Multistatic Passive Radar Research Articles

Multistatic Passive Radar for drone detection based Random Finite State

Considering the implication of radar sensors in our daily life and environment. Localizing and identifying drones are becoming a research with a greater focus in recent years. Consequently, when an unmanned aerial vehicle is used with bad intention, this can lead to a serious public safety and privacy probem. This study investigate pratical use of spectrum range for multistatic passive radar (MSPR) signal processing . Firstly, signal processing is performed after MSPR sensing detection range ,this include multipath energy detection, reference signal extraction, and receiving antenna configuration. Secondly, based on the MSPR nature, the reference signal is extracted and analyzed. In addition,taking into consideration the vulnerability of passive radar when comes to a moving target localization and real time tracking detection ,a novel method for spectrum sensing and detection which relies on Gaussian filter is proposed. The main goal is to optimize the use of the reference signal extracted with minimum interference as the shared reference signal in spectrum sensing. This will improve the system detection capability and spectrum access. Finally, a recursive method based on Bernoulli random filter is proposed, this takes consideration of drone’s present and unknown states based on time. Moreover, a system is developed meticulously to track and enhance detection of the target. A careful result of the experiment demonstrated that spectral detection can be achieved accurately even when the drone is moving while chasing its position. It shows that Cramer Rao lower error bounds remains significantly within 3% range.

Maritime Ship Joint Detection and Localization Using GNSS-Based Passive Multistatic Radar

Maritime Ship Joint Detection and Localization Using GNSS-Based Passive Multistatic Radar

A Novel Target Detection Method Based on Multi-Parameter Space for Mobile Passive Multistatic Radar

Deploying Passive Multistatic Radar (PMR) on mobile platforms provides covert and cost-effective monitoring over a large area, offering certain advantages in countermeasure. However, mobile PMR faces significant challenges, such as Doppler distortion and phase deviations. A multi-parameter space target detection method is proposed for mobile PMR to achieve target detection in three-dimensional environments. By estimating the Doppler Frequency Rate (DFR), applying bistatic range phase compensation, and implementing azimuth time integration, frame division, and data fusion, the detection accuracy and the Signal-to-Noise Ratio (SNR) are improved. Simulation results indicate that the proposed method significantly enhances the SNR and produces accurate detection results, demonstrating its efficacy.

Maritime moving target detection and localisation technique for Global Navigation Satellite Signals‐based passive multistatic radar

AbstractThis article puts forward a ship target detection and localisation technique for GNSS‐based passive multistatic radar. The approach capitalises on the large spatial diversity offered by navigation satellite constellations to provide maritime awareness using short integration time windows, thus bypassing the shortcomings suffered by approaches relying on long dwells typically considered in satellite‐based passive radar systems. The proposed approach entirely operates on the Cartesian plane and it is able to provide in a single stage the detection and localisation of the target in the surveyed area. Theoretical and simulated performance analysis are provided to illustrate as the proposed approach can outperform conventional two‐stages (i.e. bistatic detection then localisation) procedures, with particular regard to the case of significant radar cross section scintillation, likely experienced in the system under consideration. The effectiveness of the approach has been verified via experimental data acquired in a few scenarios of interest comprising opportunistic targets belonging to different types.

Robust Target Localization for Multistatic Passive Radar Networks

Passive multistatic radar networks localize a non-cooperative target exploiting the bistatic measurements associated with signals emitted by several transmitters of opportunity and acquired by multiple receivers. However, in realistic scenarios, it might happen that one or more receivers are damaged and/or under malicious attacks with a consequent degradation of the system performance. This paper proposes a procedure to reveal sensors that return anomalous measurements due to an attack or a failure. Specifically, we first detect such measurements by solving a binary hypothesis test, then we cancel them and estimate the final delays as well as the target position. The performance of the overall architecture is assessed using both synthetic data and real-recorded data.

Adaptive Channel Assignment for Maneuvering Target Tracking in Multistatic Passive Radar

In this paper, two adaptive channel assignment (CA) schemes are proposed for maneuvering target tracking (MTT) in multistatic passive radar. For a predetermined total number of channels w.r.t. each receiver, (a) the first scheme selects <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> channels to achieve the highest MTT accuracy; (b) the second scheme minimizes the number of channels while guaranteeing a required MTT performance. By utilizing the best-fitting Gaussian approximation, we derive the predicted conditional Cramér-Rao lower bound to evaluate the impact of CA on MTT performance, and formulate CA schemes as convex integer program problems, since channel variables are in binary form. The objective in the first CA problem is smooth, so we apply the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{2}$</tex-math></inline-formula> -box alternating direction method of multipliers to divide it into several simple subproblems and alternatively solve them in continuous domains without the introduction of relaxation error. The second CA problem may be infeasible when the required accuracy cannot be met with available channels. To tackle it, we introduce a linear rectification function w.r.t the performance constraint, and equivalently reformulate it as a feasible one. Then, a fast multi-start add-one-channel policy is designed to minimize the gap between the actual accuracy and the accuracy requirement. These algorithms offer considerable reductions in computational complexity compared with the branch and bound method and may achieve near-optimal performance. Simulation results demonstrate that these schemes can either achieve the highest MTT accuracy with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> channels or reduce the number of channels while guaranteeing MTT performance, compared with the traditional random channel allocation scheme.

Internet-of-Things Multistatic Passive Radar Station

This paper aims at presenting an Internet-of-things multistatic passive radar station having autonomous and field deployed capabilities. The station is wirelessly connected to a data server and requires, under expected circumstances, no maintenance whatsoever for the projected period of operation. The wireless connection is through radio waves over the Wi-Fi radio protocol making the station an internet-of-things device.&#x0D; A design of the autonomous station is disclosed. Different components are discussed together with their inter-relations. The requirements these components should satisfy, in accordance with the harsh field condition the station is subjected to, are established.&#x0D; The design is supported by three inventions registered at the Bulgarian Patent Office.

SkyWatch: A Passive Multistatic Radar Network for the Measurement of Object Position and Velocity

Quantitative three-dimensional (3D) position and velocity estimates obtained by passive radar will assist the Galileo Project in the detection and classification of aerial objects by providing critical measurements of range, location, and kinematics. These parameters will be combined with those derived from the Project’s suite of electromagnetic sensors and used to separate known aerial objects from those exhibiting anomalous kinematics. SkyWatch, a passive multistatic radar system based on commercial broadcast FM radio transmitters of opportunity, is a network of receivers spaced at geographical scales that enables estimation of the 3D position and velocity time series of objects at altitudes up to 80[Formula: see text]km, horizontal distances up to 150[Formula: see text]km, and at velocities to [Formula: see text][Formula: see text]km/s ([Formula: see text][Formula: see text]Mach). The receivers are designed to collect useful data in a variety of environments varying by terrain, transmitter power, relative transmitter distance, adjacent channel strength, etc. In some cases, the direct signal from the transmitter may be large enough to be used as the reference with which the echoes are correlated. In other cases, the direct signal may be weak or absent, in which case a reference is communicated to the receiver from another network node via the internet for echo correlation. Various techniques are discussed specific to the two modes of operation and a hybrid mode. Delay and Doppler data are sent via internet to a central server where triangulation is used to deduce time series of 3D positions and velocities. A multiple receiver (multistatic) radar experiment is undergoing Phase 1 testing, with several receivers placed at various distances around the Harvard–Smithsonian Center for Astrophysics (CfA), to validate full 3D position and velocity recovery. The experimental multistatic system intermittently records raw data for later processing to aid development. The results of the multistatic experiment will inform the design of a compact, economical receiver intended for deployment in a large-scale, mass-deployed mesh network. Such a network would greatly increase the probability of detecting and recording the movements of aerial objects with anomalous kinematics suggestive of Unidentified Aerial Phenomena (UAP).

Direct Target Joint Detection and Tracking Based on Passive Multi-Static Radar

Traditional target tracking is carried out based on the point measurements extracted from the radar resolution cells. This is not suitable for situations of low signal-to-noise ratio (SNR). In this paper, we aim to investigate the problem of the joint detection and tracking (JDT) of a target by directly using the received signals of passive multi-static radar without feeding the signals to matched filters. To this end, a novel likelihood function is proposed exploiting the statistical properties of coherent processing between the reference and surveillance signals. With such a likelihood function, the particle Bernoulli filter is employed to perform direct JDT (DJDT) of the target. A remarkable feature of the proposed method is that it is able to achieve satisfactory performance when the SNR of received signals is low. Furthermore, the proposed method cannot only achieve the existence and kinematic state of the target, but also the time-varying SNR of each receiver, which serves as an important input for sensor adjustment. The performance of the proposed method is verified via simulations.

Compound Preselector for Multistatic Passive Radar Employed in Remote Sensing Applications

Passive radars and multistatic passive radars in particular are gaining increasing attention. It is due to their effectiveness in detecting, tracking and identifying targets. Multistatic passive radars are used in remote sensing observations of the Earth’s ionosphere, meteors, space junk, etc. Another application is the detection and tracking of manmade objects and vehicles in the atmosphere or on the surface of the Earth. The current paper discusses a hardware design approach applicable to the front-end of a multistatic passive radar receiver, namely a compound preselector. The preselector is a very important part of the radio receiver in cases where there are strong neighbouring signals in the radio band of operation. Such strong signals may overload the receiver or the low noise preamplifier and render the whole system dysfunctional. This article elaborates on a two-component preselector having very high rejection ratio to nearby emitters, still keeping the receiver simple and reliable. The authors have registered several utility models on innovations in multistatic passive radars at Patent Office of Republic of Bulgaria.

Maritime Ship Target Imaging With GNSS-Based Passive Multistatic Radar

Maritime Ship Target Imaging With GNSS-Based Passive Multistatic Radar

Multistatic Passive Radar Target Detection Under Uncalibrated Receivers With Direct-Path Interference

This article examines the multistatic passive radar target detection problem under uncalibrated receivers with direct-path interference. To this aim, we consider a passive multistatic radar consisting of several transmitters nonoverlapping in frequency with a common bandwidth and several spatially separated receivers without exploiting the reference channel. For uncalibrated, multichannel and multifrequency receivers and in the presence of direct-path interference signal, we model the target detection problem as a composite hypothesis testing problem and apply the Rao framework to obtain a closed-form test statistic. Monte Carlo simulation results are provided to illustrate the performance of the proposed Rao detector and compare it with several well-known passive radar target detection algorithms. The results show that the proposed detector significantly outperforms its counterparts for different multistatic configurations in terms of detection probability and false alarm regulation as well.

Automatic Target Recognition Based on RCS and Angular Diversity for Multistatic Passive Radar

Asan important means in low-altitude airspace surveillance, passive radar has been widely studied. With the increasing complexity of the low-altitude environment, the urgent need for radar's capability of automatic target recognition (ATR) emerges. In the field of ATR for passive radar, the method based on radar cross section (RCS) is an important direction for the simplicity of feature extraction. In this article, we exploit the compensated quasi-echo-power (CQEP), i.e., quasi-RCS, to realize ATR in passive radar. We propose an ATR method on the basis of the angular diversity. First, we divide the angular space, which consists of four components of the incident angle and scattering angle of electromagnetic wave, into multiple subspaces. Then, we build a subrecognizer in each angular subspace according to the CQEP distribution characteristics. Each receiving station of the multistatic passive radar recognizes targets in an angular subspace corresponding to its observation and outputs a preliminary decision. Based on the preliminary decisions of all receiving stations, a voting strategy is adopted to make the final decision on the unknown target class in the fusion center. Experimental results based on real-life target data show that the proposed method has a significant improvement on the average correct recognition rate with fusion of three receiving stations, which validates the proposed method.

Order-Statistic Based Target Detection with Compressive Measurements in Single-Frequency Multistatic Passive Radar

This paper considers the problem of compressive target detection with direct-path interference (DPI) and clutter in a single-frequency network (SFN) based multistatic passive radar system (MS-PRS). Specifically, a measurement matrix is designed to jointly obtain compressive observations and remove the DPI and clutter. We first analyze a compressive subspace detector which assumes the target support is known. When the target supports cannot be accurately obtained, an order-statistic (OS) based detector, referred to as the OSOMP, is proposed by using the orthogonal matching pursuit (OMP) algorithm to estimate the target support, and then projecting the compressive observations into the estimated subspace. Since OMP applies an iterative ranking process to select the components/atoms of the dictionary, the OSOMP test variable is an order statistic, which has a non-convergent distribution. To cope with this problem, a modified test statistic for the OSOMP detector is presented and an analytical expression for the probability of false alarm is obtained. We further discuss the minimum number of iterations required by the OSOMP algorithm to achieve the desired probability of detection and false alarm. Numerical simulations are conducted to verify the theoretical analysis and illustrate the performance of the proposed detector relative to several benchmark detectors.

Automatic target recognition combining angular diversity and time diversity for multistatic passive radar

Automatic target recognition combining angular diversity and time diversity for multistatic passive radar

BeiDou-Based Passive Multistatic Radar Maritime Moving Target Detection Technique via Space–Time Hybrid Integration Processing

This article puts forward a BeiDou-based passive multistatic radar (PMR) maritime moving target (MMT) detection technique via space&#x2013;time hybrid integration (STHI) processing. Compared with passive bistatic radar (PBR), the utilization of multiple satellites provides an improvement in MMT detection performance, together with the capabilities of localization and velocity estimation. However, the multiple satellite transmitters cause the differences principally in bistatic range and Doppler centroid (DC) of the MMT. To integrate the PMR echoes, the biggest challenge is the two differences that need to be handled. In the proposed technique, first, the centroid-compensated keystone transform (CCKT) is proposed and applied to each PBR echo. It not only corrects range cell migration (RCM) but also equalizes the DC to the same. Then, the long-time integration is performed on each PBR echo, after which it is integrated into the range-Doppler frequency rate (DFR) domain. Finally, in order to settle the difference in bistatic range, an MMT position and velocity domain (i.e., the <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>&#x2013;<inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula>&#x2013;<inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> domain) is constituted. The obtained multiple range-DFR maps are projected to the <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>&#x2013;<inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula>&#x2013;<inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> domain, and then, the effective integration of multistatic echoes can be implemented. The final STHI result allows detecting the MMT reliably. Meanwhile, according to the 3-D position where the MMT is located in the <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>&#x2013;<inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula>&#x2013;<inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> domain, the MMT can be localized, and its velocity can be estimated simultaneously. In May 2021, we have successfully carried out the world&#x2019;s first BeiDou-based PMR MMT detection experiment, and the experimental results are given to prove the effectiveness of this technique.

Procedure for estimation of working zone of multistatic passive radar for solving aircraft observation problem

Procedure for estimation of working zone of multistatic passive radar for solving aircraft observation problem

Compressive Subspace Detectors Based on Sparse Representation in Multistatic Passive Radar Systems

This paper considers the problem of target detection by using compressive observations in a multistatic passive radar system (MS-PRS) consisting of one non-cooperative illuminator of opportunity, one reference channel, and multiple receivers. We first jointly design the measurement matrix for each receiver to obtain the compressive observations, thus making the echo signals at different receivers corresponding to the same target share the same target support indices (SSIs). Following this, a generalized likelihood ratio test (GLRT) based complex-valued compressive subspace detector (CSD) is provided given the prior of receiver noise variance. To further improve the detection performance, we develop a linear-fusion based CSD (LFCSD) by designing a set of weights at the fusion center. For the scenario of unknown noise variance, we continue to derive two GLRT-based unknown-noise CSDs (UNCSD). Specifically, we consider two cases based on whether the noise variance of each receiver is equal, referred to as the UNCSD1 and UNCSD2, respectively. Besides, we provide closed-form expressions of the probability of false alarm and detection of the proposed detectors. It is shown that the proposed UNCSD1 and UNCSD2 have constant false alarm rate (CFAR) properties. Finally, numerical simulation results show that the proposed detectors outperform the generalized coherence correlation (GCC) detector and the energy detector (ED) in low and moderate signal-to-noise ratio (SNR) regions.

A Centralized Ship Localization Strategy for Passive Multistatic Radar Based on Navigation Satellites

This letter focuses on ship target localization using a multistatic passive radar system based on navigation satellites opportunistic illuminators. In such systems, the typical approach to localize the targets is taking peripheral decisions at each bistatic channel to detect the target followed by bistatic ranges intersection. In contrast, here we consider a centralized approach in order to jointly detect and localize the ship target of interest. The proposed approach entirely operates in a Cartesian plane representing the surveyed maritime area and it is able to combine the signals pertaining the different bistatic links despite the variation of the e.m. response of the targets, typically occurring in the framework under consideration as the target is illuminated by multiple (and possibly widely separated) transmitters viewing angles. Moreover, the hypothesis of point-like targets is here replaced with the more realistic case of extended targets (i.e., occupying more than one resolution cell). The ship localization effectiveness of the method is proved via both synthetic and experimental datasets.

Passive Multistatic Radar Imaging of Vessel Target Using GNSS Satellites of Opportunity

The global navigation satellite system (GNSS)-based passive radar shows potential in permanent maritime surveillance. In this paper, the GNSS signals are exploited for vessel target imaging. From the obtained radar image, meaningful information about the vessel, such as its shape, position, length, and orientation can be extracted. In addition, the vessel is observed from different angles by spatially diverse GNSS satellites, and the multistatic geometry enables to enhance the imagery quality. The main drawback of GNSS-based passive radar stays in its limited power budget. And the inaccessible motion makes the noncooperative vessel smeared using conventional radar imaging methods. To address the problems, at first, each bistatic echo over a long observation time is integrated in range and Doppler (RD) domain after removing the two-dimensional migrations. The signal-to-noise ratio can be increased after the step. Then, with respect to a particular target velocity, the local Cartesian plane is constructed, and the multiple RD maps are projected and combined in the plane to obtain the multistatic image. In view of the inaccessibility of target kinematic parameters, such imaging processing is modeled as an optimization problem, where vessel&#x2019;s velocity is set as decision variable and the aim is to minimize the image entropy. Finally, particle swarm optimization (PSO) algorithm is applied to solve the optimization problem, after which a well-focused vessel image can be obtained. In May 2021, we have successfully carried out the world&#x2019;s first BeiDou-based passive radar maritime experiment, and effectiveness of the proposed method is verified against the experimental data.