Tutorial: Passive radar tutorial

Kuschel, Heiner; Cristallini, Diego; Olsen, Karl Erik

doi:10.1109/maes.2018.160146

Cited by 126 publications

(81 citation statements)

References 36 publications

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“…It has many advantages, such as resistant to jammers and does not need a dedicated frequency band. This radar is equipped with two antennas: The first antenna is the direct antenna that receives the direct signal, and the second antenna is the surveillance antenna that receives targets' echoes and multipath signals [1], [2]. The direct signal is reconstructed to detect targets' echoes [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

“…The direct signal is reconstructed to detect targets' echoes [3]- [5]. Many researches have been conducted studying this radar, such as studying and analyzing of signals of non-cooperative transmitters (e.g., Frequency Modulation (FM) radio, Global System for Mobile communication (GSM), Digital Video Broadcasting-Terrestrial (DVB-T), and Digital Audio Broadcasting (DAB)) [1], [2], [6]- [9], studying of the interference of the direct signal on the surveillance channel [10], [11], detection of "maneuvering/non-maneuvering" targets [12]- [14], and estimation of their parameters (e.g., Manuscript received June 20, 2020; revised September 7, 2020. Date of publication October 8, 2020.…”

Section: Introductionmentioning

confidence: 99%

“…Doppler frequency of a non-maneuvering target is estimated by applying the Maximum Likelihood method to the output of a bank of matched filters, which are tuned to different Doppler frequencies [1], [17]. But in the case of a maneuvering target, this way is ineffective because the velocity and direction of this target change non-linearly during small periods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Doppler Frequency Estimation for a Maneuvering Target Being Tracked by Passive Radar Using Particle Filter

Almanofi

Malki

Kazem

2020

J. commun. softw. syst. (Online)

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In this paper, we estimate Doppler frequency of a maneuvering target being tracked by passive radar using two types of particle filter, the first is “Maximum Likelihood Particle Filter” (MLPF) and the second is “Minimum Variance Particle filter” (MVPF). By simulating the passive radar system that has the bistatic geometry “Digital Video Broadcasting-Terrestrial (DVB-T) transmitter / receiver” with these two types, we can estimate the Doppler frequency of the maneuvering target and compare the simulation results for deciding which type gives better performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Doppler Frequency Estimation for a Maneuvering Target Being Tracked by Passive Radar Using Particle Filter

Almanofi

Malki

Kazem

2020

J. commun. softw. syst. (Online)

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“…Passive RADAR provides an attractive solution to multiple remote sensing issues including not relying on a dedicated emitter to illuminate targets bathed in electromagnetic smog, 1 and being able to detect signals returned by a cooperative target whose signature is representative on a physical quantity (identification, temperature, strain ...). 2 The basic signal processing principle of passive RADAR is to collect on the one hand a reference signal of the pseudo-random signal -most passive radar systems use existing commercial communication or navigation signals with appropriate random properties -illuminating the target, and on Further author information: send correspondence to J.-MF, E-mail: jmfriedt@femto-st.fr the other hand the surveillance signal reflected by the target, possibly frequency shifted by a Doppler offset frequency if the target is moving 3 (Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

Passive radar for measuring passive sensors: direct signal interference suppression on FPGA using orthogonal matching pursuit and stochastic gradient descent

Friedt

Feng

Chrétien

et al. 2019

Multimodal Sensing: Technologies and Applications

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Passive wireless sensors provide an attractive technology in niche applications where battery powered sensors are not applicable. Surface acoustic wave technology provides an optimum implementation of passive wireless transducers acting as cooperative targets to short range radar systems: the signal carrying the information is delayed beyond clutter, with a piezoelectric substrate converting the electromagnetic wave to an acoustic wave whose properties are dependent on the environment. We have tackled the issue of short range radar certification by considering a passive radar approach in which existing non-cooperative radiofrequency sources are used to power the sensor, and the reader is made solely of passive receivers aimed at recording the reference signal generated by the non-cooperative source and the backscattered signal returned by the sensor. A passive radar approach only requires a cross-correlation between the reference and surveillance signals to identify the time delay between the incoming and backscattered signals and hence the recovery of the physical quantity sensed by the acoustic transducer through acoustic velocity modulation. Practical sources do exhibit some short term correlation. Hence, strong copies of the reference signal with delays shorter than the one introduced by the sensor must be canceled by the receiver to allow for the weak sensor response to be extracted. This classical problem is called Direct Signal Interference (DSI) suppression. In a post-processing approach with little computation time limitation, this problem is solved using a least square error optimization approach. In the context of real time sensor measurement using a Field Programmable Gate Array (FPGA) implementation, data recording, frequency transposition and decimation are readily implemented in the gate array matrix. DSI removal appears as the limiting factor for a full FPGA implementation of the short range passive radar reader. We address the challenge by the iterative process of DSI suppression using Orthogonal Matching Pursuit (OMP) algorithm, and additionnally consider the FPGAfriendly Stochastic Gradient Descent (SGD) approach to try to recover DSI coefficients from a pipelined algorithm using streams of measurement data.

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“…Passive radar, employing non-cooperative illuminators, has attracted increasing interests in recent years [1][2][3][4][5][6][7][8][9][10][11][12]. It has many obvious advantages over traditional active radars, such as low-cost, feasibility of various illuminators and immune to the anti-radiation missiles [1]. In existing literature, illuminators of opportunity for passive radar are generally categorized into four groups: broadcast signals (DVB-T, FM, DAB, etc.)…”

Section: Introductionmentioning

confidence: 99%

PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid

Zhu

Li²,

Pan

et al. 2019

Electronics

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Many new challenges are faced by the PBR (passive bi-static radar) employing non-cooperative radar illuminators. After the CFAR (constant false alarm) processor, the appearance of the amount of false alarm clutter points impacts the following tracing performance. To enhance the PBR tracing performance, we consider to reduce these clutter points before target tracing as soon as possible. In this paper, we propose a PBR clutter suppression algorithm based on dilation morphology of non-uniform grid. Firstly, we construct the non-uniform polar grid based on the acquisition geometry of PBR. Then, with the help of the grid platform, we separate the false alarm clutter points based on the dilation morphology. To efficiently operate the algorithm, we build up its parallel iteration scheme. To verify the performance of the proposed algorithm, we utilize both simulated data and field data to do the experiment. Experimental results show that the algorithm can effectively suppress most of the clutter points. Besides, we respectively combine the proposed suppression algorithm with two typical tracking algorithms to test the performance. Experimental results reveal that the compound tracing algorithm outperforms the traditional one. It can enhance the PBR tracing performance, reduce the occurrence probability of false tracks and meanwhile save time.

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Tutorial: Passive radar tutorial

Cited by 126 publications

References 36 publications

Doppler Frequency Estimation for a Maneuvering Target Being Tracked by Passive Radar Using Particle Filter

Doppler Frequency Estimation for a Maneuvering Target Being Tracked by Passive Radar Using Particle Filter

Passive radar for measuring passive sensors: direct signal interference suppression on FPGA using orthogonal matching pursuit and stochastic gradient descent

PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid

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