Weak Target Tracking Research Articles

An Efficient Track-Before-Detect for Multi-PRF Radars With Range and Doppler Ambiguities

This article considers the detection and tracking of weak targets in multiple pulse repetition frequency (multi-PRF) radars using the track-before-detect (TBD) technique. By exploring the measurement independence among different PRFs, we decompose the joint multi-PRF and multiframe optimization problem into several lower dimensional maximizations, each of which corresponds to an intra-PRF multiframe processing. An efficient two-stage TBD procedure with a parallel structure is proposed for the multi-PRF radars. Specifically, in the first stage, a constraint inequality is derived analytically and used to decouple the measurement ambiguities from the nonlinear conversion relationship between polar and Cartesian coordinates. The intra-PRF multiframe integration can then be carried out concurrently using the ambiguous measurements and the target-like measurement plot sequences with different ambiguities are extracted for different PRFs. In the second stage, a covariance combination fusion-based inter-PRF joint disambiguation and estimation algorithm is proposed to solve the ambiguities of the plot sequences and output high-accuracy target tracks. Simulation experiments show that the proposed algorithm can provide a good detection performance and higher tracking accuracy with much lower complexity.

Radar Weak Target Tracking Based on LMB Smoothing

Radar Weak Target Tracking Based on LMB Smoothing

A Bernoulli Track-Before-Detect Filter for Interacting Targets in Maritime Radar

This article is devoted to the problem of multitarget tracking in the maritime environment using Bernoulli track-before-detect (TBD) filtering. Detection and tracking of weak targets in sea clutter using high-resolution airborne radar is notoriously challenging compared to classical target tracking problems. Furthermore, the measured amplitude in range-azimuth cells that are in the vicinity of closely spaced (interacting) targets represents a superposition of individual target contributions, demanding a special joint treatment. This article proposes a novel solution for multitarget tracking using Bernoulli TBD for maritime radar and covers cases when targets are both far apart and closely spaced. Comparisons with existing TBD algorithms are used to verify the efficacy of the proposed Bernoulli TBD algorithm with realistic sea clutter simulations used to create challenging scenarios with variable target strengths.

Improved GM-PHD filter based on threshold separation clusterer for space-based starry-sky background weak point target tracking

The movement of the background caused by the movement of a sensor platform has always been a major challenge in space-based infrared weak and small target tracking. In addition to clutter, false alarms, noise, and other disturbances, the most serious source of interference in space-based infrared weak target tracking comes from the stars. This kind of interference always exists and is large enough to be often mistakenly recognized as a new target by traditional algorithms. To reduce the influence of sensor platform movement and stellar interference on the target tracking of weak points in space, this paper respectively proposes a fast iterative closest point (ICP) registration algorithm for sparse target points and a threshold separation clustering algorithm. After pruning and merging using a Gaussian mixture probability hypothesis density (GM-PHD) filter-based algorithm, the threshold separation cluster separates the weak point targets by thresholds, and then clusters according to the Euclidean distance between the targets. Moreover, to prevent stars with special positions from being clustered into a single group, a dynamic weight extraction scheme is adopted to better distinguish stars in the group. We compared the proposed algorithm with the original GM-PHD, forward-backward smoothing (FBS), and N-scan approaches in starry-sky tracking scenarios simulated using the Tycho-2 catalog. Experimental results show that the proposed algorithm has better tracking accuracy and a lower rate of false detections.

An efficient implementation of multiple weak targets tracking filter with labeled random finite sets for marine radar

The detection and tracking of multiple weak targets with time-varying and unknown number has become a hot spot and challenging problem in marine radar application. This paper integrates δ-generalized labeled multi-Bernoulli (δ-GLMB) density and labeled multi-Bernoulli (LMB) density, which are two important special cases of labeled random finite set, to propose an effective method for multiple weak target tracking with track-before-detect strategy based on Bayes framework including prediction and update step. The proposed method can deal with multi-target tracking problem with tractable computational complexity due to prediction step with dynamic grouping procedure albeit with slightly precision loss compared with standard δ-GLMB filter. In general, the conjugate prior property based GLMB does not hold on for generic observation model in update step. In order to solve this problem, this paper applies a tractable principled approximation involving GLMB density, which was firstly proposed in the literature, to make whole Bayes filtering with labeled RFS to perform iteratively for each group with TBD model. Finally, the numerical simulation results of Swerling type 1 target tracking in K-distributed sea clutter illustrate that the proposed method is superior to the methods including standard δ-GLMB-TBD filter, LMB-TBD filter and DP-TBD algorithm in comprehensive performance.

A Dynamic Programming Track‐Before‐Detect Algorithm Based on Local Linearization for Non‐Gaussian Clutter Background

The Dynamic programming track before detect (DP-TBD) algorithm has been widely used for detection and tracking of weak targets. The selection of the merit function has an immediate influence on the performance of the DP-TBD. The amplitude merit function is easy to calculate, but the performance of which will decrease in the presence of non-Gaussian clutter. The likelihood ratio merit function in closed analytical form is difficult to derive under non-Gaussian background without target signal parameters. To solve this problem, a novel DPTBD algorithm based on local linearization is proposed. Taking maximum of the state conditional probability ratio of the target as the optimal criteria, a recursive integration equation is derived. The equation is locally linearized by Taylor series expansion and a suboptimal multi-frame test statistic is developed. The calculation of new merit function in the statistic needs only clutter distribution model, and heavy clutter peak can be restrained by making use of clutter distribution characters. So the proposed algorithm can efficiently extract weak target in strong non-Gaussian clutter. Numerical simulations are provided to assess and compare the performance of the proposed algorithm. It turns out that the proposed algorithm has better detection and tracking performance than the widely used DPTBD algorithm at present and is resilient to various clutter distribution models.

Improved weak space object tracking assisted by strong target

Radar sensor is a promising candidate for space surveillance, providing timely assessment of space objects and events. Weak space target tracking is a challenging conundrum mainly because of the low detection probability and poor measurement accuracy. In this study, a novel method of weak space target tracking assisted by strong space target is proposed. By introducing the relative motion tracking, the steady-state estimates of strong target are utilised to improve the state estimation of weak target. Compared with weak target tracking without strong target assistance, the proposed tracking method can provide higher state estimation accuracy, faster convergence rate and better track validity probability for weak target. The results of numerical simulations have validated the effectiveness of the proposed technique.

Particle filter track-before-detect implementation on GPU

Track-before-detect (TBD) based on the particle filter (PF) algorithm is known for its outstanding performance in detecting and tracking of weak targets. However, large amount of calculation leads to difficulty in real-time applications. To solve this problem, effective implementation of the PF-based TBD on the graphics processing units (GPU) is proposed in this article. By recasting the particles propagation process and weights calculating process on the parallel structure of GPU, the running time of this algorithm can greatly be reduced. Simulation results in the infrared scenario and the radar scenario are demonstrated to compare the implementation on two types of the GPU card with the CPU-only implementation.