Targets In Heavy Clutter Research Articles

Detection and tracking of the trajectories of dynamic UAVs in restricted and cluttered environment

The unidentified number of unmanned aerial vehicles (UAVs) can execute aggressive maneuvers in the restricted and the cluttered environment. Therefore, it is difficult to detect and track the uncertain motion of the UAV target in such complex environment. In addition, multi-target tracking (MTT) algorithms such as joint data association approach faces various computational complexities that could exceeds the available computation resources. This paper develops a novel smoothing data association idea in a linear multi-target (LM) tracking based on integrated probabilistic data association (sLM-IPDA) algorithm that acts like a single target tracker in the MTT situation. The significant detection and tracking performance of a UAV are validated without a-prior information of the UAV’s initial position. The forward and backward tracks are initialized separately using sensor measurements received in each scan. The sLM-IPDA estimates the backward multi-tracks simultaneously associating backward tracks in a subsequent predicted forward track for fusion. Thus, a forward track state estimate is obtain using the smoothing (fusion) measurements. This significantly improves estimation accuracy for large number of cross-over targets in heavy clutter. Numerical assessments of the sLM-IPDA are verified using both simulation and experiment to demonstrate the application of the proposed algorithm.

Doppler Resilient Complementary Waveform Design for Active Sensing

Active sensing systems prefer probing waveforms with good auto-correlation properties. Faced with the difficulty in getting individual sequences with impulse-like aperiodic auto-correlation functions, we resort to the temporal waveform diversity based on complementary sets of sequences (CSS). It seems an ideal solution, but has rarely been used due to the extreme sensitivity of CSS to Doppler shifts. This paper is devoted to applying numerical methods to the design of Doppler resilient complementary waveforms (DRCW) whose ambiguity functions are almost free of range sidelobes along modest Doppler shifts. We formulate the problem as minimizing the worst-case peak sidelobe level (PSL) with low peak-to-average power ratio (PAR) constraints, and then approximate the min-max problem by a ${l}_{p}$ -norm minimization problem. A hierarchical strategy is developed to tackle the phase optimization and the amplitude-phase optimization respectively. Correspondingly, two algorithms based on phase-gradient and Majorization-Minimization are proposed, both of which can be efficiently computed via FFT. Numerical experiments show our method is effective and superior to existing ones. The DRCWs thus obtained have their range sidelobes at all lags suppressed below −60dB, and are expected to bring significant performance improvements in active sensing. Two application examples, detecting weak targets in heavy clutter and estimating the high-resolution range profile of an extended target by active radars, are given.

Tracking and Estimation of Multiple Cross-Over Targets in Clutter.

Tracking problems, including unknown number of targets, target trajectories behaviour and uncertain motion of targets in the surveillance region, are challenging issues. It is also difficult to estimate cross-over targets in heavy clutter density environment. In addition, tracking algorithms including smoothers which use measurements from upcoming scans to estimate the targets are often unsuccessful in tracking due to low detection probabilities. For efficient and better tracking performance, the smoother must rely on backward tracking to fetch measurement from future scans to estimate forward track in the current time. This novel idea is utilized in the joint integrated track splitting (JITS) filter to develop a new fixed-interval smoothing JITS (FIsJITS) algorithm for tracking multiple cross-over targets. The FIsJITS initializes tracks employing JITS in two-way directions: Forward-time moving JITS (fJITS) and backward-time moving JITS (bJITS). The fJITS acquires the bJITS predictions when they arrive from future scans to the current scan for smoothing. As a result, the smoothing multi-target data association probabilities are obtained for computing the fJITS and smoothing output estimates. This significantly improves estimation accuracy for multiple cross-over targets in heavy clutter. To verify this, numerical assessments of the FIsJITS are tested and compared with existing algorithms using simulations.

Linear multi-target IPF algorithm for automatic tracking

The radar tracking applications perform single and multiple object detections from noise-corrupted signal. These detections are used as measurements for target tracking. Tracking in cluttered environments requires false track discrimination and data association. However, data association for tracking closely located multiple targets in heavy clutter is prohibitive due to the excessive computational requirement. This results from exponential growth of mutually exclusive and exhaustive feasible joint events for track-to-measurement assignment. Specifically, our approach treats possible detections of targets followed by other tracks as additional clutter measurements. It starts by approximating the a priori probabilities of measurement origin. These probabilities are then used to modify the clutter spatial density at the location of the measurements. The probability of target existence is used to discriminate the false tracks. The extended simulations showed the effectiveness of this approach in two different multi-target tracking scenarios.

Learning to detect small target: A local kernel method

Small target detection is a critical problem in the Infrared Search And Track (IRST) system. Although it has been studied for years, there are some challenges remained, e.g. cloud edges and horizontal lines are likely to cause false alarms. This paper proposes a novel local learning framework to detect infrared small target in heavy clutter. First, we propose a quadratic cost function to learn the parameters in the weighted local linear model. Second, we introduce the kernel trick to extend the linear model to the nonlinear model. Finally, small targets are detected in the residual image which subtracts the estimation image from original input. Our method could preserve heterogeneous area while removing target region. Experimental results show our method achieves satisfied performance in heavy clutter.

Small target detection based on accumulated center-surround difference measure

Small target detection is a critical problem in the Infrared Search And Track (IRST) system. Although it has been studied for years, there are some difficulties remained due to the clutter environment such as the cloud edge and the horizontal line. In the homogeneous area such as sky, cloud-inner area and sea surface area, target can easily be detected, but in heterogeneous area which contains cloud edge, sky-sea line the target may be falsely detected. This paper proposes a novel method called accumulated center-surround difference measure to detect infrared small target in heavy clutter. Each pixel’s accumulated center-surround difference measure is computed by using sliding window manner. The measure can effectively distinguish target region and heterogeneous region. Experimental results show our method achieves better performance.

Through-Wall Imaging of Moving Targets Using UWB Random Noise Radar

For a through-wall ultrawideband (UWB) random noise radar using array antennas, subtraction of successive frames of the cross-correlation signals between each received element signal and the transmitted signal is able to isolate moving targets in heavy clutter. Images of moving targets are subsequently obtained using the back projection (BP) algorithm. This technique is not constrained to noise radar, but can also be applied to other kinds of radar systems. Different models based on the finite-difference time-domain (FDTD) algorithm are set up to simulate different through-wall scenarios of moving targets. Simulation results show that the heavy clutter is suppressed, and the signal-to-clutter ratio (SCR) is greatly enhanced using this approach. Multiple moving targets can be detected, localized, and tracked for any random movement.

EM-ML algorithm for track initialization using possibly noninformative data

Initializing and maintaining a track for a low observable (LO) (low SNR, low target detection probability and high false alarm rate) target can be very challenging because of the low information content of measurements. In addition, in some scenarios, target-originated measurements might not be present in many consecutive scans because of mispointing, target maneuvers, or erroneous preprocessing. That is, one might have a set of noninformative scans that could result in poor track initialization and maintenance. In this paper an algorithm based on the expectation-maximization (EM) algorithm combined with maximum likelihood (ML) estimation is presented for tracking slowly maneuvering targets in heavy clutter and possibly noninformative scans. The adaptive sliding-window EM-ML approach, which operates in batch mode, tries to reject or weight down noninformative scans using the Q-function in the M-step of the EM algorithm. It is shown that target features in the form of, for example, amplitude information (AI), can also be used to improve the estimates. In addition, performance bounds based on the supplemented EM (SEM) technique are also presented. The effectiveness of new algorithm is first demonstrated on a 78-frame long wave infrared (LWIR) data sequence consisting of an Fl Mirage fighter jet in heavy clutter. Previously, this scenario has been used as a benchmark for evaluating the performance of other track initialization algorithms. The new EM-ML estimator confirms the track by frame 20 while the ML-PDA (maximum likelihood estimator combined with probabilistic data association) algorithm, the IMM-MHT (interacting multiple model estimator combined with multiple hypothesis tracking) and the EVIM-PDA estimator previously required 28, 38, and 39 frames, respectively. The benefits of the new algorithm in terms of accuracy, early detection, and computational load are illustrated using simulated scenarios as well.

"Statistics 101" for multisensor, multitarget data fusion

This tutorial summarizes the motivations, concepts, techniques, and applications of finite-set statistics (FISST), a system-level, "top-down" direct generalization of ordinary single-sensor, single-target engineering statistics to the multisensor, multitarget realm. FISST provides powerful new conceptual and computational methods for dealing with multisensor, multitarget, and multi-evidence data fusion problems. The paper begins with a broad-brush overview of the basic concepts of FISST. It describes how conventional single-sensor, single-target formal Bayesian modeling is carefully extended to general data fusion problems. We examine a simple example: joint detection and tracking of a possibly non-existent maneuvering target in heavy clutter. The tutorial concludes with a commentary on certain criticisms of FISST.

Adaptive early-detection ML-PDA estimator for LO targets with EO sensors

The batch maximum likelihood estimator, combined with the probabilistic data association algorithm (ML-PDA), has. been shown to be effective in acquiring low observable (LO)-low signal-to-noise ratio (SNR)-nonmaneuvering targets in the presence of heavy clutter. The use of signal strength or amplitude information (AI) in the ML-PDA estimator facilitates the acquisition of weak targets. We present an adaptive algorithm, which uses the ML-PDA estimator with AI in a sliding-window fashion, to detect possibly maneuvering targets in heavy clutter using electro-optical (EO) sensors. The initial time and the length of the sliding window are adjusted adaptively according to the information content of the received measurements. A track validation scheme via hypothesis testing is developed to confirm the estimated track, that is, the presence of a target, in each window. The sliding-window ML-PDA approach, together with track validation, enables early track detection by rejecting noninformative scans, target reacquisition in case of temporary target disappearance, and the handling of targets with velocities evolving over time. We demonstrate the operation of the adaptive sliding-window ML-PDA estimator on a real scenario for tracking a fast-moving F1 Mirage fighter jet using an imaging sensor. The proposed algorithm is shown to detect the target, which is hidden in as many as 600 false alarms per scan, 10 frames earlier than the multiple hypothesis tracking algorithm. This ability to successfully process large amounts of data, with near real-time performance, under time-varying low SNR conditions makes the proposed estimator superior to other existing approaches.

Target course recognition using nonsinusoidal look-down radars

The target signature of an airplane as viewed by a nonsinusoidal look-down radar is obtained analytically as a function of target shape, azimuth orientation, look-down angle, and transmitted pulse duration. The information contained in the target signature can be used for detection of stationary and moving targets in heavy clutter, as well as the recognition of target shape and orientation. The relation between target signature and target azimuth orientation shows that pulse lengths shorter than airplane length are required to perform target recognition at all azimuth angles. Target course recognition in the radar ground plane is the process of determining the azimuth angle by selecting from the database the target signature that is most similar to the observed one. The effect of look-down angle on the target signature is also analyzed. At small look-down angles, target shape information is obtained with pulse lengths shorter than aircraft length. However, at large look-down angles pulse lengths much shorter than the airplane length are required. The variation of the target signature with azimuth and look-down angles are utilized for determining two-dimensional target course recognition. Cruise missile radars operating in a lookdown mode can utilize the very-high-resolution nonsinusoidal radar to enhance target shape recognition in addition to target course recognition.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>