Greedy Algorithm-Based Track-Before-Detect in Radar Systems

Wang, Hui; Yi, Jianxin; Wan, Xianrong

doi:10.1109/jsen.2018.2853188

Cited by 17 publications

(10 citation statements)

References 23 publications

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“…The Gaussian noise model is generally used in passive radars as well [3][4][5][6][7][8][9][10][11][13][14][15][16]. However, these methods are not optimal in real world problems with non-Gaussian noise.…”

Section: Noise Modelmentioning

confidence: 99%

“…In [15], the downlink of the illuminator was simulated as a code-division multiple access (CDMA) modulation signal and the dynamic programming (DP) algorithm was used for the detection of a weak target echo. To overcome the high computational load of the DP TBD method, a greedy algorithm with similar performance to that DP TBD, but with lower computational load is proposed in [16].…”

Section: Introductionmentioning

confidence: 99%

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Robust Weighted l1,2 Norm Filtering in Passive Radar Systems

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Soysal

Jiang

et al. 2020

Sensors

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Conventional methods such as matched filtering, fractional lower order statistics cross ambiguity function, and recent methods such as compressed sensing and track-before-detect are used for target detection by passive radars. Target detection using these algorithms usually assumes that the background noise is Gaussian. However, non-Gaussian impulsive noise is inherent in real world radar problems. In this paper, a new optimization based algorithm that uses weighted l 1 and l 2 norms is proposed as an alternative to the existing algorithms whose performance degrades in the presence of impulsive noise. To determine the weights of these norms, the parameter that quantifies the impulsiveness level of the noise is estimated. In the proposed algorithm, the aim is to increase the target detection performance of a universal mobile telecommunication system (UMTS) based passive radars by facilitating higher resolution with better suppression of the sidelobes in both range and Doppler. The results obtained from both simulated data with α stable distribution, and real data recorded by a UMTS based passive radar platform are presented to demonstrate the superiority of the proposed algorithm. The results show that the proposed algorithm provides more robust and accurate detection performance for noise models with different impulsiveness levels compared to the conventional methods.

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Section: Noise Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Weighted l1,2 Norm Filtering in Passive Radar Systems

Satar

Soysal

Jiang

et al. 2020

Sensors

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“…It makes a locally optimal choice at each stage and then obtains a global optimal or suboptimal solution. It can achieve the same or similar performance with DP under some restrictions [31]. However, to achieve a good performance, the threshold determination is very complex, and the performance will decrease dramatically if the trace is composed of large gaps.…”

Section: Introductionmentioning

confidence: 98%

“…is work mainly focuses on the tracking of the time-Doppler and time-azimuth traces for targets in sight; however, the propagation environment, including the sea, land, and ionospheric propagation [10][11][12], is extremely complicated, which leads to a challenging problem due to unknown and varying number of multitarget, model mismatch, lower signal-to-noise ratio (SNR), hybrid clutter, severe breaking points, intersecting traces, etc. Typical TBD strategies include Kalman filter (KF) [13,14], Hough transform (HT) [15][16][17], velocity filtering (VF) [18][19][20][21], particle filtering (PF) [22][23][24][25], dynamic programming (DP) [26][27][28][29][30], and Greedy algorithm [31]. eir basic concept, possible advantages, and limitation for application related to this work are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

“…A drawback of DP-TBD is that the computational complexity and memory resource requirements are potentially high, because it involves processing a high-dimensional data, processing by multiframe detection, and batch/sliding window processing. Furthermore, the problems about the initial values for tracking, the threshold determination, and the prior knowledge of the number of targets are also difficult for application [31]. (6) G-TBD: e Greedy algorithm (GA) is much simpler and more rapid than DP for solving some optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

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Track-Before-Detect Procedures in AM Radio-Based Passive Radar

et al. 2021

International Journal of Antennas and Propagation

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In amplitude modulation radio-based passive radar, the track-before-detect (TBD) procedures are performed to process long time observation data. This work mainly focuses on the tracking of the time-Doppler and time-azimuth traces of multiple target under real scenarios such as low signal-to-noise ratio, hybrid clutter, severe breaking points, and intersecting traces. A new original approach to deal with the TBD problem of this work is developed. Two types of linear equations according to the simplified radio wave propagation model are formulated, which is the key point of the proposed method. We make the theoretical analysis about how the linear equations can be used to track multiple target, and how multiple target’s constant velocities and initial positions can be estimated, which is an additional parameter estimation capability of the proposed method. Both the simulated data and real experimental data are performed with the proposed method and some conventional TBD methods. Several comparisons of the results are given to verify the effectiveness of the proposed method.

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Online multitarget tracking system for autonomous vehicles using discriminative dictionary learning with embedded auto‐encoder algorithm

Jiang

2022

Softw Pract Exp

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With the advancements in 5G network and mobile edge computing technology, autonomous vehicle technology has gained new development opportunities. Multitarget tracking becomes the research hotspots in autonomous vehicles. Since many factors such as motion blur, partial occlusion, and illumination changes affect the performance of target tracking, the problem of target tracking is still an open topic. In this article, inspired by the strong discriminative ability of dictionary learning, the discriminative dictionary learning with embedded auto‐encoder (DDLEA) algorithm is developed for the multitarget tracking system. The DDLEA algorithm integrates the auto‐encoder into the dictionary learning framework and learns sparse representations while preserving the local structure and discriminative information of data. The learned dictionary model has the strong recognition ability. Further, a multitarget tracking system is developed based on the proposed DDLEA algorithm and the hierarchical data association scheme. Based on the target confidence in the STKSVD model, the hierarchical data association method first uses the Hungarian algorithm to complete the preliminary matching of high confidence targets, and then further tracks the low confidence targets to improve the tracking ability. Experiments are carried out the public MOT 2015 dataset. Compared with several popular multitarget tracking algorithms, the tracking performance of our system is satisfactory.

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Greedy Algorithm-Based Track-Before-Detect in Radar Systems

Cited by 17 publications

References 23 publications

Robust Weighted l1,2 Norm Filtering in Passive Radar Systems

Robust Weighted l1,2 Norm Filtering in Passive Radar Systems

Track-Before-Detect Procedures in AM Radio-Based Passive Radar

Online multitarget tracking system for autonomous vehicles using discriminative dictionary learning with embedded auto‐encoder algorithm

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