looking images. This work holds promise for advancing the development of radar forward-looking methods and systems.

Target detection, especially in heterogeneous environments with strong clutter, is a difficult problem in signal processing. The existence of strong clutter in the heterogeneous environment usually causes the estimate of the clutter covariance matrix (CCM) to deviate from the truth, resulting in a degradation in detection performance. To address this problem, a novel CCM estimate method based on heterogeneous clutter clustering is proposed to improve the performance of radar detectors. First, it uses the distribution characteristics of clutter samples on the Hermitian positive-definite (HPD) manifold to segregate the clutter into clusters and select representative samples. Second, based on the cluster centers, a covariance matrix that can enhance the discrimination between the target echo and clutter on the HPD manifold is constructed. The proposed method can effectively enhance the discrimination between target and clutter because the reconstructed covariance matrix can reflect the clutter characteristics better. The advantage of the proposed method is demonstrated by experimental results on both simulated and real data. Compared with the conventional CCM estimation method based on the Log-Euclidean (LE) mean, the proposed method can improve the detection probability by 29%.

In this paper, the power spectrum information geometry (PSIG) detector, which inherits the performance advantages of matrix information geometry (MIG) detectors in heterogeneous clutter backgrounds, is proposed. The PSIG detector can address two urgent problems in applications of MIG detectors, which are the expensive computation expense and unavailable acquisition ability of target velocity. Specifically, the PSIG detector utilizes power spectrums instead of high dimensional covariance matrices to characterize sample data and employs subband filter bank to extend the detection from range cells to range-Doppler cells, thus it requires less computation expense and can obtain the target velocity information according to the Doppler cell. Experiments based on the real data show the advantages of the proposed PSIG detectors in comparison with competitive methods. Especially, in the experiments with the real-recorded airborne radar data, the proposed method can effectively suppress the heterogeneous main-lobe clutter without any prior knowledge and provides detection probability improvement of more than 30% to the competitive methods with low false-alarm ratios.

Radar 3-D forward-looking imaging has always been a difficult issue in the radar detection. In this letter, a forward-looking 3-D imaging method based on attribute scattering model (ASM) for extended targets is proposed. First, an imaging model based on point scattering model (PSM) with wavefront modulation technique is constructed to achieve 3-D forward-looking imaging. Second, considering the fact that PSM-based imaging model assumes that the target is composed of a set of discrete points, it is not suitable for reconstructing the structure feature of extended targets, i.e., line structure and surface structure. To extract more geometry information of the target, the ASM that includes point scatterers (PSs), line-segment scatterers (LSSs), and rectangular-plate scatterers (RPSs) is adapted to the 3-D imaging model. Solving the parameter sets of PSs, LSSs, and RPSs with the alternating direction method of multipliers (ADMMs) algorithm, the edge and surface structure of the extended target can be reconstructed. The simulation results based on electromagnetic (EM) calculation by FEKO verify the effectiveness of the proposed method.

The acquisition of inverse synthetic aperture radar (ISAR) images of aircraft targets in complete attitude is challenging due to the noncooperative nature of the aircraft targets. To determine the characteristics of the scattering centers of aircraft targets when the attitude changes, an effective scattering center model based on the ISAR images is proposed in this letter. First, the attitude sensitivity of the aircraft target ISAR image in azimuth and pitch is analyzed. Then, a sinc mixture model (SMM) based on the ISAR imaging principle with the aim to describe the characteristics of the target scattering center and a method for estimating the SMM’s parameters are proposed. Finally, target images at arbitrary attitude are reconstructed by combining the target scattering center theory. Furthermore, compared with conditional generative adversarial network (CGAN) and linear interpolation (LI), simulation results confirm that the proposed method is effective.

In this letter, an effective parametric IF estimation method based on piece-wise sparse representation (PWSR) is proposed to enhance the precision of conventional time-frequency distribution (TFD)-based estimators. Firstly, the signal is divided into a series of short-time segments according to the piece-wise quadratic frequency modulation (QFM) model. Then, the corresponding QFM parameters of each segment are accurately estimated by solving a sparse representation (SR) problem. Moreover, a construction scheme enabling the QFM dictionary to vary adaptively with the analyzed short-time signal is also provided. Finally, the IF of each segment is reconstructed according to the SR solution individually and combined together to generate the complete IF estimates. A radar imaging example verifies that the proposed method achieves a notable improvement in estimation accuracy as compared to existing TFD-based approaches.

To improve target detection performance in non-Gaussian backgrounds, the joint design of transmit sequence and receive filter for multiple-input-multiple-output (MIMO) radar is studied. By approximating the probability density function of observed non-Gaussian data with the Gaussian mixture model, a Riemannian manifold of Gaussian mixture distribution is developed to depict the complicated background first. Then, maximizing the geometric distance on manifolds, which is converted by maximizing the discrimination between the target and clutter, is proposed as the criterion for the joint design of transmit sequence and receive filter. Thereby, under the constant-modulus constraint, the joint design problem can be transformed into an optimization problem. However, the proposed optimization problem is non-convex and constrained. To solve this problem, a Riemannian optimization framework is provided. By taking the advantage of the underlying geometric and algebraic structure of the constraint space, the original constrained optimization problem in Euclidean space can be transformed into the unconstraint optimization problem over Riemannian product manifolds. Moreover, to obtain the global optimal solution, the Riemannian gradient of the geometric distance cost is derived for the conjugate gradient algorithm. Experiments demonstrate that the proposed method shows advantages in detection performance compared with competitive methods.

This article deals with the problem of detecting maritime targets embedded in nonhomogeneous sea clutter, where the limited number of secondary data is available due to the heterogeneity of sea clutter. A class of linear discriminant analysis (LDA)-based matrix information geometry (MIG) detectors is proposed in the supervised scenario. As customary, Hermitian positive-definite (HPD) matrices are used to model the observational sample data, and the clutter covariance matrix of the received dataset is estimated as the geometric mean of the secondary HPD matrices. Given a set of training HPD matrices with class labels, which are elements of a higher dimensional HPD matrix manifold, the LDA manifold projection learns a mapping from the higher dimensional HPD matrix manifold to a lower dimensional one subject to maximum discrimination. In this study, the LDA manifold projection, with the cost function maximizing between-class distance while minimizing within-class distance, is formulated as an optimization problem in the Stiefel manifold. Four robust LDA-MIG detectors corresponding to different geometric measures are proposed. Numerical results based on both simulated radar clutter with interferences and real IPIX radar data show the advantage of the proposed LDA-MIG detectors against their counterparts without using LDA and the state-of-the-art maritime target detection methods in nonhomogeneous sea clutter.

This paper addresses the problem of detecting radar targets submerged into strong sea clutter background. In this study, a novel type of detection method based on matrix information geometry (MIG) is developed. Filtering process and manifold projection are incorporated into detector design. Firstly, a filtering scheme for correlation coefficients is established via subband decomposition, such that a subband Hermitian positive definite (HPD) manifold constructed by a set of subband HPD matrices is formulated. Accordingly, the detection is performed as discriminating the target and the clutter on the subband HPD manifold. Then, in order to enhance the discriminative power between the target and the strong clutter, a manifold projection method that maps the HPD manifold into a lower-dimensional and more discriminative one is devised. In this study, the manifold projection is formulated as an optimization problem on a Stiefel manifold according to the principle of maximizing signal-to-clutter ratio (SCR). Subsequently, a manifold projection based subband MIG detector is proposed. Extensive experiments based on simulated data and real radar data are carried out to verify the effectiveness of the proposed method. The experimental results demonstrate that the proposed method can efficiently suppress the strong sea clutter and achieve better detection performance than the competitors.

The restricted physical antenna aperture usually degrades the acquisition of target images with high azimuth resolution in radar forward-looking sight. To make breakthrough on this problem, this article offers a forward-looking radar imaging methodology by the illumination beam design and virtual aperture processing, which can improve the azimuth resolution. First, the imaging model is established with respect to the observation scenario, and the echo characteristics of the forward-looking virtual array are analyzed. Second, compared with the back-projection (BP)-based algorithm, the polar format imaging (PFI) algorithm with higher efficiency is proposed to obtain the focused images of target in the forward-looking locations. Finally, comprehensive simulations and real measured experiments are performed to corroborate the effectiveness of the proposals and show the imaging performance with different influencing factors.

In this letter, a novel microwave coincidence imaging (MCI) approach is proposed based on a multiple-input single-output (MISO) radar system to deal with the low signal-to-noise ratio (SNR) scenarios and off-grid problem. First, in coherent-detecting part, a linear frequency modulated (LFM) signal is transmitted, and dechirping processes are conducted to enhance the SNR of echoes. Then, in incoherent-modulating part, the post random phase-shifting modulations are conducted on the echoes, hence the temporal–spatial orthogonal reference radiation field of MCI is constructed, which provides the potential information of super-resolution. Further, to solve the off-grid problem of target’s scatterers in MCI, a new projecting-residual-based selection criterion is also proposed, combined with the preexisting signal subspace matching (SSM) method. The proposed method could largely eliminate the off-grid errors while conduct a reference matrix selection procedure, hence the reconstruction accuracy and computational complexity can be much improved and reduced, respectively. Finally, the validity of the proposed method and the super-resolution ability of MCI are verified by experiments.

Nonstationary moving target detection in spiky sea clutter is a challenging task due to the high-power, time-varying, and target-like properties of sea spikes. In this letter, we propose a time-frequency correlation (TFC)-based constant false alarm rate (TFC-CFAR) detection method on the time-frequency manifold, and apply it to the nonstationary moving target detection in spiky sea clutter. The data samples in each range cell are modeled as a TFC matrix that captures the correlation between two frequency components of the time-frequency distribution. The clutter covariance matrix is estimated by the geometric mean of a set of TFC matrices in reference cells. Three geometric metrics are employed to measure the dissimilarity between the clutter and target signals. Based on these geometric measures, three TFC-CFAR detectors are compared. Experiments performed on a real IPIX radar dataset confirm that the TFC can be used for identifying and eliminating sea spikes, while the TFC-CFAR detector achieves better detection performance than the conventional detectors.

Moving targets in a heterogeneous environment can be extracted through traditional robust principal component analysis (RPCA). Competitive RPCA algorithms address a nonconvex constraint by relaxing it to a fixed rank. However, the solution does not necessarily reach a global optimum. To address this problem, a moving target detection method by RPCA in the topological space of low-rank matrices is proposed to obtain superior target detection performance. First, RPCA is considered in the topological space of low-rank matrices, which is the closure of the fixed-rank manifold. Then, combined with manifold optimization and the proximal gradient, the RPCA-PGTSLr algorithm is applied to solve the problem caused by a non-differentiable sparsity term, so that the target can be precisely extracted. Experiments performed on measured data demonstrate that the proposed method exhibits advantages in detection performance over competitive methods in a heterogeneous environment.

From the perspective of feature extraction and classification, deep neural networks are widely used in radar target detection. However, in heterogeneous environment, the traditional deep neural networks are difficult to extract a robust feature, which leads to the degradation of network detection performance. In order to address this problem, a radar target detection method with multi-task learning in heterogeneous environment is proposed. Considering the influence of heterogeneous data distribution, the proposed method designs a contrastive learning module added to a multi-task autoencoder. It can learn a compact and distinguishable feature representation, which enhances the feature separability between the clutter and the target. Simultaneously, a classifier is introduced to realize a binary detection in the feature representation. Comprehensive experiments are carried out to show that the proposed method guarantees a good detection performance in heterogeneous environment and solves the issue of over-fitting to a certain extent. Compared with some classical detectors, the proposed method shows better performance.

Radar forward-looking imaging has always been a difficult problem in the radar detection. Modulating the wavefront of radar transmitted signals provides a feasible method for radar forward-looking imaging. The existing high-resolution radar imaging algorithms assume that the target scattering coefficients are sparsely distributed. However, for complex targets, the scattering coefficients no longer satisfy the sparse prior. To solve the problems of forward-looking imaging for complex targets, in this letter, a sparse representation imaging method with dictionary learning is proposed. First, the principle and imaging model of microwave modulation are introduced to achieve radar forward-looking imaging. Second, the dictionary learning method is developed to learn adaptive transformation that exploit the edge features and structural information as well as provide a sparser presentation to further improve the quality of radar images. Third, the imaging performance for different types of complex targets under different signal-to-noise ratios are analyzed. The simulation results show that the proposed method can effectively reconstruct different types of complex targets.

Ship target detection in synthetic aperture radar (SAR) imagery is of great significance in the field of ocean monitoring. Classical constant false alarm rate (CFAR) detectors and emerging information geometry methods are model-driven essentially, requiring precise modeling of the sea clutter distribution. In the complex and changeable ocean scenes, the performance of these two types of detectors is limited. To solve this problem, a ship target detection algorithm in SAR imagery based on the maximum eigenvalue of the sample covariance matrix is proposed in this letter. Without seeking the distribution model of clutter backgrounds, the difference between the target and the clutter background is fully captured by constructing the sample covariance matrix, and its maximum eigenvalue is utilized as the test statistic. Experimental results on measured SAR images show that the proposed method achieves better detection performance and faster calculation speed compared with the existing typical methods.

Accurate time-frequency (TF) feature extraction of moving target is a challenging task due to the poor resolution and serious cross-terms of conventional TF analysis (TFA) methods. In this letter, an effective TFA algorithm based on the adaptive short-time sparse representation (ASTSR) is proposed to enhance the TF feature of moving target. Firstly, the limitation of the Fourier transform-based short-time TFA is revealed from the motion approximation perspective. Then, in order to achieve accurate motion approximation, the width of the analysis window is determined adaptively by minimizing the bandwidth of each short-time signal individually. Finally, the TF representation (TFR) with high energy concentration is obtained by utilizing the sparsity of these signal segments in the chirp dictionary. Comparisons indicate that the ASTSR provides high-resolution TFRs without producing interference terms at an acceptable computational cost while performing well in weak component expressing and signal denoising. Furthermore, a ISAR imaging example confirms the potential of the proposed method.

In this letter, a novel microwave coincidence imaging (MCI) method based on the attributed scattering model (ASM) is proposed. Unlike the classical MCI model which assumes the target as a set of discrete scatterers, the ASM-based MCI equation contains three kinds of reference matrices corresponding to the point-scatterers (PSs), the line-segment-scatterers (LSSs) and the rectangular-plate-scatterers (RPSs), respectively. Hence the ASM-based MCI could resolve richer information about the object geometries. By solving the imaging equation via the alternating direction method of multipliers (ADMM) algorithm, the scattering coefficients will be obtained and the target can be reconstructed according to the presetting parameter sets. Meantime, the ASM-based MCI also earns the superresolution ability like the classical MCI, which is brought in by the temporal-spatial orthogonal radiation field. Simulations and experiment are carried out to demonstrate the performance and superresolution ability of proposed method. The ASM-based MCI makes contributions to the progress of radar forward-looking imaging theory and technology.

Microwave coincidence imaging (MCI) is a novel staring imaging technique with high resolution in azimuth. In MCI, the continuous imaging area is discretized into fine grids and the target-scattering centers are assumed to be exactly located at the centers of prediscretized grids. Recently, parametric methods are applied to MCI as target reconstruction algorithms with resolution enhancement and quality improvement. However, in practical applications, grid mismatch will severely degrade the imaging quality of parametric methods because the target-scattering centers will not totally locate at the grid centers no matter how fine the grids are. In this article, a reweighted-dynamic-grid-based MCI (RDG-MCI) method is proposed. In RDG-MCI, grids are evolving from coarse to dense iteratively rather than being fixed, and hence, off-grid errors can be eliminated gradually. Meanwhile, the reconstructed coefficients are used as weighting factors of grids in a form of weighting matrix in the next iteration and nonkey grids will be dropped out. Hence, the dynamic grids will be focused around the positions where target scatterers are most likely to exist. Furthermore, the matrix uncertain sparse Bayesian learning (MUSBL) algorithm is adopted to eliminate the residual off-grid errors. Finally, a preferable imaging result can be obtained based on the updated nonuniform grids. Also, the theoretical expected Cramér–Rao bound (ECRB) is also derived to evaluate the performance of the proposed method. The effectiveness of the proposed method, along with the super-resolution ability of MCI, is verified by simulations and outdoor experiments.

Due to a serious shortage of training data, the performance of adaptive clutter suppression suffers remarkable degradation in heterogeneous environments. To address this problem, a novel clutter suppression method via affine transformation on manifolds is proposed. First, training samples in heterogeneous environments are characterized on an established manifold in which the distribution properties are analyzed. Then, a clutter classification scheme is proposed, whereby the KL divergence decision rule is derived to identify the training data as either homogenous or heterogeneous samples. Afterward, based on the distribution properties of samples and the clutter classification scheme, an affine transformation on manifolds is proposed for sample augmentation by transporting heterogeneous samples into the region of homogeneous samples. Finally, the clutter in the area of interest is suppressed on the manifold, which combines the transformed samples with the homogeneous samples, such that superior performance is obtained. Experiments on both simulated and real data validate the superiority of the proposed method in highly heterogeneous environments.

The electromagnetic (EM) vortex imaging has demonstrated superior performance in target detection and imaging with azimuthal super-resolution. However, the restricted elevation resolution degrades the acquisition of target spatial information, which limits the development of the radar imaging technology based on orbital angular momentum (OAM). This article offers a solution to achieve the 3-D EM vortex imaging, by effectively utilizing relative motion between radar and target in the line-of-sight (LOS) direction. First, the forward-looking radar imaging scenario is presented, the 3-D echo model is derived, and the characteristics are analyzed as well. Second, the imaging method, based on the back-projection (BP) and spectrum estimation method, is proposed to obtain the target’s 3-D focused image. Furthermore, the influence factors about the elevation resolution are analyzed by the point spread function (PSF). Finally, simulations are carried out to verify the effectiveness of the theoretical analyses.

Recently, the matrix information geometry (MIG) detector, which characterizes sample data as a Hermitian positive definite (HPD) matrix located on the HPD manifold, was rapidly developed and demonstrated extraordinary performance in numerous applications, especially in heterogeneous clutter backgrounds. In this paper, the geodesic normal coordinate (GNC)-based manifold filter is proposed to improve the detection performance of the MIG detector in strong clutter backgrounds. Using the GNC system, the distribution of target echoes and clutter on the high-dimensional manifold can be visualized and analyzed. Moreover, by exploiting the information concerning the distribution of matrices, the manifold filter is proposed to enhance target echoes and suppress strong clutter. Then, the manifold-filter-based MIG detector is designed, and its superiority is theoretically analyzed. The actual clutter data is utilized to verify the effectiveness of the proposed method. The results show that the proposed manifold filter achieves a signal-to-clutter ratio improvement of more than 5 dB over the existing MIG detectors.

This letter proposes an enhanced matrix constant false alarm rate (CFAR) detection method that works on the lower-dimensional Riemannian manifold. Motivated by general matrix CFAR detection method and dimensionality reduction scheme of the Riemannian manifold, this method obtains a mapping by maximizing the geometric test statistic. Dimensionality reduction is formulated as an orthonormal constraint optimization problem on the Grassmann manifold. Moreover, an explicit mapping is obtained by solving the optimization problem via conjugate gradient approach. Performances of the proposed method are evaluated on the lower-dimensional Riemannian manifold. Experiments on simulated data and real sea clutter data demonstrate that our method leads to the robustness to outliers and the improvement of detection performance over classical methods.

Corner points play an important role in moving object detection, especially in the case of free-moving camera. Corner points provide more accurate information than other pixels and reduce the computation which is unnecessary. Previous works only use intensity information to locate the corner points, however, the information that former and the last frames provided also can be used. We utilize the information to focus on more valuable area and ignore the invaluable area. The proposed algorithm is based on reinforcement learning, which regards the detection of corner points as a Markov process. In the Markov model, the video to be detected is regarded as environment, the selections of blocks for one corner point are regarded as actions and the performance of detection is regarded as state. Corner points are assigned to be the blocks which are seperated from original whole image. Experimentally, we select a conventional method which uses marching and Random Sample Consensus algorithm to obtain objects as the main framework and utilize our algorithm to improve the result. The comparison between the conventional method and the same one with our algorithm show that our algorithm reduce 70% of the false detection.

This study explores the application of geometric measures‐based means and medians on the Riemannian manifold of Hermitian positive‐definite (HPD) matrix to target detection problems in radar systems. Firstly, the slow‐time dimension of radar received clutter data in each cell is modelled and mapped to HPD matrix space, which can be described as a complex Riemannian manifold. Each point of this manifold is an HPD matrix. Then, several geometric measures are presented for measuring closeness between two HPD matrices. According to these measures, the means and medians of a finite collection of HPD matrices are deduced, and various matrix constant false alarm rate (CFAR) detectors are designed. The principle of target detection is that if a location has enough dissimilarity from the geometric mean or median estimated by its neighbouring locations, targets are supposed to appear at this location. Moreover, different distance measures adopted in detector can result in different performance of detection. These differences are owing to different measure structure, reflected by the anisotropy of a location on the Riemannian manifold. Numerical experiments are given to demonstrate the relationship between anisotropy of the geometric measures and the detection performance of their corresponding matrix CFAR detectors.

Quantum radar cross section (QRCS) is studied in this paper, which has an objective measure of the quantum scattering ability of a specified target. The interaction process between quantum radar and the target is introduced, and new results about quantum scattering characteristics of typical targets are reported. Simulation results demonstrate that the number of side lobes of QRCS is dependent on the target size and the interatomic distance; values of QRCS increase with the increasing of the signal photon frequency. For the cylinder target, QRCS increased as the radius increases. The work and results can be beneficial to the design of quantum radar system as well as the development of remote sensing of earth observation.

Recently, the electromagnetic waves carrying orbital angular momentum are applied to radar imaging, which has the ability of azimuth resolution without motion limitation. The image reconstructed by Fourier transform method has low quality due to the high sidelobes and the duplicate of real target. In this paper, we reveal the reason in two cases with the help of point spread function. Then the least-squares (LS) method is adopted to get the high-quality imaging. The parameterized imaging model is detailed and several factors affecting the solution are discussed. Numerical experiments show that the results produced by using LS method are much nicer than that by Fourier technique when the noise and model error are not too serious. Moreover, the LS method can lower the limit of elevation angle. Finally, the imaging quality are analyzed quantitatively when the noise and model error are present.

Motivated by classical coincidence imaging which has been realized in optical systems, an instantaneous microwave-radar imaging technique is proposed to obtain focused high-resolution images of targets without motion limitation. Such a radar coincidence imaging method resolves target scatterers based on measuring the independent waveforms of their echoes, which is quite different from conventional radar imaging techniques where target images are derived depending on time-delay and Doppler analysis. Due to the peculiar features of coincidence imaging, there are two potential advantages of the proposed imaging method over the conventional ones: 1) shortening the imaging time to even a pulse width without resolution deterioration so as to improve the performance of processing noncooperative targets and 2) simplifying the receiver complexity, resulting in a lower cost and platform flexibility in application. The basic principle of radar coincidence imaging is to employ the time-space independent detecting signals, which are produced by a multitransmitter configuration, to make scatterers located at different positions reflect independent waveforms from each other, and then to derive the target image based on the prior knowledge of this detecting signal spatial distribution. By constructing the mathematic model, the necessary conditions of the transmitting waveforms are analyzed for achieving radar coincidence imaging. A parameterized image-reconstruction algorithm is introduced to obtain high resolution for microwave radar systems. The effectiveness of this proposed imaging method is demonstrated via a set of simulations. Furthermore, the impacts of modeling error, noise, and waveform independence on the imaging performance are discussed in the experiments.

When tracking a target using phase-only signal returns, range ambiguities are a major issue. In this work, a look-up table between phase measurement space and target location space is constructed for phase measurement mapping. Solving such problems via solutions to Diophantine equations has been used to locate candidate locations. Here we show how such target location ambiguity can also be resolved over time when the underlying target is in motion and where issues of clutter are treated via a phase distribution discrimination method. That is, a probability density function of the ambiguous phase-only measurement that takes both sensor noise and target motion distributions into account is derived based on directional statistics. This approach to solving phase ambiguity under significant clutter conditions has promising results.