Clutter Measurements Research Articles

Improved Cycle-Consistency Generative Adversarial Network-Based Clutter Suppression Methods for Ground-Penetrating Radar Pipeline Data

Ground-penetrating radar (GPR) is a widely used technology for pipeline detection due to its fast detection speed and high resolution. However, the presence of complex underground media often results in strong ground clutter interference in the collected B-scan echoes, significantly impacting detection performance. To address this issue, this paper proposes an improved clutter suppression network based on a cycle-consistency generative adversarial network (CycleGAN). By employing the concept of style transfer, the network aims to convert clutter images into clutter-free images. This paper introduces multiple residual blocks into the generator and discriminator, respectively, to improve the feature expression ability of the deep learning model. Additionally, the discriminator incorporates the squeeze and excitation (SE) module, a channel attention mechanism, to further enhance the model’s ability to extract features from clutter-free images. To evaluate the effectiveness of the proposed network in clutter suppression, both simulation and measurement data are utilized to compare and analyze its performance against traditional clutter suppression methods and deep learning-based methods, respectively. From the result of the measured data, it can be found that the improvement factor (Im) of the proposed method has reached 40.68 dB, which is a significant improvement compared to the previous network.

Multiple passive-sensor distributed target tracking approach with Machine Learning Feedback

In this paper, a novel recursive state estimation approach in three-dimensional space for a non-maneuvering target is developed. The proposed technique employs angle and amplitude information in a distributed fusion setup, tackling the issue of target state estimation in cluttered environments, as well as target detection with a machine learning feedback mechanism with sequence measurements from multiple passive sensors. A new Integrated Probabilistic Data Association technique with Machine Learning Feedback (IPDA-MLF) is proposed. Most of the clutter measurements are eliminated by fusing angles and amplitude information in the Local Sensor tracker with an Integrated Probabilistic Data Association algorithm (LS-IPDA) framework. To deal with low observability issues, as well as to fuse information from multiple sensors, a Machine Learning (ML) algorithm is deployed, which utilizes a convolutional neural networks-based algorithm. The proposed IPDA-MLF algorithm resolves possible track-to-track associations and provides feedback to the local tracker for increased track retention. The refined information from local trackers is again converted to heat maps and the ML-based solution is used to fuse the local tracks at a central level. Simulation results show that the performance of the proposed algorithm is improved by the incorporation of the machine learning feedback, reducing false tracks, increasing true track retention, and eliminating mismatched fusion at the central tracker.

Does your old clutter measure spark joy?

Does your old clutter measure spark joy?

Centralized multi-sensor labeled multi-Bernoulli filter with partially overlapping fields of view

In this paper, the labeled multi-Bernoulli (LMB) filter is extended to the centralized multi-sensor (MS) case, resulting in a multi-sensor multi-object tracking (MS-MOT) algorithm having the ability to cope with partially overlapping fields-of-view (FoVs). Specifically, a joint prediction and update scheme for the MS-LMB filter is presented and the inherent NP-hard multi-dimensional assignment problem is solved via Gibbs sampling. We propose a suboptimal stationary distribution for the Gibbs sampling which admits conditionally independent sampling among multiple sensors thus dramatically decreasing the computational complexity. In addition, in order to tune object initialization when there is no background knowledge of the birth locations, a novel MS measurement-driven birth process is proposed. The proposed birth process has superior ability in terms of suppressing false tracks originating from clutter measurements of multiple sensors. The effectiveness of the proposed algorithm and birth process is demonstrated in simulation experiments concerning MS surveillance scenarios with partially overlapping sensor FoVs.

Measurement based dimension descent association algorithm for OTHR multi-detection multi-target tracking

The problem of multi-detection multi-target tracking (MDMTT) using over-the-horizon radar in dense clutter environment is studied in this paper. The biggest challenge of MDMTT is the 3-dimensional multipath data association among measurements, detection models and targets. In particular, a lot of clutter measurements are generated in dense clutter environment, which increase the computational burden of 3-dimensional multipath data association greatly. A measurement based dimension descent association (DDA) algorithm is proposed to solve the 3-dimensional multipath data association, which decomposes the 3-dimensional multipath data association into two 2-dimensional data associations. The proposed algorithm can reduce the computational burden compared with the optimal 3-dimensional multipath data association and the computational complexity is analyzed. Besides, a time extension method is designed to detect the new-born targets that appear in the tracking scene, which is based on the sequential measurements. The convergence of the proposed measurement based DDA algorithm is analyzed. The estimation error can convergence to 0 as the number of Gaussian mixtures tends to infinity. The effectiveness and rapidity of the measurement based DDA algorithm are demonstrated by the comparative simulation with the previously proposed algorithms.

Sea Clutter Measurement Test and Amplitude Characteristics Analysis in the South China Sea Nearshore Area

To address the problem of sea clutter amplitude distribution characteristics varying with bandwidth, this paper obtains the raw data of clutter under different polarization methods of HH and VV in X and Ka bands based on the sea clutter measurement experiment carried out in June 2022 in the near-shore waters of South China Sea. For the data in X-band HH polarization mode, the goodness-of-fit test was conducted based on the classical models of Rayleigh, Weibull, log-normal and K distribution for the amplitude distribution characteristics of the sea clutter, and the variation of the estimated parameters of the distribution model with the bandwidth was analyzed. The test data show that: 1, with the increase of bandwidth sea clutter amplitude decreases; 2, with the increase of bandwidth Weibull distribution model parameters are decreasing trend, k distribution parameters are increasing trend; 3, the test conditions, the offshore sea clutter amplitude distribution is the optimal K distribution. The conclusions of this paper can provide a comparison and reference for the subsequent study on the characteristics of sea clutter distribution.

Tracking Multiple Unmanned Aerial Vehicles through Occlusion in Low-Altitude Airspace

In an intelligent multi-target tracking (MTT) system, the tracking filter cannot track multi-targets significantly through occlusion in a low-altitude airspace. The most challenging issues are the target deformation, target occlusion and targets being concealed by the presence of background clutter. Thus, the true tracks that follow the desired targets are often lost due to the occlusion of uncertain measurements detected by a sensor, such as a motion capture (mocap) sensor. In addition, sensor measurement noise, process noise and clutter measurements degrade the system performance. To avoid track loss, we use the Markov-chain-two (MC2) model that allows the propagation of target existence through the occlusion region. We utilized the MC2 model in linear multi-target tracking based on the integrated probabilistic data association (LMIPDA) and proposed a modified integrated algorithm referred to here as LMIPDA-MC2. We consider a three-dimensional surveillance for tracking occluded targets, such as unmanned aerial vehicles (UAVs) and other autonomous vehicles at low altitude in clutters. We compared the results of the proposed method with existing Markov-chain model based algorithms using Monte Carlo simulations and practical experiments. We also provide track retention and false-track discrimination (FTD) statistics to explain the significance of the LMIPDA-MC2 algorithm.

Development of a quantitative measurement on visual clutter in see through display.

With the wide use of transmission displays to improve operation performance, the display information highlights clutter because of the contradiction between the massive amount of information and limited display area. Our study aimed to develop a quantitative measurement for declutter design and appraisal. Using the ergonomics research system of characters and symbols in a see-through cockpit display, we set the simulated flight task interface at four pixel scale levels by enlarging all the display elements in a certain ratio. Flight task videos of 12 clutter degrees were recorded using each flight interface matched with three flight scene complexity levels. A total of 60 pilots completed the visual search tasks in the flight task video while the eye tracker was used to record the view path in real time. Visual search performance was analyzed to study the effect of various clutter factors and levels on pilots' performance in visual search tasks, and acquire quantitative clutter measure parameters. GLM univariate test revealed that there were significant differences among the fixation time in areas of interest (AOI), total Fixation point number, total fixation time at four pixel scale levels, and three flight scene complexity levels (P < 0.05). Visual search performance declined after the cutoff point, while the clutter degree increased. According to the visual search performance data, the recommend feature congestion upper pixel number limit in a 600*800 display was 18,576, and the pixel ratio was 3.87%. A quantitative measurement for declutter design and appraisal of cockpit displays was developed, which can be used to support see-through display design.

Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking Over Wireless Sensor Networks

Uncertainty quantification plays a key role in the development of autonomous systems, decision-making, and tracking over wireless sensor networks (WSNs). However, there is a need of providing uncertainty confidence bounds, especially for distributed machine learning-based tracking, dealing with different volumes of data collected by sensors. This paper aims to fill in this gap and proposes a distributed Gaussian process (DGP) approach for point target tracking and derives upper confidence bounds (UCBs) of the state estimates. A unique contribution of this paper includes the derived theoretical guarantees on the proposed approach and its maximum accuracy for tracking with and without clutter measurements. Particularly, the developed approaches with uncertainty bounds are generic and can provide trustworthy solutions with an increased level of reliability. A novel hybrid Bayesian filtering method is proposed to enhance the DGP approach by adopting a Poisson measurement likelihood model. The proposed approaches are validated over a WSN case study, where sensors have limited sensing ranges. Numerical results demonstrate the tracking accuracy and robustness of the proposed approaches. The derived UCBs constitute a tool for trustworthiness evaluation of DGP approaches. The simulation results reveal that the proposed UCBs successfully encompass the true target states with 88% and 42% higher probability in X and Y coordinates, respectively, when compared to the confidence interval-based method.

Multiple User Intent Prediction Using Interacting Multiple Model Joint Probabilistic Data Association Filter

This paper presents a novel method for multi-user motion intent estimation when the motion is observed by a single sensor. A motion model is associated with each of the activities carried out by the operator and the end location of which is termed as a motion intent. Such modeling of intent is useful in human-robot collaborative tasks. The appropriate model selection is achieved via an interacting multiple model (IMM) filter. When the position measurements of multiple users originating from one sensor are close to each other, then the measurement to operator association becomes challenging. A joint probabilistic data association (JPDA) filter is employed to address this issue. The combined IMM and JPDA filter provides a way to infer the motion intent of each operator. Simulation results show that the IMM-JPDA filter tracks two target states reaching toward goal intent in the presence of clutter measurements originating from the Kinect sensor.

Multichannel Sea Clutter Measurement and Space-Time Characteristics Analysis with L-Band Shore-Based Radar

In order to study the space-time characteristics of sea clutter, the sea clutter is always measured by the airborne multichannel radar; however, the sea clutter shows the heterogeneity between range gates, which means the space-time covariance matrix’s correspondence to the single range gate cannot be estimated accurately. Meanwhile, the measurement of the sea clutter data by the airborne radar is usually affected by the motion of the platform, which makes the analysis results unrepresentative of the space-time characteristics of the pure sea clutter. In this paper, a sea clutter measurement method based on L-band shore-based multichannel radar is proposed, where the transmit sub-array periodically moves with the pulse repetition period to obtain multiple sets of coherent processing interval pulses for each range gate. This measurement method can exclude the influences of the moving platform. Moreover, a sea clutter space-time signal model of the single range gate is proposed, and the model is used to simulate three-dimensional sea clutter data with space-time coupling characteristics. With verification of the measured and simulated data, it can be seen that the data composed of single range gate and multiple coherent processing interval pulses can accurately estimate the space-time covariance matrix corresponding to this single range gate. Furthermore, the space-time characteristics are analyzed based on the measured data. The results show that the eigenvalue spectrum and the spread width of space-time power spectrum are influenced by the backscattering coefficient of sea clutter and the speed of sea surface motion. In comparison, the decorrelation effect caused by the backscattering coefficient of sea clutter is stronger than that caused by the speed of the surface motion. The proposed method is helpful for guiding multichannel sea clutter measurement and the analysis results are of great significance to the clutter suppression algorithms of the marine multichannel radar.

A general cardinalized probability hypothesis density filter

Based on random finite set, the probability hypothesis density (PHD) filter and the cardinalized PHD (CPHD) filter have been proposed for multitarget tracking as they are computational tractable. But the classical PHD and CPHD filter is not applicable when a target generates multiple detections. The general PHD filter was proposed to apply the PHD filter to arbitrary clutter and target measurement process. However, as a filter with better performance than the PHD filter, the CPHD filter for multiple-detection tracking is not proposed except the extended target CPHD filter and the multisensor CPHD filter. In our work, the general CPHD filter, which allows the clutter process and target measurement process to be arbitrary and performs better than the general PHD filter, is proposed. The proposed general CPHD filter is the general form of all kinds of CPHD filters, as well as all kinds of PHD filters and Bernoulli filters. In the simulation, the multiple-detection model of the over-the-horizon radar is used to demonstrate the tracking performance. The simulation results show that the proposed filter improves the accuracy of the state estimates and reduces the variance of the estimated number of targets.

Distributed fusion in harsh environments using multiple bearings-only sensors with out-of-sequence-refined measurements

This paper investigates a novel distributed fusion system for target tracking in three dimensional surveillance using multiple asynchronous BO sensors by simultaneously addressing the problems of target mis-detection and clutter disturbance as well as the out-of-sequence (OOS) information fusion. The proposed distributed fusion architecture consists of the local tracking and distributed fusion, wherein, the refined BO measurements output from the local tracking in each BO sensor are transmitted to the fusion center for subsequent distributed fusion. In the local tracking, instead of kinematic state, the target pseudo state is defined and tracked by operating the proposed pseudo state-integrated probabilistic data association (PS-IPDA) algorithm with an approximately linear pseudo state evolution model, resulting in greatly refined BO sensor measurements. In the fusion center, the sequential asynchronous IPDA-general one-step retrodiction update (SAIPDA-GORU) algorithm is proposed to effectively fuse the transmitted origin-unknown OOS refined measurements (OOSRMs) with the central tracks. The SAIPDA-GORU propagates the central track hybrid state to the OOS time by deploying a general one-step retrodiction technique, and mathematically formulates the OOSRMs-to-retrodicted central track association probabilities utilized to facilitate the fusing of currently filtered central track with OOSRMs. Numerical results show that the proposed PS-IPDA algorithm reduces the errors of target bearings measurement by around a half and eliminates more than 60% of the clutter measurements contained in raw detections, saving significant communication bandwidth between local sensors and fusion center. Furthermore, the proposed SAIPDA-GORU method tangibly improves the estimation accuracy and false track discrimination over the existing discarding method, with the potential to be implemented in real-time.

Effects of Environmental Clutter on Synthesized Chiropteran Echolocation Signals in an Anechoic Chamber

Ultrasonic bat detectors are useful for research and monitoring purposes to assess occupancy and relative activity of bat communities. Environmental “clutter” such as tree boles and foliage can affect the recording quality and identification of bat echolocation calls collected using ultrasonic detectors. It can also affect the transmission of calls and recognition by bats when using acoustic lure devices to attract bats to mist-nets. Bat detectors are often placed in forests, yet automatic identification programs are trained on call libraries using echolocation passes recorded largely from open spaces. Research indicates that using clutter-recorded calls can increase classification accuracy for some bat species and decrease accuracy for others, but a detailed understanding of how clutter impacts the recording and identification of echolocation calls remains elusive. To clarify this, we experimentally investigated how two measures of clutter (i.e., total basal area and number of stems of simulated woody growth, as well as recording angle) affected the recording and classification of a synthesized echolocation signal under controlled conditions in an anechoic chamber. Recording angle (i.e., receiver position relative to emitter) significantly influenced the probability of correct classification and differed significantly for many of the call parameters measured. The probability of recording echo pulses was also a function of clutter but only for the detector angle at 0° from the emitter that could receive deflected pulses. Overall, the two clutter metrics were overshadowed by proximity and angle of the receiver to the sound source but some deviations from the synthesized call in terms of maximum, minimum, and mean frequency parameters were observed. Results from our work may aid efforts to better understand underlying environmental conditions that produce false-positive and -negative identifications for bat species of interest and how this could be used to adjust survey accuracy estimates. Our results also help pave the way for future research into the development of acoustic lure technology by exploring the effects of environmental clutter on ultrasound transmission.

Robust Multisensor MeMBer Filter for Multiple Extended-Target Tracking

This paper develops a robust extended-target multisensor multitarget multi-Bernoulli (ET-MS-MeMBer) filter for enhancing the unsatisfactory quality of measurement partitions arising in the classical ET-MS-MeMBer filter due to increased clutter intensities. Specifically, the proposed method considers the influence of the clutter measurement set by introducing the ratio of the target likelihood to the clutter likelihood. With the constraint of the clutter measurement set, it can obtain better multisensor measurement partitioning results under the original two-step greedy partitioning mechanism. Subsequently, the single-target multisensor likelihood function for the clutter case is derived. Simulation results reveal a favorable comparison to the ET-MS-MeMBer filter in terms of accuracy in estimating the target cardinality and target state under conditions with increased clutter intensities.

Multiple extended target tracking by truncated JPDA in a clutter environment

Data association is a crucial part of target tracking systems with clutter measurements. In general, its complexity increases sharply with a number of targets and measurements. Recently, high-resolution sensors have given rise to extended target tracking problems and more than one measurements can emerge from each target, making the association problems more complex. In this study, a tractable algorithm based on the Gaussian process measurement model and truncated joint probabilistic data association technique is proposed for multiple extended target tracking in the presence of measurement origin uncertainty. Based on the marginal association probabilities, the calculation amount is effectively reduced by truncating the association events with low probabilities in the shortest path problem. The effectiveness of the proposed algorithm is verified by the test of multiple extended targets tracking and compared with the linear-time joint probabilistic data association as well as the algorithm on random finite sets. Simulation results show that the proposed algorithm can track multiple extended targets accurately, which is significant in high-resolution radar tracking systems.

Predicting User Performance in Augmented Reality User Interfaces with Image Analysis Algorithms

This study determines how to apply existing image analysis measures of visual clutter to augmented reality user interfaces, in conjunction with other factors that may affect performance such as the percentage of virtual objects compared to real objects in an interface, and the type of object a user is searching for (real or virtual). Image analysis measures of clutter were specifically chosen as they can be applied to complex and naturalistic images as is common to experience while using an AR UI. The end goal of this research is to develop an algorithm capable of predicting user performance for a given AR UI. In this experiment, twelve participants performed a visual search task of locating a target object in an array of objects where some objects were virtual, and some were real. Participants completed this task under three different clutter levels (low, medium, high) against five different levels of virtual object percentage (0%, 25%, 50%, 75%, 100%) and two types of targets (real, virtual) with repetition. Task performance was measured through response time. Results show significant differences in response time between clutter levels and between virtual object percentage, but not target type. Participants consistently had more difficulty finding objects in more cluttered scenes, where clutter was determined through image analysis methods, and had more difficulty in finding objects when the virtual of objects was at 50% as opposed to other scenarios. Response time positively correlated to measures of combined clutter (virtual and real) arrays but not for measures of clutter taken of the individual array components (virtual or real), and positively correlated with the clutter scores of the target objects themselves.

External Calibration of P-Band Island-Based Sea Clutter Measurement Radar on the Sea Surface

Radar external calibration is an important measurement in determining the radar cross sections of unknown targets and the reflectance coefficients of the sea surface. This measurement is often made in an anechoic chamber by standard programs. Sometimes, this measurement must be made in outdoor conditions, and the ambient environment severely affects the calibration precision. Thus, the scheme of the measurement must be specially designed. In this article, a calibration conducted at the sea near Lingshan Island is reported, and a full program of an external calibration on the sea surface and relevant analysis are presented for a P-band island-based sea clutter measurement radar. Data analysis shows that the multipath effect from the sea surface is a major factor to degrade the precision of the calibration. Measurement data over 4 days and the multipath reflectance model of the sea surface are combined to estimate the power increment from the multipath effect. Radar system constant is estimated based on the estimated power increments at different sites, where the viewing geometries of the radar are different. The results show that the proposed external calibration method on the sea surface attains a satisfactory precision.

Robust Control for the Detection Threshold of CFAR Process in Cluttered Environments.

The constant false alarm rate (CFAR) process is essential for target detection in radar systems. Although the detection performance of the CFAR process is normally guaranteed in noise-limited environments, it may be dramatically degraded in clutter-limited environments since the probabilistic characteristics for clutter are unknown. Therefore, sophisticated CFAR processes that suppress the effect of clutter can be used in actual applications. However, these methods have the fundamental limitation of detection performance because there is no feedback structure in terms of the probability of false alarm for determining the detection threshold. This paper presents a robust control scheme for adjusting the detection threshold of the CFAR process while estimating the clutter measurement density (CMD) that uses only the measurement sets over a finite time interval in order to adapt to time-varying cluttered environments, and the probability of target existence with finite measurement sets required for estimating CMD is derived. The improved performance of the proposed method was verified by simulation experiments for heterogeneous situations.

Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors.

In the multiple asynchronous bearing-only (BO) sensors tracking system, there usually exist two main challenges: (1) the presence of clutter measurements and the target misdetection due to imperfect sensing; (2) the out-of-sequence (OOS) arrival of locally transmitted information due to diverse sensor sampling interval or internal processing time or uncertain communication delay. This paper simultaneously addresses the two problems by proposing a novel distributed tracking architecture consisting of the local tracking and central fusion. To get rid of the kinematic state unobservability problem in local tracking for a single BO sensor scenario, we propose a novel local integrated probabilistic data association (LIPDA) method for target measurement state tracking. The proposed approach enables eliminating most of the clutter measurement disturbance with increased target measurement accuracy. In the central tracking, the fusion center uses the proposed distributed IPDA-forward prediction fusion and decorrelation (DIPDA-FPFD) approach to sequentially fuse the OOS information transmitted by each BO sensor. The track management is carried out at local sensor level and also at the fusion center by using the recursively calculated probability of target existence as a track quality measure. The efficiency of the proposed methodology was validated by intensive numerical experiments.