An SDR-Based Real-Time Testbed for GNSS Adaptive Array Anti-Jamming Algorithms Accelerated by GPU

Xu, Hailong; Cui, Xiaowei; Lu, Mingquan

doi:10.3390/s16030356

Cited by 14 publications

(9 citation statements)

References 20 publications

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“…First, we solve the anti-interference vector normally according to the conventional spatial filtering algorithm, and find the spatial spectral function of the corresponding anti-jamming vector, Ffalse(ϕfalse)=bold-italicwHboldafalse(ϕfalse) where ϕ represents the angle of arrival, and boldafalse(ϕfalse) represents the steering vector of the signal [17,18]. boldafalse(ϕfalse)=false[1,e−j2πdsinϕλ,…,e−j2πfalse(M−1false)dsinϕλfalse]T as shown in Figure 18, d represents the element spacing, M represents the number of array element, λ represents the signal wavelength.…”

Section: Interference-nulling Control Algorithmmentioning

confidence: 99%

An Anti-Jamming Null-Steering Control Technique Based on Double Projection in Dynamic Scenes for GNSS Receivers

Wang

Chang

2019

Sensors

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When a global navigation satellite system (GNSS) receiver suppresses interference in a dynamic scene, the direction of the interference signal arriving at the receiver may change rapidly. The null formed by the spatial filtering based on the array antenna will become wider and shallower, and the anti-jamming performance will deteriorate. A null-steering control technique based on a dual projection algorithm is proposed in this paper, which can effectively increase the depth of the null. In this paper, the dynamic model between the interference and the receiver is established first. Based on the model, the rate of change of the arrival direction of the interference is analyzed, and the phenomenon of the spatial filtering null becoming wider and shallower is simulated and verified. Then, the double projection algorithm is introduced to effectively deepen the null. The simulation results show that the proposed method can effectively increase the null depth by 30 to 50 dB, which significantly improves the anti-jamming performance of the spatial filtering in dynamic scenes.

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Section: Interference-nulling Control Algorithmmentioning

confidence: 99%

An Anti-Jamming Null-Steering Control Technique Based on Double Projection in Dynamic Scenes for GNSS Receivers

Wang

Chang

2019

Sensors

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“…The DFT length M is set to 64. Within each frequency bin, the power inversion algorithm is adopted to generate

\overset{boldfalse~}{bold-italicw} false[m false]

[7].…”

Section: Simulationmentioning

confidence: 99%

Data‐oriented calibration method to reduce measurement bias in SFAP‐based GNSS receivers

Cui

2018

Electron. lett.

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Spatial frequency adaptive processing (SFAP) is the dimension-reduced alternative of spatial temporal adaptive processing (STAP), which can achieve good interference mitigation capability with less computational cost. However, just as STAP, the biases of the ranging code phase and carrier phase induced by the adaptive array restrict SFAP from being applied in high-precision global navigation satellite system receivers. To address this issue, a new data-oriented calibration method is proposed. Unlike previous bias reduction methods, which compensate the measurement biases with estimated values in the tracking loop, this method carries out calibration directly on the received data in the frequency domain, thus avoids modification to the rest parts of the receiver other than the SFAP module. The calibration value in each frequency bin is determined from the transfer function of the SFAP array. Simulations are performed on a practical array antenna designed by the high-frequency structure simulator software with high fidelity, and the results verify the effectiveness of this method.

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“…This SDRadar offers sufficient computational capability to support 4‐element antenna array up to 40Msps (mega samples per second). After that, a testbed [14] for anti‐jamming receiver was developed embedded with Space‐Time Adaptive Processing and Space‐Frequency Adaptive Processing. It further deploys the batch mode to fully take advantage of the parallelism resources provided by GPU.…”

Section: Introductionmentioning

confidence: 99%

Design of high‐speed software defined radar with GPU accelerator

Chun

Vishwakarma

et al. 2022

IET Radar Sonar & Navi

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Software defined radar (SDRadar) systems have become an important area for future radar development and are based on similar concepts to Software defined radio (SDR). Most of the processing like filtering, frequency conversion and signal generation are implemented in software. Currently, radar systems tend to have complex signal processing and operate at wider bandwidth, which means that limits on the available computational power must be considered when designing a SDRadar system. This paper presents a feasible solution to this potential limitation by accelerating the signal processing using a GPU to enable the development of a high speed SDRadar system. The developed system overcomes the limitation on the processing speed by CPU-only, and has been tested on three different SDR devices. Results show that, with GPU accelerator, the processing rate can achieve up to 80 MHz compared to 20 MHz with the CPU-only. The high speed processing makes it possible to run in real-time and process full bandwidth across the WiFi signal acquired by multiple channels. The gains made through porting the processing to the GPU moves the technology towards real-world application in various scenarios ranging from healthcare to IoT, and other applications that required significant computational processing. K E Y W O R D SGPU accelerator, signal processing, software defined radar | INTRODUCTIONThe applications of radar cover many broad and various areas, for example, the long-range airborne and weather surveillance, short-range target detection, target recognition and classification, etc. These applications have diverse demands, leading to the proliferation of highly specialised radar systems on the same platform, for example, ship, aircraft and others [1]. In addition, these platforms are also equipped with a number of other types of Radio Frequency (RF) sensors, such as communication and navigation systems. Many radar systems were implemented using hardware such as Field programmable gate arrays (FPGAs). However, a software implementation, which uses general-purpose processors is more desirable for its cost-effective, flexibility and fast development. Thus, the approach of sharing a platform with an SDRadar allows these requirements to be met [2]. However, increases in signal sampling rates (signal bandwidth) and the number of receiver channels in Multiple-Input and Multiple-Output (MIMO)/ distributed radar systems, mean there are more data need to be processed. For example, the universal software radio peripheral (USRP) family [3] has sampling rate from 20 MHz up to 160 MHz for 2 to 4 channels, the DigitizerNetbox (Dig-itizerNetbox) [4] can operate at 5 GHz with up to 16 channels, and also Ultra-Wide Band design [5]. Consequently, this increasing requirement in computational power becomes a key parameter to be considered for an SDRadar system.FPGA and GPU are commonly employed to accelerate computational processing. There are a number of differences between these two architectures, in terms of flexibility, powerThis is an open access...

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An SDR-Based Real-Time Testbed for GNSS Adaptive Array Anti-Jamming Algorithms Accelerated by GPU

Cited by 14 publications

References 20 publications

An Anti-Jamming Null-Steering Control Technique Based on Double Projection in Dynamic Scenes for GNSS Receivers

An Anti-Jamming Null-Steering Control Technique Based on Double Projection in Dynamic Scenes for GNSS Receivers

Data‐oriented calibration method to reduce measurement bias in SFAP‐based GNSS receivers

Design of high‐speed software defined radar with GPU accelerator

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