scholarly journals Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar

2021 ◽  
Vol 13 (4) ◽  
pp. 701 ◽  
Author(s):  
Binbin Wang ◽  
Hao Cha ◽  
Zibo Zhou ◽  
Bin Tian
Keyword(s):  
Fourier Transform ◽  
Target Detection ◽  
Time Integration ◽  
Detection Performance ◽  
Bistatic Radar ◽  
Detection Range ◽  
Robust Detection ◽  
Coherent Integration ◽  
Long Time ◽  
Long Time Integration

Clutter cancellation and long time integration are two vital steps for global navigation satellite system (GNSS)-based bistatic radar target detection. The former eliminates the influence of direct and multipath signals on the target detection performance, and the latter improves the radar detection range. In this paper, the extensive cancellation algorithm (ECA), which projects the surveillance channel signal in the subspace orthogonal to the clutter subspace, is first applied in GNSS-based bistatic radar. As a result, the clutter has been removed from the surveillance channel effectively. For long time integration, a modified version of the Fourier transform (FT), called long-time integration Fourier transform (LIFT), is proposed to obtain a high coherent processing gain. Relative acceleration (RA) is defined to describe the Doppler variation results from the motion of the target and long integration time. With the estimated RA, the Doppler frequency shift compensation is carried out in the LIFT. This method achieves a better and robust detection performance when comparing with the traditional coherent integration method. The simulation results demonstrate the effectiveness and advantages of the proposed processing method.

scholarly journals BeiDou-Based Passive Radar Vessel Target Detection: Method and Experiment via Long-Time Optimized Integration

2021 ◽  
Vol 13 (19) ◽  
pp. 3933
Author(s):  
Chuan Huang ◽  
Zhongyu Li ◽  
Mingyue Lou ◽  
Xingye Qiu ◽  
Hongyang An ◽  
...  
Keyword(s):  
Target Detection ◽  
Optimization Problem ◽  
Time Integration ◽  
Satellite System ◽  
Observation Time ◽  
Detection Performance ◽  
Passive Radar ◽  
Power Budget ◽  
Long Time ◽  
Range Migration

The BeiDou navigation satellite system shows its potential for passive radar vessel target detection owing to its global-scale coverage. However, the restrained power budget from BeiDou satellite hampers the detection performance. To solve this limitation, this paper proposes a long-time optimized integration method to obtain an adequate signal-to-noise ratio (SNR). During the long observation time, the range migration, intricate Doppler migration, and noncoherence characteristic bring challenges to the integration processing. In this paper, first, the keystone transform is applied to correct the range walk. Then, considering the noncoherence of the entire echo, the hybrid integration strategy is adopted. To remove the Doppler migration and correct the residual range migration, the long-time integration is modeled as an optimization problem. Finally, the particle swarm optimization (PSO) algorithm is applied to solve the optimization problem, after which the target echo over the long observation time is well concentrated, providing a reliable detection performance for the BeiDou-based passive radar. Its effectiveness is shown by the simulated and experimental results.

scholarly journals Maritime Moving Target Long Time Integration for GNSS-Based Passive Bistatic Radar

2018 ◽  
Vol 54 (6) ◽  
pp. 3060-3083 ◽  
Author(s):  
Debora Pastina ◽  
Fabrizio Santi ◽  
Federica Pieralice ◽  
Marta Bucciarelli ◽  
Hui Ma ◽  
...  
Keyword(s):  
Time Integration ◽  
Bistatic Radar ◽  
Moving Target ◽  
Long Time ◽  
Long Time Integration ◽  
Passive Bistatic Radar

scholarly journals Fast Implementation of Insect Multi-Target Detection Based on Multimodal Optimization

2021 ◽  
Vol 13 (4) ◽  
pp. 594
Author(s):  
Rui Wang ◽  
Yiming Zhang ◽  
Weiming Tian ◽  
Jiong Cai ◽  
Cheng Hu ◽  
...  
Keyword(s):  
Target Detection ◽  
Computational Cost ◽  
Detection Performance ◽  
Coherent Detection ◽  
Observation Data ◽  
Multimodal Optimization ◽  
Performance Simulation ◽  
Single Target ◽  
Parameter Search ◽  
Long Time

Entomological radars are important for scientific research of insect migration and early warning of migratory pests. However, insects are hard to detect because of their tiny size and highly maneuvering trajectory. Generalized Radon–Fourier transform (GRFT) has been proposed for effective weak maneuvering target detection by long-time coherent detection via jointly motion parameter search, but the heavy computational burden makes it impractical in real signal processing. Particle swarm optimization (PSO) has been used to achieve GRFT detection by fast heuristic parameter search, but it suffers from obvious detection probability loss and is only suitable for single target detection. In this paper, we convert the realization of GRFT into a multimodal optimization problem for insect multi-target detection. A novel niching method without radius parameter is proposed to detect unevenly distributed insect targets. Species reset and boundary constraint strategy are used to improve the detection performance. Simulation analyses of detection performance and computational cost are given to prove the effectiveness of the proposed method. Furthermore, real observation data acquired from a Ku-band entomological radar is used to test this method. The results show that it has better performance on detected target amount and track continuity in insect multi-target detection.

scholarly journals New Stable Closed Newton-Cotes Trigonometrically Fitted Formulae for Long-Time Integration

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
T. E. Simos
Keyword(s):  
Time Integration ◽  
Differential Method ◽  
Algebraic Order ◽  
Long Time ◽  
Long Time Integration ◽  
Differential Methods

The closed Newton-Cotes differential methods of high algebraic order for small number of function evaluations are unstable. In this work, we propose a new closed Newton-Cotes trigonometrically fitted differential method of high algebraic order which gives much more efficient results than the well-know ones.

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