Clutter Statistics Research Articles

Narrow-Band Clutter Mitigation in Spectral Polarimetric Weather Radar

In this paper, a new clutter suppression method, named the moving double spectral linear depolarization ratio (MDsLDR) filter, is put forward to mitigate narrow-band clutter in weather radars. The narrow-band clutter observed in the Doppler domain includes: 1) stationary clutter such as ground clutter and 2) nonstationary clutter such as artifacts caused by the radar system itself or external sources. These artifacts are difficult to remove, because they are not confined to specific azimuth and range bins. Based on the difference of the spectral-polarization feature and the spectral continuity of precipitation and clutter, the MDsLDR filter can remove ground clutter, artifacts, and noise. The performance of the newly proposed filter is assessed by data collected by the Doppler-polarimetric IRCTR Drizzle Radar. Three precipitation cases are considered in this paper: moderate/light precipitation, convective precipitation with hook-echo signature, and light precipitation with severe artifact contamination. Furthermore, the implementation of the MDsDLR filter requires relatively low computation complexity, so that the MDsLDR filter can be operated in real time.

Search Tracker: Human-Derived Object Tracking in the Wild Through Large-Scale Search and Retrieval

Humans use context and scene knowledge to easily localize moving objects in conditions of complex illumination changes, scene clutter and occlusions. In this paper, we present a method to leverage human knowledge in the form of annotated video libraries in a novel search and retrieval based setting to track objects in unseen video sequences. For every video sequence, a document that represents motion information is generated. Documents of the unseen video are queried against the library at multiple scales to find videos with similar motion characteristics. This provides us with coarse localization of objects in the unseen video. We further adapt these retrieved object locations to the new video using an efficient warping scheme. The proposed method is validated on in-the-wild video surveillance datasets where we outperform state-of-the-art appearance-based trackers. We also introduce a new challenging dataset with complex object appearance changes.

An Information Theoretic Approach to Robust Constrained Code Design for MIMO Radars

The problem of transmit code design in multiple-input multiple-output (MIMO) radar systems is addressed in this paper. The problem is considered in both colocated and widely separated antenna radars in the presence of signal-dependent interference and target mobility. Due to complexity of exact expressions of the optimal detector in the MIMO radars, information theoretic criteria are employed as design metrics which results in a general form of nonconvex optimization problems. In order to tackle the problem, a novel technique based on the minorization-maximization algorithm is proposed and solutions are presented under practical constraints on the transmit code, namely energy constraint, peak-to-average-power ratio constraint, and similarity constraint. Furthermore, the devised method is extended to be robust against uncertainties of the clutter and interference statistics. Finally, numerical examples are used to show the performance of the proposed technique in different situations.

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System.

Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware.

Compressed sensing‐based ground MTI with clutter rejection scheme for synthetic aperture radar

This study investigates moving target indication (MTI) and velocity estimation for the single-channel synthetic aperture radar system. A four-dimensional grid is employed for location and velocity representation of any moving targets. Clutter terms spread among all location grid cells and a small number of velocity grid cells corresponding to small velocities. Since in practical situations, clutter spectra have a certain widening in frequency, it is favourable to cancel an arbitrarily chosen number of clutter bins about zero Doppler. In this study, a subspace-based clutter rejection method is proposed to cancel the stationary ground clutter in addition to clutter up to higher Doppler frequencies. That is, after suppressing the clutter successfully, the ground MTI problem becomes sparse and a compressive sensing reconstruction algorithm is applied to solve for the location and velocity of the moving target. To reduce the amount of data storage and computational load, some undersampling schemes are done based on the ability of compressive sensing to solve the underdetermined set of equations. Finally, it is shown how the proposed clutter cancellation method is important for the high data rate reduction case of undersampling.

Scheme of Parameter Estimation for Generalized Gamma Distribution and Its Application to Ship Detection in SAR Images

In the detection applications of synthetic aperture radar (SAR) data, a crucial problem is developing precise models for clutter statistics. Generalized gamma distribution ( $\text{G}\Gamma \text{D}$ ) has been widely applied in many fields of signal processing, and it has been demonstrated to be an appropriate model for describing the statistical behaviors of SAR sea clutter, wherein parameter estimation is a key issue for determining the practical application of $\text{G}\Gamma \text{D}$ . Work that contains three major aspects is performed in this paper. First, an approximate estimator for $\text{G}\Gamma \text{D}$ parameters based on the well-known “method-of-log-cumulants” is derived; a theoretical comparison between the approximate estimator and other known estimators is also presented. Second, based on this estimator, a scheme of parameter estimation is further given by comprehensively considering estimation precision, speed, and applicable conditions. The simulation results show that the presented scheme is fast and effective. Third, we assess the fitting performance of $\text{G}\Gamma \text{D}$ and the proposed scheme using real SAR sea clutter data, and compare the model with generalized-K distribution. The experiments on single-look complex and multilook-processing L-band ALOS-PALSAR and C-band RADARSAT-2 SAR data verify the effectiveness of the proposed scheme of $\text{G}\Gamma \text{D}$ parameter estimation. Moreover, several examples of ship detection in real SAR images testify to the usefulness of the proposed scheme in practical applications.

Chemical detection on surfaces by hyperspectral imaging

We have developed a method for using hyperspectral (HS) data to identify and locate chemical materials on arbitrary surfaces using the materials’ reflection or emission spectra that makes no prior modeling assumptions about the presence of pure pixels or the statistics of the background clutter and sensor noise. To our knowledge, this is the first time that surface detection without dependence on background information has been achieved. There are three main components to the method: (1) an HS unmixing algorithm based on the alternating direction method of multipliers that is applied over local subsets of the imaging to resolve the HS data into a set of linearly independent spectral and spatial components; (2) the fitting of those unmixing spectra to a set of candidate template spectra; and (3) a support vector machine classifier for chemical detection, identification, and location. The algorithm is illustrated on HS data collected by a Telops Hyper-Cam infrared camera on data resulting from the deposition of chemical agent simulants on various surfaces.

Single dataset method for spread‐Doppler clutter suppression in HF hybrid sky‐surface wave radar

In high-frequency hybrid sky-surface wave radars, the spread-Doppler clutter (SDC) compromise low-velocity target detection performance. Generally, adaptive filter methods achieve clutter suppression by estimating a clutter-plus-noise covariance matrix from target-free training data, which are typically obtained from neighbouring range bins. Under certain circumstances, the clutter statistics change significantly across adjacent bins. To deal with this problem, a single dataset algorithm is proposed to suppress the SDC without secondary data. Firstly, a spatial orthogonal projection matrix is introduced to block the target component in the cell under test. Afterwards, a localised processing method is derived, using previously constructed clutter samples, to suppress the SDC. According to the experimental results, the proposed method can achieve better clutter suppression performance based on real data.

CA-CFAR Adjustment Factor Correction with a priori Knowledge of the Clutter Distribution Shape Parameter

Oceanic and coastal radars operation is affected because the targets information is received mixed with and undesired contribution called sea clutter. Specifically, the popular CA-CFAR processor is incapable of maintaining its design false alarm probability when facing clutter with statistical variations. In opposition to the classic alternative suggesting the use of a fixed adjustment factor, the authors propose a modification of the CA- CFAR scheme where the factor is constantly corrected according on the background signal statistical changes. Mathematically translated as a variation in the shape parameter of the clutter distribution, the background signal changes were simulated through the Weibull, Log-Normal and K distributions, deriving expressions which allow choosing an appropriate factor for each possible statistical state. The investigation contributes to the improvement of radar detection by suggesting the application of an adaptive scheme which assumes the clutter shape parameter is known a priori. The offered mathematical expressions are valid for three false alarm probabilities and several windows sizes, covering also a wide range of clutter conditions.

A novel sequential algorithm for clutter and direct signal cancellation in passive bistatic radars

Cancellation of clutter and multipath is an important problem in passive bistatic radars. Some important recent algorithms such as the ECA, the SCA and the ECA-B project the received signals onto a subspace orthogonal to both clutter and pre-detected target subspaces. In this paper, we generalize the SCA algorithm and propose a novel sequential algorithm for clutter and multipath cancellation in the passive radars. This proposed sequential cancellation batch (SCB) algorithm has lower complexity and requires less memory than the mentioned methods. The SCB algorithm can be employed for static and non-static clutter cancellation. The proposed algorithm is evaluated by computer simulation under practical FM radio signals. Simulation results reveal that the SCB provides an admissible performance with lower computational complexity.

Robust SAR STAP via Kronecker decomposition

This paper proposes a spatiotemporal decomposition for the detection of moving targets in multi-antenna synthetic aperture radar (SAR). As a high-resolution radar imaging modality, SAR detects and localizes nonmoving targets accurately, giving it an advantage over lower-resolution ground-moving target indication (GMTI) radars. Moving target detection is more challenging due to target smearing and masking by clutter. Space-time adaptive processing (STAP) is often used to remove the stationary clutter and enhance the moving targets. In this work, it is shown that the performance of STAP can be improved by modeling the clutter covariance as a space versus time Kronecker product with low-rank factors. Based on this model, a low-rank Kronecker product covariance estimation algorithm is proposed, and a novel separable clutter cancelation filter based on the Kronecker covariance estimate is introduced. The proposed method provides orders of magnitude reduction in the required number of training samples as well as improved robustness to corruption of the training data. Simulation results and experiments using the Gotcha SAR GMTI challenge dataset are presented that confirm the advantages of our approach relative to existing techniques.

A New Robust Vital Sign Detection in Complex Environments Using Ultrawideband Radar

In this paper, we present a new algorithm for detection of respiratory movement of a person behind an obstacle by using ultrawideband (UWB) impulse radar. In this scenario, the most significant sign of being alive is the respiratory motions, which is hidden in parameters of the returned signal. This signal, whose statistical characteristics are generally nonstationary, is mixed with both stationary and nonstationary clutters and white and colored noise. In this paper, in an analytical way, the returned signal has been addressed by the Fourier series model with time-varying coefficients, as a fitting model. Then, based on the unconditional orthonormal representation of band-limited signals, a minimum mean square error estimator is introduced to determine the time-varying coefficients of the Fourier series. Using the new representation of the Fourier coefficients, a new approach is suggested that enables us to extract parameters of the respiration in very low signal-to-noise-and-clutter ratio (SNCR) conditions in the presence of both stationary and nonstationary clutters. Getting the most out of the estimator, this approach alleviates the problems associated with clutters and noise, without resorting to match filtering. Hence, a robust and blind respiratory-motion detection (RMD) from stationary or nonstationary received signal is obtained. By experimental data, we demonstrate the applicability of the new approach for the respiratory detection using UWB impulse radar in different aspects.

Coherent radar detection in Compound-Gaussian clutter: Clairvoyant detectors

This paper provides a historical and tutorial overview of coherent radar target detection in compound-Gaussian clutter and offers some new perspectives and avenues of research in this challenging area. It begins with a brief introduction that motivates the need to develop statistical models of non-Gaussian clutter and then reviews some of the physical ideas that led to modeling multivariate radar clutter statistics by the compound-Gaussian model. With this starting point, the paper then reviews a series of ideas that have been developed to describe clairvoyant detectors in such clutter. The term “clairvoyant” refers to the assumption that the properties of the clutter are assumed to be known. In a practical scenario, this assumption does not hold and adaptive techniques are needed to estimate clutter properties and implement the detector. Such techniques are guided however by the appropriate clairvoyant detector structures and hence it is proper to start by studying these detectors. As part of this review, the paper offers new ways of looking at this problem that suggest new research topics. This review is limited to the problem of clairvoyant detection in which the relevant properties of the clutter are assumed to be known. Adaptive detection in compound- Gaussian clutter will be the topic of a subsequent tutorial that the authors are preparing.

Asymptotic Performance of the Geometric Mean Detector in Pareto Distributed Clutter

Several independent investigations have demonstrated the validity of the Pareto distribution as a model for X-band high-resolution maritime surveillance radar intensity clutter returns. This has included validation of it for data collected from fixed costal surveillance radars, as well as airborne maritime surveillance radars. Consequently, there has been a steady development of radar detection schemes under such a clutter model assumption, both from a coherent and noncoherent detection perspective. One of the major initiatives at the Australian Defence Science and Technology Group has been to investigate the development of sliding window constant false alarm rate detectors for operation in a Pareto distributed clutter. A transformation approach has provided this capability, and one of the observations has been that a geometric mean detector, derived via this approach, tends to perform ideally when the number of clutter statistics in the clutter range profile is large. This letter examines this phenomenon and provides a mathematical explanation for it.

Comparison of clutter rejection techniques for measurement of small displacements of body surface using radar

To measure vital signs using Doppler radar, a common approach involves the use of time-varying echo phase. To acquire these measurements, clutter rejection is necessary because clutter power is often larger than echo power. To reject static clutter, several techniques have been proposed that assume relatively large phase rotation angles because many studies assume the measurement of the upper torso, where displacement is mainly caused by respiration. However, signals from other parts of the human body are known to have smaller displacements that exhibit small phase rotation angles, which make clutter rejection more difficult. Three clutter rejection techniques for measuring small displacements are compared and their performances are investigated. Using numerical analysis, one method is demonstrated to be the most effective, even for small displacements in noisy data. The best method successfully estimates the centre with an error of −13.4 and −24.0 dB, with a signal-to-noise ratio of 10 and 40 dB and the range of phase rotation angles of 90° and 15°.

Six Object Tracking Algorithms: A Comparative Study

Objectives: To compare five different objects tracking algorithms performance wise with the proposed algorithm and to find out the best one among them for tracking of an object in occlusion and Background clutter condition i.e. when background contains target features. Methods/Analysis: In the proposed algorithm, the object’s position is obtained by Mean Shift tracking and then the prediction of object’s position is done through Kalman Filter. The Proposed method has Kalman Filter which consists of an adaptive system matrix and adaptive process error covariance and measurement error covariance matrices respectively. Adaptive system matrix of Kalman Filter is getting updated online depending on the quality of observation by Mean Shift algorithm and adaptive process and measurement noise covariance matrices are getting updated according to the variation in Bhattacharya Coefficient respectively. Findings: Proposed algorithm has a maximum value (0.2177 and 0.4821) of tracking efficiency metric i.e. Bhattacharya Coefficient in both video dataset among all the algorithms and it is taking less execution time (0.002761 sec and 0.005431 sec) than other Kalman filter based tracking algorithms. Applications/Improvements: Proposed algorithm is able to track the target in occlusion and background variation condition with more efficiency and less execution time than rest of the algorithms.

A Dataset for Improved RGBD-Based Object Detection and Pose Estimation for Warehouse Pick-and-Place

An important logistics application of robotics involves manipulators that pick-and-place objects placed in warehouse shelves. A critical aspect of this task corresponds to detecting the pose of a known object in the shelf using visual data. Solving this problem can be assisted by the use of an RGBD sensor, which also provides depth information beyond visual data. Nevertheless, it remains a challenging problem since multiple issues need to be addressed, such as low illumination inside shelves, clutter, texture-less and reflective objects as well as the limitations of depth sensors. This letter provides a new rich dataset for advancing the state-of-the-art in RGBD-based 3D object pose estimation, which is focused on the challenges that arise when solving warehouse pick-and-place tasks. The publicly available dataset includes thousands of images and corresponding ground truth data for the objects used during the first Amazon Picking Challenge at different poses and clutter conditions. Each image is accompanied with ground truth information to assist in the evaluation of algorithms for object detection. To show the utility of the dataset, a recent algorithm for RGBD-based pose estimation is evaluated in this letter. Given the measured performance of the algorithm on the dataset, this letter shows how it is possible to devise modifications and improvements to increase the accuracy of pose estimation algorithms. This process can be easily applied to a variety of different methodologies for object pose detection and improve performance in the domain of warehouse pick-and-place.

Detection of Moving Targets by Refocusing for Airborne CSSAR

Due to the advantage of short revisit time, airborne circular stripmap synthetic aperture radar (CSSAR) is an attractive tool for air-to-ground surveillance and reconnaissance. This paper deals with the issue of ground moving-target detection with an airborne CSSAR and proposes a new autofocus-based detection algorithm. The prominent features of the proposed algorithm are the incorporation of the Doppler ambiguity information into the autofocus-based moving-target detection and the ability to solve the problem of large range cell migration, which affects most of autofocus algorithms. The proposed algorithm can detect not only the targets whose azimuth chirp rates are different from that of the static clutter background but also the targets whose Doppler ambiguities are different from that of the static clutter background. Numerical results show that the proposed algorithm works well in both homogeneous and heterogeneous clutter backgrounds.

Doppler radar microphone with logarithmic square‐law detector

A Doppler radar based microphone is similar to an acoustic microphone except that it detects vibrations rather than sound. A microphone with a logarithmic square-law detector for monitoring honeybees is described. The performance of a logarithmic square-law detector is compared with a more conventional zero-intermediate frequency (IF), direct down conversion receiver. It is shown that unavoidable coupling between the transmitter and receiver as well as stationary clutter have the same effect in the square-law receiver as the local oscillator signal and mixer have in the direct down conversion receiver. The theoretical output of either configuration is formally the same. Both receiver designs were implemented in a low-cost, low-power radar microphone operating in the unlicensed 5.8 GHz industrial, scientific and medical band. The use of a logarithmic square-law detector resulted in a significant simplification of the radar microphone design as well as a 10 dB improvement in signal-to-noise ratio.

A Sea-Sky Line Detection Method for Unmanned Surface Vehicles Based on Gradient Saliency.

Special features in real marine environments such as cloud clutter, sea glint and weather conditions always result in various kinds of interference in optical images, which make it very difficult for unmanned surface vehicles (USVs) to detect the sea-sky line (SSL) accurately. To solve this problem a saliency-based SSL detection method is proposed. Through the computation of gradient saliency the line features of SSL are enhanced effectively, while other interference factors are relatively suppressed, and line support regions are obtained by a region growing method on gradient orientation. The SSL identification is achieved according to region contrast, line segment length and orientation features, and optimal state estimation of SSL detection is implemented by introducing a cubature Kalman filter (CKF). In the end, the proposed method is tested on a benchmark dataset from the “XL” USV in a real marine environment, and the experimental results demonstrate that the proposed method is significantly superior to other state-of-the-art methods in terms of accuracy rate and real-time performance, and its accuracy and stability are effectively improved by the CKF.