Design and Evaluation of a Low-Cost Passive Radar Receiver Based on IoT Hardware

Moser, Daniel; Tresoldi, Giorgio; Schüpbach, Christof; Lenders, Vincent

doi:10.1109/radar.2019.8835690

Cited by 8 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, using our current bandwidth of 15.36 MHz with the maximum integration time of 14 min would require almost 52 gigabytes of memory to store the raw data; however, field‐programmable gate array implementations of our autocorrelation‐based technique could perform on‐board processing and then store the final result to reduce the data volume by more than six orders of magnitude per measurement. Additionally, the development of application‐specific integrated circuit chips could perform passive sounding on the order of milliwatts of power (Moser et al., 2019; Wilson et al., 1991) (compared to up to 6 Watts of power drawn by the SDR) to reduce the power consumption by over an order of magnitude. Passive sounding also offers the flexibility to utilize electrically short antennas (Ellingson, 2005; Romero‐Wolf et al., 2016) with wide azimuthal beam patterns to reduce the overall system's size relative to active radars; this has led to interest from the planetary science community (Romero‐Wolf et al., 2015, 2016; Schroeder et al., 2016), where data, power, and size constraints are even more extreme.…”

Section: Discussionmentioning

confidence: 99%

Glaciological Monitoring Using the Sun as a Radio Source for Echo Detection

Peters

Schroeder

Chu

et al. 2021

Geophysical Research Letters

View full text Add to dashboard Cite

While active radio sounding is widely used for both airborne and ground-based surveys (Bell et al., 2011;Jenkins et al., 2006), these measurements are expensive and resource intensive when performed for multiple years, with fine temporal resolution, and on spatial scales greater than several kilometers. However, if radar sounders could perform these measurements without transmitting an electromagnetic pulse for echo detection, this would greatly reduce the power consumption, size, design complexity, and cost of the system. Although active systems could also be miniaturized, their low-power transmitted signal would experience geometric power fall-off, whereas a passive sounding technique exploiting a distant source would experience almost no geometric spreading loss and enable lower-resource systems in challenging environments (Schroeder et al., 2016). We have therefore developed

show abstract

Section: Discussionmentioning

confidence: 99%

Glaciological Monitoring Using the Sun as a Radio Source for Echo Detection

Peters

Schroeder

Chu

et al. 2021

Geophysical Research Letters

View full text Add to dashboard Cite

show abstract

“…Raspbian adalah sistem operasi (OS) didalamnya, tetapi ada berbagai varian ARM Linux lain yang dapat berjalan di atasnya. Memiliki berbagai model dengan antarmuka untuk kebutuhan yang berbeda [11]. Raspberry Pi 2 Model B (perangkat keras versi terbaru) memiliki 1 gigabyte (GB) memori akses acak (RAM), ARM quad-core 900MHz prosesor, empat antarmuka universal serial bus (USB), dan Port Ethernet, mini USB untuk catu daya dan antarmuka multimedia definisi tinggi (HDMI) untuk tampilan.…”

Section: B Raspberry Pi Sebagai Operation Systemunclassified

Rancang Bangun Sistem Pemantau Penerima Sinyal Automatic Dependent Surveillance-Broadcast (ADS-B) Berbasis Raspberry Pi dan Antena Ground Plane Sebagai Antena Penerima

Maharani

Soim

Fadhli

2022

protk

View full text Add to dashboard Cite

Currently, the technology used for aircraft monitoring is Radar technology. Radar's accuracy is hampered, however, by the fact that while an aircraft is airborne or in flight, it is frequently obscured by clouds, making it difficult to detect the aircraft. Due to constraints in radar technology, Automatic Dependent Surveillance-Broadcast technology was developed to improve the performance of air navigation security. Raspberry Pi, RTL-SDR, and LNA were used as the research method in designing the ADS-B signal receiver monitoring system. The 1090 MHz Ground Plane Antenna is preferable because it has a higher gain and a radiation pattern influenced by radial elements, allowing it to collect a considerably broader signal when combined with Raspberry Pi RTL-SDR and LNA. The monitoring results based on the test data are impressive, generating six aircraft simultaneously, indicating that dump 1090 is very effective at translating the aircraft's ADS-B signal data emitted every 0.5 seconds. ICAO, Altitude and Longitude information are clear Based on tests, the aircraft's ADS-B signal was identified between 4,000 and 38,000 feet over the maximum value, causing the signal to disappear and fail to decode the information. The maximum distance measured during the test was 49.7 kilometers from the elevated location where the ADS-B signal monitoring device was installed. The results of the first test were placed 10 meters above the ground. The result is significantly better than the short antenna position. This significantly improves the accuracy of the ADS-B signal monitoring system and the synchronization of information exchange through real-time monitoring.

show abstract

“…However, this system does not provide a practical solution for GPU‐based accelerators in real‐time processing. A Raspberry Pi base SDRadar system described in [9] built for passive radar including reference signal reconstruction and two‐dimensional FFT (size of 2048 × 512) with on‐board CPU and GPU. In this case, GPU only shows a slight improvement by 10% due to the sequential framework design which leads to overhead for single FFT.…”

Section: Introductionmentioning

confidence: 99%

“…Considering these advantages, many researchers have been working on GPU-based accelerator in various systems to deal with high computational process such as fast Fourier transform (FFT) [9] and correlation [10]. An early work [11] presents an analysis on correlation with GPU acceleration and demonstrates a speed-up factor of 15 compared to CPU only process.…”

mentioning

confidence: 99%

Design of high‐speed software defined radar with GPU accelerator

Chun

Vishwakarma

et al. 2022

IET Radar Sonar & Navi

View full text Add to dashboard Cite

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...

show abstract

Design and Evaluation of a Low-Cost Passive Radar Receiver Based on IoT Hardware

Cited by 8 publications

References 7 publications

Glaciological Monitoring Using the Sun as a Radio Source for Echo Detection

Glaciological Monitoring Using the Sun as a Radio Source for Echo Detection

Rancang Bangun Sistem Pemantau Penerima Sinyal Automatic Dependent Surveillance-Broadcast (ADS-B) Berbasis Raspberry Pi dan Antena Ground Plane Sebagai Antena Penerima

Design of high‐speed software defined radar with GPU accelerator

Contact Info

Product

Resources

About