Sparsity‐based STAP algorithm with multiple measurement vectors via sparse Bayesian learning strategy for airborne radar

Duan, Keqing; Wang, Zetao; Xie, Wu; Chen, Hui; Wang, Yongliang

doi:10.1049/iet-spr.2016.0183

Cited by 70 publications

(66 citation statements)

References 30 publications

(50 reference statements)

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“…Micro-distortion detection of the lidar scanning signal could be used to improve the lidar systems. Existing micro-distortion detection technologies of lidar scanning signal have the problems of long detection time, high energy consumption, and poor performance against interference [19,20]. To deal with these problems, a technique based on geometric statistics for micro-distortion detection of the lidar signal was proposed.…”

Section: Discussionmentioning

confidence: 99%

Micro-Distortion Detection of Lidar Scanning Signals Based on Geometric Analysis

et al. 2019

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When detecting micro-distortion of lidar scanning signals, current hardwires and algorithms have low compatibility, resulting in slow detection speed, high energy consumption, and poor performance against interference. A geometric statistics-based micro-distortion detection technology for lidar scanning signals was proposed. The proposed method built the overall framework of the technology, used TCD1209DG (made by TOSHIBA, Tokyo, Japan) to implement a linear array CCD (charge-coupled device) module for photoelectric conversion, signal charge storage, and transfer. Chip FPGA was used as the core component of the signal processing module for signal preprocessing of TCD1209DG output. Signal transmission units were designed with chip C8051, FT232, and RS-485 to perform lossless signal transmission between the host and any slave. The signal distortion feature matching algorithm based on geometric statistics was adopted. Micro-distortion detection of lidar scanning signals was achieved by extracting, counting, and matching the distorted signals. The correction of distorted signals was implemented with the proposed method. Experimental results showed that the proposed method had faster detection speed, lower detection energy consumption, and stronger anti-interference ability, which effectively improved micro-distortion correction.

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Section: Discussionmentioning

confidence: 99%

Micro-Distortion Detection of Lidar Scanning Signals Based on Geometric Analysis

et al. 2019

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“…where ξ c is the clutter power, and σ v is the velocity standard deviation. Consider the ICM case with σ v = 0.1 [15]. The other simulation parameters are same as simulation A.…”

Section: Performance Comparison With the Intrinsic Clutter Motionmentioning

confidence: 99%

A Grid-Less Total Variation Minimization-Based Space-Time Adaptive Processing for Airborne Radar

Wang

2020

IEEE Access

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Sparse recovery (SR) based space-time adaptive processing (STAP) has attracted much attention due to its small requirement of snapshots in the estimation of the clutter plus noise covariance matrix (CNCM). However, most of the existing SR STAP methods suffer from the grid mismatch effect of the dictionary matrix. In this paper, a novel grid-less total variation minimization (TVM) based STAP approach is proposed, which avoids the discretization of the spatial-temporal profile and possible mismatch of the spatialtemporal gird. The optimization problem is firstly introduced to estimate the clutter subspace steering vector by minimizing the defined atomic norm based on the TVM. Then the optimization problem is reformulated via utilizing the property of the radar space-time steering vector and approximation of the Bessel function. Finally, with a solution obtained by the optimization problem, a projection method is presented to obtain an accurate estimation of the CNCM. The proposed STAP method can be applied for both the side-looking and non-side-looking case. Numerical results validate its effectiveness compared with the other SR STAP methods.

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“…Section 3 provides the optimal configuration, without considering receiver constraints. However, in general, the UAV receiving platform is affected by its movement constraints and cannot achieve the conditions for optimal configuration within a short time [30]. Usually, the UAVs are far from the emitter; also, they are affected by the communication constraint and collision avoidance constraint.…”

Section: Uav Path Optimizationmentioning

confidence: 99%

Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach

Wang

Bai

et al. 2018

Applied Sciences

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In localization estimation systems, it is well known that the sensor-emitter geometry can seriously impact the accuracy of the location estimate. In this paper, time-difference-of-arrival (TDOA) localization is applied to locate the emitter using unmanned aerial vehicle (UAV) swarms equipped with TDOA-based sensors. Different from existing studies where the variance of measurement noises is assumed to be independent and changeless, we consider a more realistic model where the variance is sensor-emitter distance-dependent. First, the measurements model and variance model based on signal-to-noise ratio (SNR) are considered. Then the Cramer-Rao low bound (CRLB) is calculated and the optimal configuration is analyzed via the distance rule and angle rule. The sensor management problem of optimizing UAVs trajectories is studied by generating a sequence of waypoints based on CRLB. Simulation results show that path optimization enhances the localization accuracy and stability.

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Sparsity‐based STAP algorithm with multiple measurement vectors via sparse Bayesian learning strategy for airborne radar

Cited by 70 publications

References 30 publications

Micro-Distortion Detection of Lidar Scanning Signals Based on Geometric Analysis

Micro-Distortion Detection of Lidar Scanning Signals Based on Geometric Analysis

A Grid-Less Total Variation Minimization-Based Space-Time Adaptive Processing for Airborne Radar

Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach

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