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Presents the table of contents for this issue of this publication. [ABSTRACT FROM PUBLISHER]

In many practical scenarios with multipath propagation, one target may generate multiple detections in one scan. Proper use of multipath-induced measurements can improve the detection of very low observable (VLO) targets. In this paper, a true multitarget tracker, the joint multipath maximum likelihood probabilistic multihypothesis tracker (JMP-ML-PMHT) is proposed to address this problem. The standard ML-PMHT is extended to incorporate multipath detections and jointly track multiple VLO targets. The Cramer–Rao lower bound with multipath detections is derived. Simulation results with an over-the-horizon-radar scenario show that the JMP-ML-PMHT can detect and track multiple VLO targets by effectively utilizing the information in multipath measurements. [ABSTRACT FROM AUTHOR]

This paper devises a new constant false alarm rate (CFAR) detection scheme to deal with the problem of radar target detection in heterogeneous environment. The proposed scheme, called “clustering-CFAR detector,” is data dependent and composed of three stages: an adaptive clustering procedure that, exploiting the recorded measurements of the clutter environment, divides the detection area into different classes to provide auxiliary information, a dynamic reference cell selector that chooses appropriate secondary data according to the classes, and a conventional CFAR processor to make the final decision about the target presence. The performance of “clustering-CFAR detector” is analyzed by computer simulation and public radar measured data (IPIX data and MSTAR data), and compared with existing CFAR detectors. The results show that the new detector achieves a better performance in the aspects of terrain classification, control of false alarm points, and probability of detection. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of coherent radar detection of fast-moving targets in a high-range resolution mode. In particular, we are focusing on the spiky clutter modeled as a compound Gaussian process with rapidly varying power along range. Additionally, a fast-moving target of interest has a few range cells migration within the coherent processing interval. Two coherent CFAR detectors are proposed taking into account target migration and highly inhomogeneous clutter. Both detectors involve solution of a transcendental equation, carried out numerically in a few iterations. The performance evaluation is addressed by numerical simulations and it shows a significant improvement in detection of fast-moving targets in inhomogeneous heavy tailed radar clutter. [ABSTRACT FROM AUTHOR]

This paper proposes a novel joint probabilistic data association (JPDA) filter for joint target tracking and track maintenance under unknown detection probability and clutter rate. The proposed algorithm consists of two main parts: (1) the standard JPDA filter with a Poisson point process birth model for multi-object state estimation; and (2) a multi-Bernoulli filter for detection probability and clutter rate estimation. The performance of the proposed JPDA filter is evaluated through empirical tests. The results of the empirical tests show that the proposed JPDA filter has comparable performance with ideal JPDA that is assumed to have perfect knowledge of detection probability and clutter rate. Therefore, the algorithm developed is practical and could be implemented in a wide range of applications. [ABSTRACT FROM AUTHOR]

Traditional constant false alarm rate (CFAR) detectors suffer probability of detection (PD) degradation influenced by the outliers such as interfering ship targets, side lobes, and ghosts, especially in crowded harbors and busy shipping lines. In this paper, a new two-parameter CFAR detector based on adaptively truncated clutter statistics (TS-LNCFAR) is proposed. The new two-parameter CFAR detector uses log-normal as the statistical model; by adaptive-threshold-based clutter truncation in the background window, the outliers are removed from the clutter samples, while the real clutter is preserved to the largest degree. The log-normal model is accurately built using the truncated clutter statistics through the maximum-likelihood estimator. Compared with traditional CFAR detectors, the parameter estimation is more accurate, and TS-LNCFAR has a better false alarm regulation property and a high PD in a multiple-target environment. Furthermore, the parameter estimation and threshold calculation do not need iterative numerical calculation, and TS-LNCFAR has a high computational efficiency. The superiority of the proposed TS-LNCFAR detector is validated on the multilook Envisat-ASAR and TerraSAR-X data. [ABSTRACT FROM PUBLISHER]

We study detection and imaging of small reflectors in heavy clutter, using an array of transducers that emits and receives sound waves. Heavy clutter means that multiple scattering of the waves in the heterogeneous host medium is strong and overwhelms the arrivals from the small reflectors. Building on the adaptive time-frequency filter of [Borcea et al, SIIMS 2011;4(3)], we propose a robust method for detecting the direction of arrival of the direct echoes from the small reflectors, and suppressing the unwanted clutter backscatter. This improves the resolution of imaging. We illustrate the performance of the method with realistic numerical simulations in a non-destructive testing setup. [ABSTRACT FROM AUTHOR]

Ultrafast plane wave microvessel imaging significantly improves ultrasound Doppler sensitivity by increasing the number of Doppler ensembles that can be collected within a short period of time. The rich spatiotemporal plane wave data also enable more robust clutter filtering based on singular value decomposition. However, due to the lack of transmit focusing, plane wave microvessel imaging is very susceptible to noise. This paper was designed to: 1) study the relationship between ultrasound system noise (primarily time gain compensation induced) and microvessel blood flow signal and 2) propose an adaptive and computationally cost-effective noise equalization method that is independent of hardware or software imaging settings to improve microvessel image quality. [ABSTRACT FROM AUTHOR]

In this letter, we address adaptive radar detection of point-like targets in Gaussian clutter with an unknown covariance matrix. To this end, we first exploit the symmetrically structured power spectral density of the clutter to transfer data from the complex to the real domain. Then, the spillover of target energy is incorporated into the design criteria to come up with two architectures capable of guaranteeing improved detection performances and range estimation. The performance assessments, conducted on both simulated data and real recorded datasets, demonstrate the effectiveness of the newly proposed detectors compared with the state-of-the-art counterparts, which ignore either the clutter spectral symmetry or the energy spillover. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel framework for object detection and recognition in cluttered images, given a single hand-drawn example as model. Compared with previous work, our contribution is threefold. (1) Three preprocessing procedures are proposed to reduce the number of irrelevant edge fragments that are often generated during edge detection in cluttered real images. (2) A novel shape descriptor is introduced for conducting partial matching between edge fragments and model contours. (3) An efficient search strategy is adopted to identify the location of target object hypotheses. In the hypotheses verification stage, an appearance-based (support vector machine on pyramid histogram of oriented gradients feature) method is adopted to verify the hypothesis, identify the object, and refine its location. We do extensive experiments on several benchmark datasets including ETHZ shape classes, INRIA horses, Weizmann horses, and the two classes (anchors and cups) from Caltech 101. Experimental results show that the proposed method can significantly improve the accuracy of object detection. Comparisons with other recent shape-based methods further demonstrate the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Salient object detection is a fundamental problem in computer vision. Existing methods using only low-level features failed to uniformly highlight the salient object regions. In order to combine high-level saliency priors and low-level appearance cues, we propose a novel Background Prior based Salient detection method (BPS) for high-quality salient object detection. Different from other background prior based methods, a background estimation is added before performing saliency detection. We utilize the distribution of bounding boxes generated by a generic object proposal method to obtain background information. Three background priors are mainly considered to model the saliency, namely background connectivity prior , background contrast prior and spatial distribution prior , allowing the proposed method to highlight the salient object as a whole and suppress background clutters. Experiments conducted on two benchmark datasets validate that our method outperforms 11 state-of-the-art methods, while being more efficient than most leading methods. [ABSTRACT FROM AUTHOR]

Conventional Doppler ultrasound is useful for visualizing fast blood flow in large resolvable vessels. However, frame rate and tissue clutter caused by movement of the patient or sonographer make visualizing slow flow with ultrasound difficult. Patient and sonographer motion causes spectral broadening of the clutter signal, which limits ultrasound’s sensitivity to velocities greater than 5–10 mm/s for typical clinical imaging frequencies. To address this, we propose a clutter filtering technique that may increase the sensitivity of Doppler measurements to at least as low as 0.52 mm/s. The proposed technique uses plane wave imaging and an adaptive frequency and amplitude demodulation scheme to decrease the bandwidth of tissue clutter. To test the performance of the adaptive demodulation method at suppressing tissue clutter bandwidths due to sonographer hand motion alone, six volunteer subjects acquired data from a stationary phantom. Additionally, to test in vivo feasibility, arterial occlusion and muscle contraction studies were performed to assess the efficiency of the proposed filter at preserving signals from blood velocities 2 mm/s or greater at a 7.8 MHz center frequency. The hand motion study resulted in initial average bandwidths of 175 Hz (8.60mm/s), which were decreased to 10.5 Hz (0.52 mm/s) at −60 dB using our approach. The in vivo power Doppler studies resulted in 4.73 dB and 4.80 dB dynamic ranges of the blood flow with the proposed filter and 0.15 dB and 0.16 dB dynamic ranges of the blood flow with a conventional 50 Hz high-pass filter for the occlusion and contraction studies, respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

The interference characteristics of cross-correlated broadband fields received by a single hydrophone deployed near the bottom at two different ranges are analyzed in this paper. The ray theory is used to derive the interference pattern, which is a combination of two kinds of interference phenomena. One kind of interference period contains the information of radial source velocity, and the other one is related to source depth. The source motion parameters, including the range and time of closet point of approach (CPA), constant source velocity and source depth, can be estimated by computing the Fourier transform of cross-correlated broadband fields when the existence of a CPA is apparent in the data. If the CPA is not evident, only the radial source velocity and the discrimination of surface versus submerged source can be provided. Note that the proposed method is a broadband technique. Experimental results confirm this single hydrophone estimation method of radial source velocity and source depth. [ABSTRACT FROM AUTHOR]

The detection and discrimination of infrared small dim targets is a challenge in automatic target recognition (ATR), because there is no salient information of size, shape and texture. Many researchers focus on mining more discriminative information of targets in temporal-spatial. However, such information may not be available with the change of imaging environments, and the targets size and intensity keep changing in different imaging distance. So in this paper, we propose a novel research scheme using density-based clustering and backtracking strategy. In this scheme, the speeded up robust feature (SURF) detector is applied to capture candidate targets in single frame at first. And then, these points are mapped into one frame, so that target traces form a local aggregation pattern. In order to isolate the targets from noises, a newly proposed density-based clustering algorithm, fast search and find of density peak (FSFDP for short), is employed to cluster targets by the spatial intensive distribution. Two important factors of the algorithm, percent and γ , are exploited fully to determine the clustering scale automatically, so as to extract the trace with highest clutter suppression ratio. And at the final step, a backtracking algorithm is designed to detect and discriminate target trace as well as to eliminate clutter. The consistence and continuity of the short-time target trajectory in temporal-spatial is incorporated into the bounding function to speed up the pruning. Compared with several state-of-arts methods, our algorithm is more effective for the dim targets with lower signal-to clutter ratio ( SCR ). Furthermore, it avoids constructing the candidate target trajectory searching space, so its time complexity is limited to a polynomial level. The extensive experimental results show that it has superior performance in probability of detection ( Pd ) and false alarm suppressing rate aiming at variety of complex backgrounds. [ABSTRACT FROM AUTHOR]

In Part I of this paper, we detected elements blocked by ribs during simulated and in vivo transcostal liver scans, and we turned those elements OFF to compensate for the loss in visibility of liver vasculature. Here, we apply blocked-element detection and adaptive compensation to large synthetic-aperture (SA) data collected through rib samples ex vivo, in order to reduce near-field clutter and to recover lateral resolution. To construct large synthetic transmit and receive apertures, we collected the individual-channel signals from a fully sampled matrix array at multiple and known array locations across the tissue samples. The blocked elements in SAs were detected using the method presented in Part I and retroactively turned OFF, while the subapertures covering intercostal spaces were either compounded, or coherently summed using uniform and adaptive element-weighting schemes. Turning OFF the blocked elements reduced the reverberation clutter by 5 dB on average. Adaptive weighing of the nonblocked elements to rescale the attenuated spatial frequencies reduced sidelobe levels by up to 5 dB for the transcostal acquisitions, and demonstrated a potential to restore lateral resolution to the nonblocked levels. In addition, the arrival-time surfaces were reconstructed to estimate the aberration from intercostal spaces and to offer further means to compensate for the loss of focus quality in transthoracic imaging. [ABSTRACT FROM AUTHOR]

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 CACFAR 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. [ABSTRACT FROM AUTHOR]

A clutter is a family of mutually incomparable sets. The set of circuits of a matroid, its set of bases, and its set of hyperplanes are examples of clutters arising from matroids. In this paper we address the question of determining which are the matroidal clutters that best approximate an arbitrary clutter Λ. For this, we first define two orders under which to compare clutters, which give a total of four possibilities for approximating Λ (i.e., above or below with respect to each order); in fact, we actually consider the problem of approximating Λ with clutters from any collection of clutters Σ, not necessarily arising from matroids. We show that, under some mild conditions, there is a finite non-empty set of clutters from Σ that are the closest to Λ and, moreover, that Λ is uniquely determined by them, in the sense that it can be recovered using a suitable clutter operation. We then particularize these results to the case where Σ is a collection of matroidal clutters and give algorithmic procedures to compute these clutters. [ABSTRACT FROM AUTHOR]

In radar antennas, asymmetric side lobes are useful, where undesired signals such as noise and ground clutter should be minimized. Also, for practical implementation, the feeding network of such antennas should be efficiently designed. In this paper, a simple analytical method for synthesizing asymmetric side lobe pattern with a wide-angle steered null in the nonuniformly excited linear arrays is presented. In this method, the difference in the side lobe levels on both sides of the main beam is achieved by varying just the phase excitations of the two-edge elements. The major novelty of this paper lies in the fact that the required asymmetric side lobe pattern can be achieved by changing a single phase shifter resulting in a simple feeding network. [ABSTRACT FROM AUTHOR]

In recent years, automatic or semiautomatic processing and analysis systems for a large amount of social listening data have been introduced and used in many industries. Honda Motor Co., Ltd. is also collecting and analyzing voice of customers from much type of media such as SNS using automatic Social Listening System. And verifying whether corporate images and brands are appropriately communicated or not every day. This verification is also used to find symptoms of risk that may be recalled. On the other hand, we found that there were many copied sentences which were delivered from us to society in collected information as voice and opinion of customers. In this case, if these collected sentences are automatically processed as voice of customers using a normal language processing algorithm, we should have a risk to get excessively more positive result than actual. This is because the information delivered from the company like announcement of a new product etc., always includes many positive expressions. And it has been confirmed that the distributor’s advertisement and a large amount of retweets follows it, also causes the same risk for the same reasons. It’s hard to say that we are correctly measuring the voice of customers. Based on the above situations, in this paper, firstly, we named the phenomenon as “Echo Phenomenon”, which incorrectly recognizes delivered information from us as voice of customers. And present it as a problem. Secondly, we propose a simple method to avoid this Echo Phenomenon problem without damaging useful information as much as possible, and show examples of application and its effect. [ABSTRACT FROM AUTHOR]

Maximum likelihood algorithms for estimating parameters of a correlation matrix of clutter for adaptive allocation of signals of moving targets in multifrequency coherent pulse radar-tracking systems are synthesized. Schematic diagrams of the corresponding measuring instruments are provided. [ABSTRACT FROM AUTHOR]

The refractivity from clutter (RFC) technique has been proved to be an effective way to estimate atmospheric duct structure. An important issue for RFC is how to make the estimate more robust, especially in range-dependent ducting conditions. Traditionally, statistical inversion methods need a large number of forward propagation model runs to obtain an acceptable result. Especially when the parameter search space is multidimensional, these methods are prone to being trapped into local optimal solutions. Recently published results (Zhao and Huang) indicate that the adjoint parabolic equation (PE) method holds promise for real-time estimation of one-dimensional refractive index structure from radar sea clutter returns. This paper is aimed at extending the adjoint PE method to range-dependent evaporation duct cases, with a log-linear relationship describing duct structures. Numerical simulations are used to test the performance of this method and the results are compared with that retrieved using a genetic algorithm. Both noise-free and 3-dB additive Gaussian noise clutter simulations are considered, as well as linearly and nonlinearly varying duct height with range. [ABSTRACT FROM AUTHOR]

Volumetric flow rate estimation is an important ultrasound medical imaging modality that is used for diagnosing cardiovascular diseases. Flow rates are obtained by integrating velocity estimates over a cross-sectional plane. Speckle tracking is a promising approach that overcomes the angle dependency of traditional Doppler methods, but suffers from poor lateral resolution. Recent work improves lateral velocity estimation accuracy by reconstructing a synthetic lateral phase (SLP) signal. However, the estimation accuracy of such approaches is compromised by the presence of clutter. Eigen-based clutter filtering has been shown to be effective in removing the clutter signal; but it is computationally expensive, precluding its use at high volume rates. In this paper, we propose low-complexity schemes for both velocity estimation and clutter filtering. We use a two-tiered motion estimation scheme to combine the low complexity sum-of-absolute-difference and SLP methods to achieve subpixel lateral accuracy. We reduce the complexity of eigen-based clutter filtering by processing in subgroups and replacing singular value decomposition with less compute-intensive power iteration and subspace iteration methods. Finally, to improve flow rate estimation accuracy, we use kernel power weighting when integrating the velocity estimates. We evaluate our method for fast- and slow-moving clutter for beam-to-flow angles of 90° and 60° using Field II simulations, demonstrating high estimation accuracy across scenarios. For instance, for a beam-to-flow angle of 90° and fast-moving clutter, our estimation method provides a bias of −8.8% and standard deviation of 3.1% relative to the actual flow rate. [ABSTRACT FROM PUBLISHER]

The detection of randomly distributed extended multi-targets under various circumstances is of special interest. An improved plot fusion method is proposed in this work. The proposed plot fusion method is based on a multi-contour tracking and region growing algorithm. The proposed method is applied to the DP-TBD algorithm for extended targets in the K-distributed sea clutter. Models and shortcomings of existing methods are presented. The principle of the DP-TBD algorithm for extended targets and implementation of the improved plot fusion method are explained in detail. In the simulation, the methods are applied to almost all situations, close trajectories, crossing trajectories, small targets, weak targets and targets in a high clutter region. Comparison with existing methods demonstrates that the proposed method is practical and superior to the existing methods. [ABSTRACT FROM AUTHOR]

In 1973, Goldstein introduced his log-t detector, which attained the constant false alarm rate property for a class of clutter models including the lognormal and Weibull distributions. This paper shows that under the assumption of Pareto-distributed clutter, false alarm regulation can also be attained. A modification of the log- t detector is shown to enhance its performance in terms of managing interference. [ABSTRACT FROM PUBLISHER]

1 Introduce importance sampling to optimize original P 2 quantile update algorithm. 2 Enhance the efficiency of statistical method in computing radar detector thresholds. 3 Improve the evaluation accuracy of low false alarm probability. 4 Be suitable for complex detector schemes and detection environment. A threshold setting and false alarm probability evaluation method is proposed for radar detectors for which an analytical relationship between thresholds and false alarm probabilities is impossible or difficult to obtain due to complicated detection schemes or non-Gaussian noise and clutter. Importance sampling is performed to increase the sampling efficiency of low-probability samples. Then, the weighted piecewise parabolic (P 2) interpolation method is used to rapidly plot the relationship curve between the threshold and the false alarm of the detector, with a low storage requirement and high accuracy. The proposed method effectively enhances the detector threshold computing scale and addresses the statistical verification of the false alarm probability for radar detectors with a low false alarm rate. [ABSTRACT FROM AUTHOR]

Renal Cell Cancer (RCC) appears asymptomatic at its early stage until progression; therefore, most RCCs are diagnosed incidentally. The current gold standard for RCC diagnostics is a contrast-enhanced CT-scan; however, more cost-effective and practical diagnostic imaging tools are still being researched. Recently, contrast-ultrasound dispersion imaging (CUDI) has shown promise for prostate cancer localization by the extraction of quantitative perfusion and dispersion features based on contrast-enhanced ultrasound (CEUS) acquisitions. CUDI builds on the physiological process of tumor-driven angiogenesis, leading to a complex and irregular microvascular architecture that produces changes in the local blood perfusion and contrast-dispersion kinetics. Here, we investigated the feasibility of CUDI for detecting primary RCC. At the Amsterdam University Medical Centers, nine patients underwent two-minute CEUS recordings using a Philips iU22 scanner (Philips Healthcare, WA) under approval granted by the local ethics committee. During the scanning, the patient was under anesthesia and apnea to mitigate the impact of respiratory motion. To optimize the image quality, we tested different settings on the first 4 patients, and the remaining 5 acquisitions were scanned with the optimal settings and used for the following CUDI analysis. Prior to the CUDI analysis, singular-value-decomposition-based pre-processing was implemented to remove residual tissue clutter and noise. The pre-processed data was then analyzed by two different CUDI techniques, namely time-intensity curve (TIC) fitting and spatiotemporal similarity analysis. Both approaches aim at extracting dispersion parameters that reflect the underlying microvascular architecture. Tumor and parenchyma regions were delineated by two urologists in consensus, based on the corresponding CT scans. Pixel-based classification was then performed by the obtained CUDI parameters and the area under the receiver-operating-characteristic curve (AUC) was calculated. All the AUC values obtained by the spatiotemporal similarity analysis in the 5-patient dataset were higher than 0.6, and the highest AUC=0.96 was obtained for an individual patient. Moreover, the spatiotemporal similarity analysis outperformed the TIC fitting analysis for RCC detection. Our preliminary results show the potential of CUDI for solid RCC localization. However, further validation with an extended dataset and cross validation is required. [ABSTRACT FROM AUTHOR]

• We propose four detectors for point-like target detection in clutter and noise cover pulse jamming. • The proposed detectors have the CFAR property. • They can improve the detection performance compared with the existing ones. This paper deals with the adaptive detection of the point-like target under clutter and noise cover pulse (NCP) jamming. In the design stage, we assume that the jamming is orthogonal to the signal subspace. Simultaneously, two training datasets are considered available. According to generalized likelihood ratio test (GLRT), Wald, and Rao criteria, NCP-GLRT, NCP-Wald, and NCP-Rao detectors are designed respectively. Then, considering the degraded detection performance of the NCP-Rao, a modified detector called NCP-MRao is proposed. Moreover, the performance of the newly presented detectors is analyzed, and the probability of false alarm and the detection probability for the NCP-GLRT are derived. Finally, numerical simulation results show that compared with the analogous counterparts, the proposed detector can improve detection performance. [ABSTRACT FROM AUTHOR]

The Pareto distribution has been validated as a suitable model for X-band maritime surveillance radar clutter returns, and consequently there has been much interest in developing radar detection algorithms under such a clutter model assumption. Recent research has shown that it is possible to apply a transformation approach to adapt the traditional constant false alarm rate (CFAR) detectors, designed to operate in exponentially distributed clutter, to the Pareto setting. However, it was found that this approach resulted in the decision rule requiring a priori knowledge of the Pareto scale parameter. It is shown here that this shortcoming can be rectified by application of a complete sufficient statistic to the transformed detector. Consequently, new decision rules are derived and it is shown that they not only achieve the CFAR property but in some instances can improve the performance of the decision rules from which they are derived. [ABSTRACT FROM AUTHOR]

One of the major challenges in array-based medical ultrasound imaging is the image quality degradation caused by sidelobes and off-axis clutter, which is an inherent limitation of the conventional delay-and-sum (DAS) beamforming operating on a finite aperture. Ultrasound image quality is further degraded in imaging applications involving strong tissue attenuation and/or low transmit power. In order to effectively suppress acoustic clutter from off-axis targets and random noise in a robust manner, we introduce in this paper a new adaptive filtering technique called frequency-space (F-X) prediction filtering or FXPF, which was first developed in seismic imaging for random noise attenuation. Seismologists developed FXPF based on the fact that linear and quasilinear events or wavefronts in the time-space (T-X) domain are manifested as a superposition of harmonics in the frequency-space (F-X) domain, which can be predicted using an auto-regressive (AR) model. We describe the FXPF technique as a spectral estimation or a direction-of-arrival problem, and explain why adaptation of this technique into medical ultrasound imaging is beneficial. We apply our new technique to simulated and tissue-mimicking phantom data. Our results demonstrate that FXPF achieves CNR improvements of 26% in simulated noise-free anechoic cyst, 109% in simulated anechoic cyst contaminated with random noise of 15 dB SNR, and 93% for experimental anechoic cyst from a custom-made tissue-mimicking phantom. Our findings suggest that FXPF is an effective technique to enhance ultrasound image contrast and has potential to improve the visualization of clinically important anatomical structures and diagnosis of diseased conditions. [ABSTRACT FROM AUTHOR]

Dim and small target detection in complex background is considered a difficult and challenging problem. Conventional algorithms using the local difference/mutation possibly produce high missed or mistaken detection rates. In this paper, we propose an effective algorithm for detecting dim and small infrared targets. In order to synchronously enhance targets and suppress complex background clutters, we adopt an adaptive entropy-based window selection technique to construct a novel local difference measure (LDM) map of an input image, which measures the dissimilarity between the current region and its neighboring ones. In this way, the window size can be adaptively regulated according to local statistical properties. Compared with the original image, the LDM map has less background clutters and noise residual. This guarantees the lower false alarm rates under the same probability of detection. Subsequently, a simple threshold is used to segment the target. More than 600 dim and small infrared target images against different complex and noisy backgrounds were utilized to validate the detection performance of the proposed approach. Extensive experimental results demonstrate that the proposed method not only works more stably for different target movements and signal-to-clutter ratio values, but also has a better performance compared with classical baseline methods. The evaluation results suggest that the proposed method is simple and effective with regard to detection accuracy. [ABSTRACT FROM AUTHOR]

The Sequential Monte Carlo (SMC) implementation for the probability hypothesis density (PHD) filter, referred to as the SMC-PHD filter, is a good candidate for multi-target tracking (MTT) problems. It recursively propagates the weighted particle set that approximates the multi-target posterior density. In this paper, we propose an improved SMC-PHD filter for MTT called the particle-gating SMC-PHD filter. First, a robust gating based on particles propagated from a previous time period is proposed to select the observations of survival targets. Second, a sigma-nearest-gating is proposed to accurately select the observations of new targets. By employing only the observations obtained by the above algorithms to update the state estimations, the overall processing speed of the filter is significantly improved. In addition, a softening factor is suggested to lower the average number of clutters in the updater. This provides more accurate estimation compared with the basic SMC-PHD filter. Finally, the respective real-time and tracking performances of the proposed SMC-PHD filter are verified by the simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sparsity-based direct data domain space-time adaptive processing (D3-STAP) algorithm for airborne radar that considers the intrinsic clutter motion (ICM). The proposed D3-STAP scheme models the received returns in the presence of ICM as a sparse measurement model. Then, we derive the principle of the sparsity-based D3-STAP that uses the focal underdetermined system solution (FOCUSS) method. The proposed D3-STAP algorithm estimates the clutter covariance matrix by a Hadamard product of the covariance matrix taper (CMT) and the clutter covariance matrix estimate with the FOCUSS technique. In addition, we develop a CMT adaptation approach for the proposed D3-STAP algorithm to automatically select the best CMT. Simulation results show that the proposed algorithm outperforms the existing D3-STAP using the least-squares technique and the sparsity-based D3-STAP algorithm without CMT. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of adaptive detection for distributed targets embedded in Gaussian disturbance without secondary data. We suppose that some a-priori spectral models for the interference in the cells under test and a lower bound on the power spectral density of the white disturbance term are available. First, we propose an approximate estimation algorithm for the unknown parameters under both hypotheses. Then, we propose a generalized likelihood ratio test that employs the approximate estimates. Finally, we evaluate the performance of the proposed detector under Gaussian disturbance and verify its advantage to some existing techniques. [ABSTRACT FROM AUTHOR]

The article presents methods that help in the elimination of mutual clutter as well as the consequences of two FM sounding signal sonars operating in the same body of water and frequency band. An in-depth analysis of mutual clutter was carried out. The effects of sounding signal differentiation were determined, as was the Doppler effect on mutual clutter suppression. One of the methods analysed is of particular interest in a situation in which collaborating sonars are operating in opposite frequency modulation directions. This method is effective for both linear and hyperbolic frequency modulations. A formula was derived, identifying exactly how much quantities of clutter may be lessened. The work included comprehensive computer simulations and measurements as well as tests in real-life conditions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for both 3-D object detection and six-degree-of-freedom (6-DoF) pose estimation based on smooth 3-D surface segments and their visual signatures in cluttered scenes with objects partially occluded. It is an appearance-based approach that does not require precise geometric models of the objects. We introduce a robust and flexible strategy to build object models based on segmented smooth surfaces of RGB-D images. We next introduce a strategy to detect objects and estimate their 6-DoF poses from a single image by taking full advantage of the surface-based object models. Our approach can reconstruct objects in cluttered scenes from a single view, where different objects or multiple instances of the same object can be stacked together or physically attached and occlude one another. It detects objects and estimates their 6-DoF poses even if the objects have transparent regions. Experimental results and comparison with related work demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In radar and sonar networks, heavy clutter and noise have generated strong impairments to information transmission and processing. Coding techniques have been widely used to cope with various channel noises and thereby to improve transmission performance. In the study, we investigate a novel cascaded coding scheme for systems operating in adverse electromagnetic environments, e.g., underwater acoustic communications (UAC), where the impulsive noise will be inevitable and the bit error ratio (BER) of receiver will be deteriorated greatly. Our cascaded encoding scheme employs the polar code as inner code while the Luby transform (LT) code as outer code. Inspired on a novel conception of channel polarization, the polar code uses a group of idea sub-channels to carry the useful information, while let other bad sub-channels bear no information. By cascading the outer LT code which accomplishes some parity check to the input of inner code, the performance of inner polar code is improved, as the decoding of polar code relies on a successive cancelation technique which is relatively sensitive to initial input. Error correcting performance of the new cascaded code is studied under impulsive noise characterized by a Middleton Class-A model. Simulations validate the proposed cascaded coding scheme. Compared with the popular low-density parity check (LDPC) code, the cascaded scheme can significantly improve the BER performance in the presence of impulsive interference, which also surpasses another cascaded code that is proved to be effective in impulsive noise channel, i.e., cascaded LDPC and LT coding scheme. [ABSTRACT FROM AUTHOR]

A dual order statistic detector is examined and shown to have the constant false alarm rate property when the clutter is modelled by a certain class of distributions. For the case of Pareto distributed clutter, mathematical analysis establishes some rules of thumb that can be used to select appropriate order statistic indices. The performance of this detector is then examined in such clutter, including an analysis of the effects of interfering targets and clutter transitions. [ABSTRACT FROM AUTHOR]

In this paper, the problem of Clutter Map Constant False Alarm Rate (CMAP-CFAR) detection is considered for a distributed detection with two and three sensors and a data fusion centre. We assume that the sensors are identical and that the target is a fluctuating according to the Swerling I model, embedded in a white Gaussian noise of unknown variance. Closed form expressions of the global probabilities of detection and false alarm for the ‘AND’, ‘OR’ and ‘MAJORITY’ rules and the Adaptive Detection Threshold are determined and the performance of the system is investigated and analyzed. [ABSTRACT FROM AUTHOR]

This paper focuses on real-time tracking of multiple extended targets in clutter based on labeled multi- Bernoulli filter. To address this problem, a novel approach is proposed within the recently presented box-particle framework. Unlike the traditional point-particle approach, the measurements of extended targets are modeled as interval measurements in this work, and the corresponding likelihood function is given based on interval analysis. Then, labeled multi-Bernoulli recursion for extended targets is implemented by box particles, referred to as BP-LMB filter. Furthermore, BP-MM-LMB filter is proposed to better accommodate the uncertainty of target dynamics by integrating the BP-LMB filter with interacting multiple models (IMM) algorithm. Simulations demonstrate that the proposed approach can significantly reduce the number of particles and well track multiple extended targets with less runtime. [ABSTRACT FROM AUTHOR]

In order to address the problem of passive tracking from multiple asynchronous angle-only sensors with location uncertainty in heavy clutter, a new iterative maximum-likelihood probabilistic data-association algorithm is proposed in this paper. An iterative prediction?update framework is adopted in the algorithm to simultaneously estimate the target state as well as the sensor state. At the prediction stage, a deterministic sampling approach is used to adjust the measurement covariance with sensor location uncertainty. Then a two-step grid-search technique is proposed to optimize the log-likelihood ratio, combined with a gradient-based search method. At the update stage, the operational sensor states are updated with target state estimates and measurements in corresponding validation gates. The updated sensor states are used to establish a more accurate log-likelihood ratio in the next iteration, which leads to better parameter estimation. In addition, the effects of the sensor location uncertainty on the track acceptance test and the posterior Cram?er?Rao lower bound are also analyzed. Simulation results show that the proposed method provides a computationally efficient way to improve track initialization performance in heavy clutter with sensor location uncertainty. The proposed work has applications in sonar tracking, geolocation, electronic support measures, and infrared search and tracking systems. [ABSTRACT FROM PUBLISHER]

Big brown bats ( Eptesicus fuscus) emit frequency-modulated (FM) biosonar sounds containing two or more harmonic sweeps. Echoes from frontally located targets arrive with first and second harmonics intact, leading to focused delay images. Echoes from offside or distant objects arrive with the second harmonic relatively weaker (lowpass-filtered), leading to defocused images, which prevents their clutter interference effects (Bates et al. J Exp Biol 214:394-401, ). Realistic targets contain several glints at slightly different distances and reflect several echoes at correspondingly different delays. The bat registers the delay of the nearest glint's echoes in the time domain. The delays of echoes from the farther glints are registered in the frequency domain, from interference nulls in the spectrum. Lowpass-filtering of echoes directly affects the image of the nearest glint by defocusing the delay image. However, lowpass-filtering also is superimposed on the interference spectrum used to register the farther glints, which distorts the pattern of interference nulls, defocusing the farther glints inversely, in the spectral domain, before they are perceived as delays. Differences in blurring between time-domain and frequency-domain parts of images identifies separate computational paths to perceptually reconstruct objects and prevent interference from off-side or distant clutter. [ABSTRACT FROM AUTHOR]

The video/infrared images captured at long range usually have low brightness and low contrast objects of interest with respect to surrounding background clutter. In this work, we design and analyze two detectors relying on the general likelihood ratio test (GLRT) to detect weak barely discernible multi-pixel objects with known (the first detector) and unknown shape, size and position (the second detector). The derived algorithms combine the multi-pixel matched subspace detector and multi-pixel background-plus-noise power change detector in a unique scheme. The numerical simulations and real experiments show that the first detector considerably outperforms the second one, especially in real-word situation, when the size, shape and position of the object are unknown. Finally, we compare the performances of the proposed detectors to the performances of the recently proposed modified mean subtraction filter and focused correlation (FC) detector. [ABSTRACT FROM AUTHOR]

In infrared target tracking it is difficult to predict the existence of background interference as there is a contradiction between the real-time and effectiveness of the algorithms. Hence this paper presents a particle filter tracking algorithm based on adaptive fusion of color features and edge features. Using the natural frame work of a particle filter, with infrared conditions it selects color features and edge features that best represent the target information to construct the multi-feature model of the target. According to the different feature separability of target and background, the weight of each feature component of a multi-feature is adaptively adjusted. This dynamic space model improves the particle filter tracking algorithm to predict the motion state of the particles to overcome the effects of environment mutations on tracking stability. Experimental results show that the proposed algorithm can overcome interference from all kinds of background clutter and noise ensuring tracking robustness and accuracy. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of the clutter subspace projector estimation in the context of a disturbance composed of a low rank heterogeneous (Compound Gaussian) clutter and white Gaussian noise. In such a context, adaptive processing based on an estimated orthogonal projector onto the clutter subspace (instead of an estimated covariance matrix) requires less samples than classical methods. The clutter subspace estimate is usually derived from the eigenvalue decomposition of a covariance matrix estimate. However, it has been previously shown that a direct maximum likelihood estimator of the clutter subspace projector can be obtained for the considered context. In this paper, we derive two algorithms based on the block majorization-minimization framework to reach this estimator. These algorithms are shown to be computationally faster than the state of the art, with guaranteed convergence. Finally, the performance of the related estimators is illustrated on realistic Space Time Adaptive Processing for airborne radar simulations. [ABSTRACT FROM PUBLISHER]