Heavy Clutter Environment Research Articles

Multi-Target Track Initiation in Heavy Clutter

In the heavy clutter environment, the information capacity is large, the relationships among information are complicated, and track initiation often has a high false alarm rate or missing alarm rate. Obviously, it is a difficult task to get a high-quality track initiation in the limited measurement cycles. This paper studies the multi-target track initiation in heavy clutter. At first, a relaxed logic-based clutter filter algorithm is presented. In the algorithm, the raw measurement is filtered by using the relaxed logic method. We not only design a kind of incremental and adaptive filtering gate, but also add the angle extrapolation based on polynomial extrapolation. The algorithm eliminates most of the clutter and obtains the environment with high detection rate and less clutter. Then, we propose a fuzzy sequential Hough transform-based track initiation algorithm. The algorithm establishes a new meshing rule according to system noise to balance the relationship between the grid granularity and the track initiation quality. And a flexible superposition matrix based on fuzzy clustering is constructed, which avoids the transformation error caused by 0–1 voting method in traditional Hough transform. In addition, the algorithm allows the superposition matrixes of nonadjacent cycles to be associated to overcome the shortcoming that the track can’t be initiated in time when the measurements appear in an intermittent way. And a slope verification method is introduced to detect formation-intensive serial tracks. Last, the sliding window method is employed to feedback the track initiation results timely and confirm the track. Simulation results verify that the proposed algorithms can initiate the tracks accurately in heavy clutter.

A Track Initiation Algorithm Using Residual Threshold for Shore-Based Radar in Heavy Clutter Environments

There is a large error in the actual radar trajectory tracking process. Track initiation is the primary problem in trajectory tracking and the first step in target tracking. The current track initiation algorithms are greatly affected by heavy clutter environments, so it is necessary to propose an algorithm to solve the problem of low track initiation efficiency. This paper presents a track initiation algorithm using a residual threshold in heavy clutter environments. The falling probability of measured value and decision threshold are used to determine the correlation window. The angle limiting condition is added to establish the track association, and the residual threshold is used to further eliminate the false tracks. The initial track experiment with the trajectory data in the sea near Rizhao Port shows that the algorithm is superior to the traditional logic method and Kalman filter method in track quality. The experiment uses the AIS buffer zone to calculate track initiation probability and uses the multi-region AIS trajectory data for verification. The experimental result shows that track initiation probability with the proposed algorithm in this paper can reach 92.31%.

Robust measurement validation for radar target tracking using prior information

In this study, the authors propose a robust measurement validation algorithm for radar target tracking in a heavy-clutter environment. This algorithm defines the prior information with the radar measurement information from the estimated target trajectory. An additional validation gate is set up within the conventional validation gate using the statistics of the prior information then only a part of the validation measurement is selected to update the target state. To verify the effectiveness of the proposed algorithm, the authors designed a simulation for target tracking. The simulation results show that the proposed algorithm has a lower root mean squared position error compared to conventional algorithms in a heavy-clutter environment.

A Variable-Diameter-Arc-Helix Radon Transform for Detecting a Near Space Hypersonic Maneuvering Target

For detection and tracking of a near space hypersonic maneuvering target in a heavy clutter environment, a novel transform domain method called Variable-Diameter-Arc-Helix Radon Transform (VDAH-RT) is proposed as a tool to integrate observations along the maneuvering path. Considering the 3D maneuvering trajectory is like a variable diameter arc helix, the trajectory of near space hypersonic target can be modeled by a variable diameter arc helix. Since the maneuvering parameters are unknown, variable diameter arc helixes with different parameters can be used to match the maneuver track. Using the idea of Radon Transform(RT), the problem of maneuvering trajectory detection and tracking is transformed into the energy peak detection problem in VDAH-RT parameter domain. This Variable-Diameter-Arc-Helix Radon Transform (VDAH-RT) technique extends the RT or HT(Hough Transform) for straight-line features detection to an arbitrary curve trajectory. The results demonstrate that the proposed algorithm provides great advantage in the detection and tracking of a hypersonic maneuvering dim target.

Particle Filter for Tracking with Pulsed Doppler Radar in Heavy Clutter Environment

Particle Filter for Tracking with Pulsed Doppler Radar in Heavy Clutter Environment

Low-Altitude and Slow-Speed Small Target Detection Based on Spectrum Zoom Processing

This paper proposes a spectrum zoom processing based target detection algorithm for detecting the weak echo of low-altitude and slow-speed small (LSS) targets in heavy ground clutter environments, which can be used to retrofit the existing radar systems. With the existing range-Doppler frequency images, the proposed method firstly concatenates the data from the same Doppler frequency slot of different images and then applies the spectrum zoom processing. After performing the clutter suppression, the target detection can be finally implemented. Through the theoretical analysis and real data verification, it is shown that the proposed algorithm can obtain a preferable spectrum zoom result and improve the signal-to-clutter ratio (SCR) with a very low computational load.

Improving RPCA-Based Clutter Suppression in GPR Detection of Antipersonnel Mines

Detecting shallow buried antipersonnel mines (APMs) with a ground-penetrating radar (GPR) is a challenging task because of clutter contamination, which often obscures the APM response. In this letter, a novel method combining migration imaging with the low-rank and sparse representation method to suppress clutter and extract target image is presented. The proposed method first focuses and strengthens the target response with migration imaging. Then, since the focused target response and clutter, respectively, constitute the sparse component and the low-rank component of the recorded data, the recently proposed robust principal component analysis (RPCA) can be applied to the recorded data to separate the target response (sparse component) from the clutter (low-rank component). Numerical simulation and experiments with real GPR systems are conducted. Results demonstrate the effectiveness of the proposed method in improving signal-to-clutter ratio and retrieving geometrical information of the target, which permits a better APM identification in heavy clutter environment.

Efficient TR‐TBD algorithm for slow‐moving weak multi‐targets in heavy clutter environment

In this study, the authors present an efficient time-dimension-reduced track-before-detect (TR-TBD) processor for slow-moving weak multi-targets detection in strong clutter environment. In their proposed framework, they elaborate observations from multiple frames (or scans) and resample them in time direction, then distinguish the slow-moving targets from the clutter in the radon parameter domain by exploiting the fact that different velocities of targets have different skewing angles corresponding to their tracks in the range–time (range–pulse) plane. To further enlarge the skewing angles differences between the slow-moving targets and the clutter, TR-TBD is proposed by incorporating the time-dimension reduction operator. This is very helpful to amplify the skewing angle of slow-moving targets, while the improvement is very small for the clutter. Therefore, it is much convenient to figure out the slow-moving weak targets from heavy clutter environment based on their amplified skewing angle differences by setting proper threshold. After detecting the targets, CLEAN-based track recovery method is proposed to eliminate the false tracks and recover the true tracks. Experimental results on real-data demonstrate that the proposed algorithm can detect the closely spaced targets and eliminate the false tracks under low signal-to-noise ratio and signal-to-clutter ratio.

Multiple Detection-Aided Low-Observable Track Initialization Using ML-PDA

In low signal-to-noise ratio or heavy clutter environments, target track initialization is a challenging task. The maximum likelihood probabilistic data association (ML-PDA) algorithm has been demonstrated to be effective in dealing with this issue. In practical scenarios, multiple signals from one target via different propagation paths can be detected in a scan. Signals from different propagation paths convey useful information and can improve track initialization performance. However, the conventional ML-PDA algorithm assumes that a target can generate at most one detection per scan. That is, it cannot handle multiple target-originated measurements per scan correctly, nor take full advantage of the additional information contained in those seemingly extraneous returns. In this paper, a multiple-detection ML-PDA (MD-ML-PDA) estimator is proposed to rectify this shortcoming. The proposed estimator exploits the additional information available in all measurements by considering the combinatorial events of association that are formed from MD patterns. It is capable of handling the possibility of multiple target-originated measurements per scan with less-than-unity detection probability for various paths in the presence of clutter. The proposed MD-ML-PDA estimator is applied to a simulated sonar target tracking scenario. The same algorithm can be used on other angle-only tracking problems as well. Results show that MD-ML-PDA can effectively handle multiple target-originated measurements and yield improved track initialization performance over the traditional single detection ML-PDA. The Cramer–Rao lower bound for MD track initialization is also derived.

Small dim object tracking using a multi objective particle swarm optimisation technique

Dim object tracking in a heavy clutter environment is a theoretical and technological challenge in the field of image processing. For a small dim object, conventional tracking methods fail for the lack of geometrical information. Multiple hypotheses testing (MHT) is one of the generally accepted methods in target tracking systems. However, processing a tree structure with a significant number of branches in MHT has been a challenging issue. Tracking high-speed objects with traditional MHT requires some presumptions which limit the capabilities of these methods. This study presents a hierarchal tracking system in two levels to solve this problem. For each point in the lower-level, a multi objective particle swarm optimisation technique is applied to a group of consecutive frames to reduce the number of branches in each tracking tree. Thus, an optimum track for each moving object is obtained in a group of frames. In the upper-level, an iterative process is used to connect the matching optimum tracks of the consecutive frames based on the spatial information and fitness values. The experimental results show that the proposed method has a superior performance in relation to some common dim object tracking methods over different image sequence data sets.

Integrated track initialization and maintenance in heavy clutter using probabilistic data association

Target tracking in high clutter or low signal-to-noise ratio (SNR) environments is an important topic and still a challenging task. Joint Maximum Likelihood Probabilistic Data Association (JML-PDA) is a well-known batch method for initializing the tracks of very low observable (VLO) targets in heavy clutter environments. On the other hand, the Joint Probabilistic Data Association (JPDA) algorithm, which is commonly used for recursive track maintenance, lacks track initialization capability. In this paper, we propose a Combined JML-PDA and JPDA (CJML-PDA) algorithm to automatically initialize and maintain the tracks. This combined approach seamlessly shares information between the initialization and maintenance stages of the tracker. In contrast, in other batch-recursive approaches the initialization and maintenance algorithms operate rather independent of each other. The effectiveness of the proposed algorithm is demonstrated on a heavy clutter scenario and its performance is tested on closely-spaced (but resolved) targets with association ambiguity using angle-only measurements.

복잡한 환경에서 다중표적추적을 위한 고속 트랙병합 기법

In this paper, we proposed a method of fast track merging which is the foundation of track to track association technique. The existing method of track merging is performed throughout comparison between tracks to tracks. Therefore, it has heavy calculation time. In our research, we developed a method for fast clustering by using nearest neighbor measurement identification. The simulation results show that the proposed method is more faster than previous method about 3.3%. We expect that this method could be effectively used in multi-target tracking particularly in heavy clutter environment.

Track Initiation for Dim Small Moving Infrared Target Based on Spatial-Temporal Hypothesis Testing

Track initiation for dim small moving target particularly in a heavy clutter environment is a theoretical and technological challenge for diverse tracking systems. The different spatial-temporal characteristics presenting in sequence scans are utilized to recognize target and initialize track in this paper. In spatial domain, the small target mapped in the image is a uniform gray spot other than pixel-sized object with high congregated degree, whereas, the false alarm is independent, irrelative and lower congregated degree. In temporal domain, the target’s trajectory projected on image sequence is continuous for the continuity of target motion and will appear in the neighborhood at consecutive instants with the maximum probability, on the contrary, the false alarm is disorderly, and occurs in the neighborhood at consecutive instants is impossible. Based on the spatial-temporal characteristics mentioned above, a track initiation algorithm for dim small moving target based on spatial-temporal hypothesis testing, which consists of neighborhood clustering and trajectory continuity, is derived and analyzed in detail. The theory analysis and experimental results show that this method could effectively initialize the track for dim small moving target in heavy clutter environment.

A gate size estimation algorithm for data association filters

The problem of forming validation regions or gates for new sensor measurements obtained when tracking targets in clutter is considered. Since the gate size is an integral part of the data association filter, this paper is intended to describe a way of estimating the gate size via the performance of the data association filter. That is, the gate size can be estimated by looking for the optimal performance of the data association filter. Simulations show that this estimation method of the gate size offers advantages over the common and classical estimation methods of the gate size, especially in a heavy clutter and/or false alarm environment.

An analysis of the shadow feature technique in radar detection

Good target detection can be achieved by using shadow feature algorithms, particularly in heavy clutter environments. The performance enhancement of a shadow feature algorithm applied to a maximum likelihood constant false-alarm rate (ML-CFAR) detector is analyzed. Results show that detection performance is significantly improved.

Experimental studies on circular SAR imaging in clutter using angular correlation function technique

This paper describes a novel approach for three-dimensional (3-D) imaging in a heavy-clutter environment. The proposed approach consists of two techniques. The first calls for the use of a synthetic aperture radar (SAR) moving in a circular orbit, whereas the second involves using angular correlation function (ACF) measurement for achieving clutter suppression. To demonstrate the imaging and clutter suppression capabilities of this combined approach (circular-correlation SAR), microwave imaging experiments are conducted at X-band frequencies (7-13 GHz). It is found that in heavy-clutter environments, this combined SAR approach outperforms conventional SAR, resulting in clearer images with enhanced target-to-clutter ratio.

Neural network learning of low-probability events

In the problem of stationary target identification (STI) via millimeter wave (MMW) seeker radars in heavy clutter environments, it is often necessary to use nonparametric identification procedures, as detailed parametric models of clutter and target returns are generally unavailable. Neural networks provide an attractive approach to perform nonparametric identification. However, when identifying low-probability events, the computational overhead associated with training a neural network can become excessive. This is because low-probability events must be adequately represented in the training sample. We present a modified backpropagation training algorithm based on a likelihood ratio weighting function (LRWF) to train the neural network using a much smaller training set than that required using the standard backpropagation algorithm This algorithm is closely related to the importance sampling technique used in digital communication systems to obtain probability of error estimates by using a much smaller number of simulation runs than what is required with standard Monte Carlo simulation. The modified backpropagation technique results in a significant reduction in computational overhead in training the network, resulting from a substantial reduction in the size of the training set required to achieve a given level of performance. We demonstrate the performance of the algorithm on simulated data for the STI problem in MMW radar.