Track Detection of Low Observable Targets Using a Motion Model

Park, J. Daniel; Doherty, J.F.

doi:10.1109/access.2015.2471935

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…A motion model analyzes the motion curve of the target in the history frames and predicts the position of the target in the current frame [37,38]. Most single-object trackers do not use a motion model [6][7][8][9].…”

Section: Motion Modelmentioning

confidence: 99%

Spatial–Semantic and Temporal Attention Mechanism-Based Online Multi-Object Tracking

Meng

Wang

et al. 2020

Sensors

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Multi-object tracking (MOT) plays a crucial role in various platforms. Occlusion and insertion among targets, complex backgrounds and higher real-time requirements increase the difficulty of MOT problems. Most state-of-the-art MOT approaches adopt the tracking-by-detection strategy, which relies on compute-intensive sliding windows or anchoring schemes to detect matching targets or candidates in each frame. In this work, we introduce a more efficient and effective spatial–temporal attention scheme to track multiple objects in various scenarios. Using a semantic-feature-based spatial attention mechanism and a novel Motion Model, we address the insertion and location of candidates. Some online-learned target-specific convolutional neural networks (CNNs) were used to estimate target occlusion and classify by adapting the appearance model. A temporal attention mechanism was adopted to update the online module by balancing current and history frames. Extensive experiments were performed on Karlsruhe Institute of Technologyand Toyota Technological Institute (KITTI) benchmarks and an Armored Target Tracking Dataset (ATTD) built for ground-armored targets. Experimental results show that the proposed method achieved outstanding tracking performance and met the actual application requirements.

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Section: Motion Modelmentioning

confidence: 99%

Spatial–Semantic and Temporal Attention Mechanism-Based Online Multi-Object Tracking

Meng

Wang

et al. 2020

Sensors

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“…An important issue in the radar tracking community that arouses wide attention is the detection and tracking of weak (dim) targets in very low signal-to-noise ratio (SNR) environments [1][2][3][4][5]. A key difficulty for solving this lies in the inherent relationship between target detection probability and false alarm probability.…”

Section: Introductionmentioning

confidence: 99%

Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform

Rao

Zhou

Nie³

2022

Remote Sensing

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An elliptical Hough transform (EHT) algorithm is proposed in the framework of track-before-detect (TBD) for joint detection and tracking of weak exoatmospheric targets. The new approach exploits the fact that when restricted to a two-body problem, the exoatmospheric target often follows an elliptical orbit, and thus the Hough transform integrated with orbital geometry information would have better detection performance. The relationship between the original radar measurements in data space and the elliptical parameters in parameter space is explicitly derived with multiple steps of coordinate transformation. It is found that the data points mapping into the parameter space essentially represent a quartic curve. An EHT-based algorithm is then designed, and orbit planarity is also taken into account to reduce the effect of noise accumulation. The influences of primary and secondary thresholds and the signal-to-noise ratio (SNR) on the detection performance are compared by simulations. Additionally, a real radar tracking dataset from a scientific satellite on 28 May 2017 is used to investigate the efficiency of the method. By adding some imaginary clutter to the raw orbit, the results indicate that it is very effective in detecting the real satellite trajectory in a low signal-to-noise ratio (SNR) environment. The advantage of the new method lies in it can not only simultaneously detect and track weak exoatmospheric targets but also can predict the trajectory by using these available detected parameters.

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“…W ITH the development of stealth technique and highly maneuvering target, such as stealth craft, unmanned aerial vehicle, and ballistic missile etc, the long-time integration and detection of the low-observable maneuvering targets attract growing attentions in modern radar [1][2][3][4][5]. In general, the radar echoes of the weak and maneuvering targets have several characteristics: 1) Low signal-to-noise ratio (SNR); 2) High-speed or high maneuverability [5].…”

Section: Introductionmentioning

confidence: 99%

Coherent Integration Algorithm for a Maneuvering Target Based on Frequency Spectrum Segment Processing

2020

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This paper considers the coherent integration problem for a low-observable maneuvering target, where the velocity and acceleration result in range migration (RM) and Doppler frequency migration (DFM) within the coherent pulse interval. A novel method based on the frequency spectrum segment processing (FSSP) and the segmental Lv's distribution (SLVD) is proposed to realize the long-time coherent integration for multiple maneuvering targets. In this method, FSSP is proposed to eliminate the RM effect by dividing the received signal into several subband signals and expanding the range resolution of the subband signals. Then SLVD is applied to achieve the coherent integration of the subband signals and accumulate the energy of all the subband signals coherently. The proposed method can realize the coherent integration for multiple maneuvering targets without any prior knowledge of the targets' motion. The simulation and experimental results demonstrate the effectiveness of the proposed algorithm. INDEX TERMS Maneuvering target detection, long-time coherent integration, frequency spectrum segment processing (FSSP), segmental Lv's distribution (SLVD).

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Track Detection of Low Observable Targets Using a Motion Model

Cited by 10 publications

References 27 publications

Spatial–Semantic and Temporal Attention Mechanism-Based Online Multi-Object Tracking

Spatial–Semantic and Temporal Attention Mechanism-Based Online Multi-Object Tracking

Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform

Coherent Integration Algorithm for a Maneuvering Target Based on Frequency Spectrum Segment Processing

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