Targets In Cluttered Environment Research Articles

Data-Driven Intelligent Multi-Frame Joint Tracking Method for Maneuvering Targets in Clutter Environments

Data-Driven Intelligent Multi-Frame Joint Tracking Method for Maneuvering Targets in Clutter Environments

High-resolution, multi-frequency and full-polarization radar database of small and group targets in clutter environment

High-resolution, multi-frequency and full-polarization radar database of small and group targets in clutter environment

Target‐tracking algorithm based on improved probabilistic data association

AbstractWhen tracking a single manoeuvring target in clutter environment, when the number of effective measurements within the detection threshold is small, it usually has a greater and more obvious impact on target‐tracking results. If the observation data error is large at this time, the tracking position and speed error will be larger. To solve this problem, a target‐tracking algorithm based on improved probabilistic data association is proposed in this paper. By dynamically adjusting the detection threshold, the effective quantity within the detection threshold of each frame is basically stable. Simulation results show that the improved algorithm is more accurate in location and speed than the traditional probabilistic data association method and Kalman filter, and the availability and effectiveness of the algorithm are verified.

Adaptive FDA Radar Transmit Power Allocation for Target Detection Enhancement in Clutter Environment

The detection of static targets in clutter environments has been an active topic in radar community, which is also a difficult problem, since there is no Doppler information that can be used to distinguish the target from clutter. Aiming at this problem, in this article, we develop an adaptive approach based on frequency diverse array (FDA) radar to enhance the detection performance. First, a parametric measurement model that incorporates the practical dependence of the clutter responses on transmit frequencies is introduced. Then, based on the generalized likelihood ratio (GLR) test, an adaptive detector that requires no training data is developed to determine the existence of a target in a specific cell under test, where the scattering features of the target and clutter are assumed unknown. In addition, the detector has the property of constant false alarm rate (CFAR) with respect to the clutter. Moreover, under the criterion of maximizing the detection probability, we propose an adaptive scheme in which the collected data are utilized to select the transmit weights for power allocation to improve the detection. Numerical simulations are conducted to illustrate the effectiveness of the proposed detector and demonstrate the performance improvement owing to the adaptive power allocation design.

Time Reversal Detection for Moving Targets in Clutter Environments

Moving target detection plays a pivotal role in many applications, especially in radar systems. In addition, time-reversal (TR) technology can improve radar detection probability by effectively solving the problem of multipath signals. However, detection performance can be affected when clutter exists and target Doppler shifts are unknown. For this reason, we propose a novel moving target detection algorithm that leverages TR technology and a novel waveform optimization method in clutter and multipath signal environments. In this paper, we establish a TR signal model for a moving target in such environments and derive the TR average likelihood ratio test (TR-ALRT) detector. Additionally, we introduce a waveform optimization method that adapts to clutter and unknown Doppler information further to enhance the performance of the TR-ALRT detector. Consequently, a TR-ALRT waveform optimization (TR-ALRT-WO) detector is derived to adapt to the unknown Doppler and environment information, thereby improving the detection probability. Comparative analysis with other detectors under the same conditions validates the effectiveness of our proposed TR-ALRT-WO detector. Furthermore, numerical experiments demonstrate the superior performance of the proposed detection algorithm.

Adaptive Clutter Suppression in Randomized Stepped-Frequency Radar

Randomized stepped-frequency (RSF) radar, which transmits random-frequency pulses in a coherent processing interval (CPI), has an excellent electronic counter-countermeasures (ECCM) performance and the ability to synthesize wide-frequency band with a narrow bandwidth receiver. However, due to the use of different frequency pulses, RSF waveform is incompatible with the conventional moving-target-detection (MTD) technique, which makes clutter suppression a difficult problem for RSF radar and limits its application. In this article, the effect of clutter on the coherent processing for RSF radar is first analyzed. The result shows that coherent processing can improve the signal-to-clutter ratio on target Doppler channel by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> (number of pulses in one CPI) times, which suppresses weak clutter scatterers to a large extent whilst strong clutter scatterers would still be left influential. Focusing on dominant clutter scatterers, the adaptive range-Doppler clutter suppression algorithm, which is based on designing clutter suppression filters with two-dimensional property to obtain the clear high-range-resolution profile of moving target in clutter environment, is then proposed for RSF radar. The proposed algorithm, which takes into account clutter Doppler extension, can be used for RSF radar coherent processing in clutter environment.

A closely spaced target track maintenance algorithm based on Gaussian mixture probability hypothesis density

As one suboptimal but tractable alternative to the Bayes filter within the random finite set (RFS) framework, the probability hypothesis density (PHD) filter has attracted considerable attention because of its low computational load and ease of implementation. However, the standard PHD filter is unable to estimate the tracks of targets in clutter circumstances. Although some PHD-based trackers are proposed to estimate the tracks of targets, the track maintenance performances of these trackers suffer from significant declines in closely spaced target environments. In this paper, with the help of the track label and an association-update factor matrix, a closely spaced target track maintenance algorithm based on the Gaussian mixture PHD filter is proposed to identify and provide the trajectories of close targets over times. Numerical simulations demonstrate that the proposed algorithm is capable of correctly estimating the tracks of closely spaced targets in clutter environments, and is superior to the related multi-target trackers in terms of target states, computational load, and association accuracy of tracks.

Joint Subarray Selection and Power Allocation for Cognitive Target Tracking in Large-Scale MIMO Radar Networks

This article develops a joint subarray selection and power allocation (JSSPA) strategy for tracking multiple targets in clutter environments using large-scale distributed MIMO radar networks. The mechanism of our strategy is to implement the best resource allocation on the basis of the information in the tracking recursive manner, aiming at improving the overall tracking accuracy. By integrating with the information reduction factor, we derive the predicted conditional Cramer–Rao lower bound (PC-CRLB) in clutter, which offers a more accurate measure of target state estimate than the standard posterior Cramer–Rao lower bound. Then, the sum of weighted PC-CRLBs is utilized as the optimization criterion to guide our JSSPA strategy. It is shown that the optimization model is a nonconvex problem that involves three variables, and a two-stage local search-based algorithm is proposed to solve it. Numerical simulations verify the tracking performance improvement by the proposed method, compared with four other resource allocation strategies.

Optimization of Adaptive MTI Filter

Moving target indication (MTI) is an effective means for radar to find moving targets in clutter environment. This paper introduces the basic principles of MTI, how to avoid the blind speed problem and the optimization of MTI filter. Implementing the multi-notch adaptive moving target indication (AMTI) filter that designed by using the stagger code in varied cases, which is based on a feature vector method optimization.

클러터 환경하에서 기동표적의 추적을 위한 가변차원 확률 데이터 연관 필터

An enhancement of the probabilistic data association filter is presented for tracking a single maneuvering target in clutter environment. The use of the variable dimensional structure leads the probabilistic data association filter to adjust to real motion of a target. The detection of the maneuver for the model switching is performed by the acceleration estimates taken from a bias estimator of the two stage Kalman filter. The proposed algorithm needs low computational power since it is implemented with a single filtering procedure. A simple Monte Carlo simulation was performed to compare the performance of the proposed algorithm and the IMMPDA filter.