Illuminators Of Opportunity Research Articles

Convex Optimization-Based Power Allocation Strategies for Target Localization in Distributed Hybrid Non-Coherent Active-Passive Radar Networks

To more effectively exploit the omnipresent public non-cooperative illuminators of opportunity (IOs) to improve the low probability of intercept (LPI) performance, a power allocation strategy is developed for target localization in distributed hybrid non-coherent active-passive radar networks. The core of the strategy is to optimize each active radar power allocation to minimize the total intercepted power for the hostile intercept receiver in view of power constraints, while achieving the predetermined collaborative target localization accuracy with the assistance of public non-cooperative IOs. Considering the timing uncertainty of public non-cooperative IOs, the localization performance is determined by joint CramrRao lower bound (CRLB) of the target position and the time-of-arrival (TOA) of the direct-path in a distributed passive radar network. Then, the Bayesian CRLB (BCRLB) is derived to evaluate collaborative target localization accuracy, adopted as the optimization criterion for the power allocation strategy. Furthermore, the optimization problem of the power allocation strategy is strictly proved to be a convex optimization, which implies that the global optimal lower bound can be easily obtained. By introducing a semi-definite matrix, the optimization problem is subsequently converted to semi-definite programming (SDP), resolved by the exiting classical algorithm directly. Finally, the simulation results verify the theoretical analysis and the LPI performance of the proposed strategy which outperforms existing strategies.

Moving Target Detection Using GNSS-Based Passive Bistatic Radar

GNSS (global navigation satellite system)-based passive bi-static radar (PBR) has many advantages benefiting from the bi-static configuration and the excellent properties of the GNSS signals. In this paper, the possibility of moving target indication (MTI) using a GNSS-based PBR is analyzed and illustrated by experiments. The power budget is firstly evaluated to investigate the capabilities and limitations of the GNSS-based PBR. Due to the low power density of GNSS signals, long-time integration is required to achieve long-range surveillance. In our previous work, a Radon Fourier Transform (RFT)-based long-time integration algorithm has been proposed for GNSS-based PBR. However, the RFT-based methods are computationally inefficient. In this paper, a novel MTI algorithm based on high frame rate image sequence (HFRIS) is introduced to the GNSS-based PBR. Benefiting from the avoiding of the iterative multi-dimensional parameter searching, it has a higher computational efficiency than the RFT-based methods. At the same time, the possibility of multi-static positioning for moving targets using the GNSS-based PBR is analyzed. It is shown that the absolute position of the target in three dimensions could be determined if at least 3 satellites are utilized to measure the range delay of the target return. To confirm our analysis, experiments are conducted which detects the airplanes using the GPS signals as illuminators of opportunity. The collected data are processed by both the RFT-based method and the HFRIS-based method. Both methods have successfully detected the airplane target at a distance of about 5km, and the HFRIS-based method shows a far better performance in terms of processing efficiency. The multi-static positioning experiment is also conducted, and the estimated position of the target is well consistent with the values acquired by the flight record, which shows the potential of the GNSS-based PBR on multi-static operations.

5G Network-Based Passive Radar

This article presents the pioneering results of successful target detection using a fully operational and cooperative 5G cellular network as a source of illumination in a passive radar system. The electromagnetic wave generated by the real-life base station allowed for remote sensing of a moving car in bistatic geometry, which shows the feasibility of passive radar in which new generation radio networks serve as an illuminator of opportunity. This article describes passive radar principles, the theoretical fundamentals of the 5G signal structure, and the proposed processing for 5G network-based passive radar. Also, the 5G passive radar demonstrator is described, and the trial results show the possibilities and limitations of using new radio standards in the context of passive radars.

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.

Co-Channel Interference Suppression for LTE Passive Radar Based on Spatial Feature Cognition.

Passive radars based on long-term evolution (LTE) signals suffer from sever interferences. The interferences are not only from the base station used as the illuminator of opportunity (BS-IoO), but also from the other co-channel base stations (CCBS) working at the same frequency with the BS-IoO. Because the reference signals of the co-channel interferences are difficult to obtain, cancellation performance degrades seriously when traditional interference suppression methods are applied in LTE-based passive radar. This paper proposes a cascaded cancellation method based on the spatial spectrum cognition of interference. It consists of several cancellation loops. In each loop, the spatial spectrum of strong interferences is first recognized by using the cyclostationary characteristic of LTE signal and the compressed sensing technique. A clean reference signal of each interference is then reconstructed according to the spatial spectrum previously obtained. With the reference signal, the interferences are cancelled. At the end of each loop, the energy of the interference residual is estimated. If the interference residual is still strong, then the cancellation loop continues; otherwise it terminates. The proposed method can get good cancellation performance with a small-sized antenna array. Theoretical and simulation results demonstrate the effectiveness of the proposed method.

Improved probability hypothesis density filter for multi‐target tracking of non‐cooperative bistatic radar

Non-cooperative bistatic radar refers to the passive bistatic radar using a non-cooperative radar as the illuminator of opportunity. Limited by the non-cooperation and bistatic configuration, multi-target tracking of the non-cooperative bistatic radar is confronted with challenges of low detection probability and high clutter. Based on the sequential Monte Carlo implementation of probability hypothesis density (PHD) filter, this study proposes an improved PHD (I-PHD) filter. Similar to the tracking gate in data association, whether each candidate target is detected at each time is determined and recorded, and the sequential probability ratio test distinguishes a real target and false target based on this record. To distinguish miss detection and target death, a new survival probability dependent on target position is defined, and the survival probability of a specific target drops rapidly when it is located near the boundary and moves outwards. To label targets and extract their states, a new method of track identification and state estimation is proposed. The simulation results illustrate that the proposed I-PHD filter can effectively track multiple targets of non-cooperative bistatic radar under low detection probability and high clutter and has superior performance than competing methods.

Parasitic Surveillance Potentialities Based on a GEO-SAR Illuminator

Synthetic aperture radar systems operating with satellites in geosynchronous orbits (GEO-SAR) can provide a permanent coverage of wide specific areas of the Earth’s surface. As well as for primary applications in remote sensing areas such as soil moisture and deformation monitoring, the wide availability of the signal emitted by a GEO-SAR on a regional scale makes it an appealing illuminator of opportunity for bistatic radars. Different types of receiving-only devices located on or near the Earth could exploit the same signal source, noticeably already conceived for radar purposes, for applications in the framework of both military and civil surveillance. This paper provides an overview of possible parasitic applications enabled by a GEO-SAR illuminator in different operative scenarios, including aerial, ground and maritime surveillance. For each selected scenario, different receiver configurations are proposed, providing an assessment of the achievable performance with discussions about the expected potentialities and challenges. This research aims at serving as a roadmap for designing parasitic systems relying on GEO-SAR signals, and also aims at extending the net of potential users interested in investing in GEO-SAR missions.

Fusing Measurements from Wi-Fi Emission-Based and Passive Radar Sensors for Short-Range Surveillance

In this work, we consider the joint use of different passive sensors for the localization and tracking of human targets and small drones at short ranges, based on the parasitic exploitation of Wi-Fi signals. Two different sensors are considered in this paper: (i) Passive Bistatic Radar (PBR) that exploits the Wi-Fi Access Point (AP) as an illuminator of opportunity to perform uncooperative target detection and localization and (ii) Passive Source Location (PSL) that uses radio frequency (RF) transmissions from the target to passively localize it, assuming that it is equipped with Wi-Fi devices. First, we show that these techniques have complementary characteristics with respect to the considered surveillance applications that typically include targets with highly variable motion parameters. Therefore, an appropriate sensor fusion strategy is proposed, based on a modified version of the Interacting Multiple Model (IMM) tracking algorithm, in order to benefit from the information diversity provided by the two sensors. The performance of the proposed strategy is evaluated against both simulated and experimental data and compared to the performance of the single sensors. The results confirm that the joint exploitation of the considered sensors based on the proposed strategy largely improves the positioning accuracy, target motion recognition capability and continuity in target tracking.

Passive MIMO Radar Detection with Unknown Colored Gaussian Noise

The target detection of the passive multiple-input multiple-output (MIMO) radar that is comprised of multiple illuminators of opportunity and multiple receivers is investigated in this paper. In the passive MIMO radar, the transmitted signals of illuminators of opportunity are totally unknown, and the received signals are contaminated by the colored Gaussian noise with an unknown covariance matrix. The generalized likelihood ratio test (GLRT) is explored for the passive MIMO radar when the channel coefficients are also unknown, and the closed-form GLRT is derived. Compared with the GLRT with unknown transmitted signals and channel coefficients but a known covariance matrix, the proposed method is applicable for a more practical case whenthe covariance matrix of colored noise is unknown, although it has higher computational complexity. Moreover, the proposed GLRT can achieve similar performance as the GLRT with the known covariance matrix when the number of training samples is large enough. Finally, the effectiveness of the proposed GLRT is verified by several numerical examples.

Passive Radar Transmitter Localization Using a Planar Approximation

In this article, we review and compare different methods for the localization of an illuminator of opportunity for passive radar systems employing cooperative targets. For assessment and comparison, we use numerical simulations and real-world radar measurements. To achieve reliable operation under real-world conditions, a correct and robust association of the bistatic radar target observations with ground truth information is necessary. We introduce a novel solution to this issue with an efficient preprocessing algorithm based on a planar approximation.

Passive Bistatic Radar Detection Performance Prediction Considering Antenna Patterns and Propagation Effects

This article deals with the problem of detection performance prediction for passive bistatic radars that use illuminators of opportunity, such as radio or television transmitters. First, a simplistic free-space propagation model is introduced. Next, the effects of the transmitter and receiver antenna patterns are analyzed. Then, the influence of the pattern-propagation factor is investigated, considering the elevation patterns, multipath, and diffraction. The performance prediction of the free-space model and a more realistic model with antenna patterns and propagation effects are compared. The variation of the echo power level caused by the propagation effects predicted in the simulations is verified with real data acquired with an FM-based passive radar. The presented approach allows for a more realistic prediction of the passive radar performance to be achieved in comparison with a simple free-space propagation assumption. Some examples of passive radar performance optimization using the introduced methodology are presented.

2-D PBR Localization Complying With Constraints Forced by Active Radar Measurements

A new algorithm for passive bistatic radar (PBR) localization is proposed via joint exploitation of multiple illuminators of opportunity and measurements gathered by a colocated active radar. Ad-hoc constraints within the localization process are bestowed accounting for both a priori information on the PBR receive antenna main-beam size and the uncertainty characterizing active radar data, i.e., the root mean square error (RMSE) of range/bearing measurements. Hence, the estimation task is cast as an elliptic positioning problem, according to the constrained least squares framework. The resulting non-convex optimization problem is globally solved providing a closed-form estimate in Cartesian coordinates. The performance of the proposed estimator, in terms of RMSE, exhibits sensible improvement with respect to counterparts. The proposed technique is also applied to the case of a dynamic scenario for a bi-sensor surveillance system, where the active rotating platform acquires measurements at each scanning period. The results show the localization performance improvement achieved when the system is complemented with a PBR staring in a specific search sector of interest.

Experimental Seaborne Passive Radar.

Passive bistatic radar does not emit energy by itself but relies on the energy emitted by illuminators of opportunity, such as radio or television transmitters. Ground-based passive radars are relatively well-developed, as numerous demonstrators and operational systems are being built. Passive radar on a moving platform, however, is a relatively new field. In this paper, an experimental seaborne passive radar system is presented. The radar uses digital radio (DAB) and digital television (DVB-T) for target detection. Results of clutter analysis are presented, as well as detections of real-life targets.

Experimental Verification of the Concept of Using LOFAR Radio-Telescopes as Receivers in Passive Radiolocation Systems.

The paper presents a new idea of using a low-frequency radio-telescope belonging to the LOFAR network as a receiver in a passive radar system. The structure of a LOFAR radio-telescope station is described in the context of applying this radio-telescope for detection of aerial (airplanes) and space (satellite) targets. The theoretical considerations and description of the proposed signal processing schema for the passive radar based on a LOFAR radio-telescope are outlined in the paper. The results of initial experiments verifying the concept of a LOFAR station use as a receiver and a commercial digital radio broadcasting (DAB) transmitters as illuminators of opportunity for aerial object detection are presented.

Passive MIMO radar detection in the presence of clutter or multi‐path without reference channel

Target detection problem in a multiple-input multiple-output (MIMO) radar which includes non-cooperative illuminators of opportunity (IOs) and spatially distributed receivers is discussed. The receivers utilize only one processing channel and discard the difficulties in providing an ideal version of the transmitted waveforms. The signal model, in addition to the receiver noise and the possible echo from the target of interest, considers the direct-path and clutter/multi-path interferences (DPI and MPI). A Rao test and an approximated GLR test detectors are proposed to solve the problem and an innovative recursive algorithm is proposed to simultaneously estimate the nuisance parameters in the test. Then, by regarding some matrix properties in the proposed Rao detector expression, a low complexity version of the Rao detector is proposed. Besides, two detectors based on Rao and GLR principles in the case of active MIMO radar are introduced as the performance upper-bounds for the proposed detectors. The convergence and suitable performance of the proposed detectors are verified by numerical simulations.

Software Phase Correction Technique for Passive Radar

A traditional radar actively transmits pulses and receive the corresponding echoes. By computing the time taken to receive the echoes the system is able to detect targets and estimate their ranges. However, mainly for military applications, an active radar has a major drawback: it can be detected by the enemy since it radiates electromagnetic pulses. As such, there is currently high research and development activity in the field of passive radar systems. In this alternative scenario, the radar does transmit any signal. Instead, it uses signals already present in the environment, such as TV, radio broadcasts and satellite digital video broadcast as transmitters of opportunity. By measuring the time difference between the signal received by the transmitter of opportunity and the signal echoed by the targets, the radar can detect targets and estimate their ranges. In scenarios which use very low power illuminators of opportunity, such as satellite DVB-S or GPS, relatively long integration times are required in order to obtain reasonable values of signal to noise ratio. This implies that, besides frequency correction, the coherency of local oscillators in the receivers need to be maintained during long time intervals, which can be very difficult. The paper presents a novel phase correction technique for passive radar which uses targets of opportunity, already present in the target area, as references to maintain the coherency of the oscillators for all the integration interval. The proposed methodology is quite simple and enables the use of low-cost hardware with independent oscillators for the reference and surveillance channels which can be geographically distributed. The obtained results illustrate the effectiveness and applicability of the method.

An Overview of Parametric Modeling and Methods for Radar Target Detection With Limited Data

This article provides a survey of recent results on exploiting parametric auto-regressive (AR) models for adaptive radar target detection. Specifically, three types of radar systems are considered, including phased-array radar with multiple co-located transmitters and receivers, distributed multi-input multi-output (MIMO) radar with widely and spatially separated transmitters and receivers, and passive radar which uses existing sources as illuminators of opportunity (IOs). These radar systems are of significant interest for a wide range of military and civilian applications. For each of the three types of radars, we discuss how AR processes can be employed to succinctly model the underlying signal correlation and efficiently estimate it from limited data, thus enabling effective target detection in complex non-homogeneous environments when training data is limited. We illustrate the performance of such parametric model assisted detectors relative to conventional non-parametric approaches by using computer simulated and experimental data.

Using DTMB‐Based Passive Radar for Small Unmanned Aerial Vehicle Detection

There is inevitable polarization angle deviation between the target echo signal and the direct path signal of illuminator of opportunity (IO) in passive radar. In order to investigate the potential performance loss in target detection induced by the random deviation, small unmanned aerial vehicle (UAV) detection experiments with digital television terrestrial multimedia broadcasting‐ (DTMB‐) based passive radar are conducted in this paper. Experimental results show that the polarization angles of the clutter signal and target echo signal are inconsistent. When the polarization diversity technology is used to suppress the clutter signal, the processing performance of the target echo signal may be reduced. On the premise that clutter is effectively suppressed by the processing algorithm, polarization synthesis can maximize the target echo signal processing gain. The effectiveness of the target localization algorithm combining time difference of arrival (TDOA) and direction of arrival (DOA) is also verified with polarization diversity reception in this paper.

Maritime Target Localization From Bistatic Range Measurements in Space-Based Passive Radar

In this article, localization of a maritime target using a passive multistatic radar system, consisting of a single receiver and a number of satellites as its transmitters, is investigated. The set of communication satellites and also the global navigation satellite system (GNSS), including GPS, Glonass, and Galileo, are employed as illuminators of opportunity. In the receiver, the reflected signals from the target along with the direct signal from the satellites are employed to extract a set of bistatic range measurements. Leveraging these measurements and the satellite’s position information, a novel localization method considering zero height of maritime target and appropriate combination of the measurement signals is presented. The proposed method is efficient for localization of maritime targets in cases with arbitrary bistatic range accuracy. The results of numerical simulations confirm the efficiency of the proposed method and its superior performance in comparison with the state-of-the-art methods.

Multitarget Detection in Passive MIMO Radar Using Block Sparse Recovery

In this paper, we develop a new algorithm for centralized target detection in passive MIMO radar (PMR) using sparse recovery technique. PMRs use a network of receivers and illuminators of opportunity to detect and localize targets. We consider a widely separated PMR network assuming the availability of reference channels. We first transform the collected information of all receivers to a common space and combine them to attain a unified model. The problem of target detection in the extracted model is equal to a block sparse recovery problem. Since employing the generic sparse recovery tools are impractical due to the ultra-large dimension of the sensing matrix, we exploit the structure of the involving matrices and propose a very efficient distributed algorithm which extracts all scatterers, including targets and clutter simultaneously with a unified procedure. The proposed algorithm is highly efficient, and it does not require a high bandwidth link to transfer raw data from nodes to the fusion center. Moreover, the algorithm inherently benefits from parallel processing and distributes the extensive computations among receivers. Our simulation results demonstrate that the proposed algorithm outperforms the popular PMR detection algorithm, especially in the presence of interfering targets and any strong clutter residue.