Radar Network System Research Articles

Non‐cooperative game‐theoretic distributed power control technique for radar network based on low probability of intercept

Here, the problem of non-cooperative game-theoretic distributed power control is studied in a radar network system based on low probability of intercept (LPI) subject to the signal-to-interference-plus-noise ratio (SINR) constraint and the transmit power constraint of each radar, where all the radars in the network share the same frequency band. The objective is to improve the LPI performance by reducing the transmit power caused by some radars' SINRs over the specified threshold. First, a novel LPI performance-oriented utility function is defined as a metric to evaluate power control. Then, consider that radars in the network are self-interested to maximise their own utilities, the distributed power control problem is formulated as a non-cooperative game, and an iterative power control algorithm is proposed that converges quickly to the Nash equilibrium (NE) of the non-cooperative game. Finally, the existence and uniqueness of NE are proved analytically. Numerical simulation results are provided to demonstrate that, compared with other methods, the presented algorithm not only guarantees the minimum SINR requirements of all radars but also improves the LPI performance for radar network.

LPI radar network optimization based on geometrical measurement fusion

The power optimization problem and target assignment are investigated to improve low probability of interception (LPI) in a distributed radar network system. The target geometrical localization error and probability of detection are considered as QoS metrics of the network. We introduce geometrical fusion gain as a metric to fuse information for measuring target localization by multiple radars. The problem is then considered as an optimization challenge based on measurement error covariance ellipses, which satisfy detection and localization accuracy of the network. The LPI problem can be solved with two different objectives for the optimization as it minimizes the maximum power and cuts down on total power. Due to the combinatorial nonconvex and nonlinear nature of the optimization problems, relaxations are considered to make the problems more tractable. They can be efficiently solved by dividing them into two subproblems. Firstly, there is the power allocation in which a framework is proposed to compute minimum required power for a radar assignment scheme by applying a numerical method. Secondly, there is the radar assignment in which a heuristic assignment algorithm is suggested to acquire sufficient radar-to-target assignment based on calculated power. The simulation results show that the proposed algorithms considerably improve LPI performance while detection probability and target localization error constraints can be satisfied.

Joint Transmitter Selection and Resource Management Strategy Based on Low Probability of Intercept Optimization for Distributed Radar Networks

AbstractIn this paper, a joint transmitter selection and resource management (JTSRM) strategy based on low probability of intercept (LPI) is proposed for target tracking in distributed radar network system. The basis of the JTSRM strategy is to utilize the optimization technique to control transmitting resources of radar networks in order to improve the LPI performance while guaranteeing a specified target‐tracking accuracy. The weighted intercept probability and transmit power of radar networks is defined and subsequently employed as the optimization criterion for the JTSRM strategy. The resulting optimization problem is to minimize the LPI performance criterion of radar networks by optimizing the revisit interval, dwell time, transmitter selection, and transmit power subject to a desired target‐tracking performance and some resource constraints. An efficient and fast three‐step solution technique is also developed to solve this problem. The presented mechanism implements the optimal working parameters based on the feedback information in the tracking recursion cycle in order to improve the LPI performance for radar networks. Numerical simulations are provided to verify the superior performance of the proposed JTSRM strategy.

A Novel Data Fusion Algorithm to Combat False Data Injection Attacks in Networked Radar Systems

Networked radar systems are vulnerable to different types of attacks, including electronic countermeasure (ECM) jamming and false data injection (FDI) attack. Substantial research has concentrated on ECM jamming, which interferes with radar echoes between a radar and targets. However, FDI attack in which an attacker somehow replaces or modifies radars’ measurements, has rarely been considered. FDI attack is much stealthier than ECM jamming, making detection more difficult. In this paper, we take the first attempt to investigate the FDI attack's effects on a networked radar system. Further, we propose a novel data fusion algorithm to combat this attack. The proposed algorithm can dramatically reduce the attack's adverse effects, since it creatively introduces data's confidence factors into data fusion and adaptively decreases the fusion weights of the injected data. Numerical results verify the effectiveness of the proposed algorithm.

Cramér‐Rao Lower Bounds for Joint Target Parameter Estimation in FM‐Based Distributed Passive Radar Network with Antenna Arrays

AbstractTo avoid the disadvantages of the active radar which utilizes its own transmitter to emit electromagnetic radiations, passive radars use the signals readily available in the environment and can provide superior capabilities of stealth target detection, low probability of intercept, low cost, and robustness. This paper investigates the joint target parameter (delay and Doppler) estimation performance for a frequency modulation (FM)‐based distributed passive radar network (DPRN) system with antenna arrays. The DPRN system consists of multiple FM‐based illuminators of opportunity and multiple radar receivers, which are placed on moving platforms. First, we consider the scenario where the target state parameters are unknown, the maximum likelihood estimator is developed, and the log‐likelihood ratio of the received signal for a complex Gaussian extended target is derived. Then, the Cramér‐Rao lower bounds (CRLBs) on the Cartesian coordinates of target position and velocity are computed for a DPRN system with MT FM‐based transmitters of Q antenna elements and MR multichannel receivers of P antenna elements. Finally, numerical examples demonstrate that grouping the receiving antenna elements into properly sized arrays can reduce estimation errors. It is also shown that the joint CRLB is a function of signal‐to‐noise ratio, the number of receiving antenna elements, the properties of the transmitted FM waveform, and the relative geometry between the target and the DPRN architecture. The analytically closed‐form expressions for CRLB are an important performance metric in that they enable the optimal placement of radar receivers to improve the target parameter estimation performance.

Joint Node Selection and Power Allocation Strategy for Multitarget Tracking in Decentralized Radar Networks

Networked radar systems have been demonstrated to offer enhanced target tracking capabilities. An effective radar resource allocation strategy can efficiently optimize system parameters, leading to performance enhancements. In this paper, two critical but limited system resources are considered for optimization: the number of radar nodes and the transmitted power. In this scenario, a joint node selection and power allocation (JSPA) strategy is developed with the objective of tracking multiple targets. The proposed mechanism implements the optimal resource allocation based on the feedback information in the tracking recursion cycle in order to improve the worst-case tracking accuracy with multiple targets. The network architecture considered in this paper is decentralized so that communication requirements may be reduced while maintaining system robustness. Since the predicted conditional Cramer–Rao lower bound (PC-CRLB) provides a lower bound on the accuracy of the target state estimates conditional on the actual measurement realizations, it is more accurate than the standard posterior CRLB and is thus derived and used as an optimization criterion for the JSPA strategy. It is shown that the optimal JSPA is a two-variable nonconvex optimization problem. We propose an efficient two-step semidefinite programming based solution to solve this problem. Numerical results demonstrate the superior performance of the proposed strategy and the effectiveness of the proposed solution.

Power allocation for target detection in radar networks based on low probability of intercept: A cooperative game theoretical strategy

AbstractDistributed radar network systems have been shown to have many unique features. Due to their advantage of signal and spatial diversities, radar networks are attractive for target detection. In practice, the netted radars in radar networks are supposed to maximize their transmit power to achieve better detection performance, which may be in contradiction with low probability of intercept (LPI). Therefore, this paper investigates the problem of adaptive power allocation for radar networks in a cooperative game‐theoretic framework such that the LPI performance can be improved. Taking into consideration both the transmit power constraints and the minimum signal to interference plus noise ratio (SINR) requirement of each radar, a cooperative Nash bargaining power allocation game based on LPI is formulated, whose objective is to minimize the total transmit power by optimizing the power allocation in radar networks. First, a novel SINR‐based network utility function is defined and utilized as a metric to evaluate power allocation. Then, with the well‐designed network utility function, the existence and uniqueness of the Nash bargaining solution are proved analytically. Finally, an iterative Nash bargaining algorithm is developed that converges quickly to a Pareto optimal equilibrium for the cooperative game. Numerical simulations and theoretic analysis are provided to evaluate the effectiveness of the proposed algorithm.

Adaptive resource management algorithm for target tracking in radar network based on low probability of intercept

In this paper, a low probability of intercept (LPI) performance driven adaptive resource management algorithm for target tracking in a radar network is presented, where the radar network consists of a dedicated radar transmitter and multiple receivers. Firstly, the intercept probability for radar network systems is derived. Then, an adaptive resource management scheme based on LPI is proposed, in which a novel objective function for LPI performance is defined and minimized by optimizing the revisit interval, dwell time, and transmit power in radar networks to guarantee a specific target tracking accuracy with passive time difference of arrival and frequency difference of arrival cooperation. Numerical simulations demonstrate the superior performance of the proposed adaptive resource management scheme over other methods via Monte Carlo simulations.

Modified Cramér‐Rao lower bounds for joint position and velocity estimation of a Rician target in OFDM‐based passive radar networks

AbstractOwing to the increased deployment and the favorable range and Doppler resolutions, orthogonal frequency‐division multiplexing (OFDM)‐based L band digital aeronautical communication system type 1 (LDACS1) stations have become attractive systems for target surveillance in passive radar applications. This paper investigates the problem of joint parameter (position and velocity) estimation of a Rician target in OFDM‐based passive radar network systems with multichannel receivers placed on moving platforms, which are composed of multiple OFDM‐based LDACS1 transmitters of opportunity and multiple radar receivers. The modified Cramér‐Rao lower bounds (MCRLBs) on the Cartesian coordinates of target position and velocity are computed, where the received signal from the target is composed of dominant scatterer (DS) component and weak isotropic scatterers (WIS) component. Simulation results are provided to demonstrate that the target parameter estimation accuracy can be improved by exploiting the DS component. It also shows that the joint MCRLB is not only a function of the transmitted waveform parameters, target radar cross section, and signal‐to‐noise ratio but also a function of the relative geometry between the target and the passive radar networks. The analytical expressions of MCRLB can be utilized as a performance metric to access the target parameter estimation in OFDM‐based passive radar networks in that they enable the selection of optimal transmitter‐receiver pairs for target estimation.

A Novel Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking in an LPI Radar Network.

Radar networks are proven to have numerous advantages over traditional monostatic and bistatic radar. With recent developments, radar networks have become an attractive platform due to their low probability of intercept (LPI) performance for target tracking. In this paper, a joint sensor selection and power allocation algorithm for multiple-target tracking in a radar network based on LPI is proposed. It is found that this algorithm can minimize the total transmitted power of a radar network on the basis of a predetermined mutual information (MI) threshold between the target impulse response and the reflected signal. The MI is required by the radar network system to estimate target parameters, and it can be calculated predictively with the estimation of target state. The optimization problem of sensor selection and power allocation, which contains two variables, is non-convex and it can be solved by separating power allocation problem from sensor selection problem. To be specific, the optimization problem of power allocation can be solved by using the bisection method for each sensor selection scheme. Also, the optimization problem of sensor selection can be solved by a lower complexity algorithm based on the allocated powers. According to the simulation results, it can be found that the proposed algorithm can effectively reduce the total transmitted power of a radar network, which can be conducive to improving LPI performance.

Cramer-Rao Lower Bound Evaluation for Linear Frequency Modulation Based Active Radar Networks Operating in a Rice Fading Environment.

This paper investigates the joint target parameter (delay and Doppler) estimation performance of linear frequency modulation (LFM)-based radar networks in a Rice fading environment. The active radar networks are composed of multiple radar transmitters and multichannel receivers placed on moving platforms. First, the log-likelihood function of the received signal for a Rician target is derived, where the received signal scattered off the target comprises of dominant scatterer (DS) component and weak isotropic scatterers (WIS) components. Then, the analytically closed-form expressions of the Cramer-Rao lower bounds (CRLBs) on the Cartesian coordinates of target position and velocity are calculated, which can be adopted as a performance metric to access the target parameter estimation accuracy for LFM-based radar network systems in a Rice fading environment. It is found that the cumulative Fisher information matrix (FIM) is a linear combination of both DS component and WIS components, and it also demonstrates that the joint CRLB is a function of signal-to-noise ratio (SNR), target’s radar cross section (RCS) and transmitted waveform parameters, as well as the relative geometry between the target and the radar network architectures. Finally, numerical results are provided to indicate that the joint target parameter estimation performance of active radar networks can be significantly improved with the exploitation of DS component.

Large‐Volume Data Compression Using Compressed Sensing for Meteorological Radar

SUMMARYIn Japan, severe weather phenomena such as heavy rains and tornados sometimes cause meteorological disasters. In many cases, these are micro scale phenomena in the sense of spatial and temporal resolutions, which make it difficult to detect them with conventional meteorological radars due to their insufficient spatial and temporal resolutions. Therefore, we have been developing meteorological radars with high resolution and accuracy such as phased array radar (PAR) and Ku‐band broad band radar (BBR), and radar network systems consisting of multiple PARs and BBRs to realize further enhancement of the radar performance in terms of efficiency and accuracy. These high‐resolution radars, however, definitely produce large‐volume data, which is unacceptable in a current backbone information network. In order to solve this problem, in this paper, we tackle the compression of the large‐volume radar data by using Compressed sensing (CS), which can realize highly efficient data compression for sparse signals. When using CS, the radar data are compressed by projecting it onto a randomly generated subspace, and the compressed data are reconstructed by solving a simple ℓ1 optimization problem. We apply the CS‐based data compression scheme to measured radar reflectivity factor, and evaluate the relation between compression ratio and reconstruction accuracy. For the compression ratio of 0.3, rainfall rate calculated from the reconstructed radar reflectivity factor has a mean error of ‐0.89 mm/h with more than 30 dBZ precipitation.

Cramér–Rao bound analysis for joint target location and velocity estimation in frequency modulation based passive radar networks

With the wide spread availability and the favourable Doppler resolution, the frequency modulation (FM) commercial radio signals have become attractive for passive radar applications. Passive radar networks using multiple illuminators of opportunity and multichannel receivers have been shown to offer significant performance improvement owing to their advantage of signal and spatial diversities. In this study, the authors compute the joint Cramer–Rao lower bound (CRLB) for the target parameter (delay and Doppler) estimation error utilising FM commercial radio signals as illuminators of opportunity for passive radar network systems, where the non-coherent and coherent processing scenarios are considered. The numerical simulations are provided to show that the joint CRLB is not only a function of the transmitted waveforms but also of the relative geometry between the target and the passive radar networks for both non-coherent and coherent cases. The expressions for joint CRLB are an important performance metric for target parameter estimation in FM-based passive radar networks.

Transmitter Subset Selection in FM-Based Passive Radar Networks for Joint Target Parameter Estimation

Passive radar network systems utilize multiple transmitters of opportunity and multichannel receivers to offer remarkable performance improvement due to the advantage of signal and spatial diversities. The frequency modulation (FM) commercial radio signals have become attractive for passive radar applications owing to their wide-spread availability and the favourable Doppler resolution. In this paper, two transmitter subset selection schemes, balancing the trade-off between target parameter estimation accuracy and infrastructure utilization, are proposed for FM-based passive radar networks. In the first, the subset size of selected transmitters employed in the estimation process is minimized by effectively selecting a subset of transmitters, such that the required target parameter estimation mean-square error (MSE) threshold is attained. In the second, an optimal subset of transmitters of a predetermined size $\kappa $ is selected, such that the estimation MSE is minimized. These problems are formulated as a knapsack problem, where the coherent Cramer–Rao lower bound (CRLB) is used as a performance metric. Both transmitter subset selection schemes are tackled with greedy selection algorithms by successively selecting transmitters so as to minimize the performance gap between the CRLB and a predetermined MSE threshold or a predetermined subset size. Numerical simulations demonstrate that the problem of transmitter subset selection is not only a function of the transmitted waveforms but also of the relative geometry between the target and the passive radar network systems, which leads to reductions in both computational load and signal processing costs.

A frequency-sharing weather radar network system using pulse compression and sidelobe suppression

To mitigate damages due to natural disasters and abruptly changing weather, the importance of a weather radar network system (WRNS) is growing. Because radars in the current form of a WRNS operate in distinct frequency bands, operating a WRNS consisting of a large number of radars is very costly in terms of frequency resource. In this paper, we propose a novel WRNS in which multi-site weather radars share the same frequency band. By employing pulse compression with nearly orthogonal polyphase codes and sidelobe removal processing, a weather radar of the proposed frequency-sharing WRNS addresses inter-site and intra-site interferences simultaneously. Through computer simulations, we show the feasibility of the proposed system taking the performance requirement of a typical single weather radar into account.

Fuzzy Chance-constrained Programming Based Security Information Optimization for Low Probability of Identification Enhancement in Radar Network Systems

In this paper, the problem of low probability of identification (LPID) improvement for radar network systems is investigated. Firstly, the security information is derived to evaluate the LPID performance for radar net- work. Then, without any prior knowledge of hostile inter- cept receiver, a novel fuzzy chance-constrained program- ming (FCCP) based security information optimization scheme is presented to achieve enhanced LPID perform- ance in radar network systems, which focuses on minimiz- ing the achievable mutual information (MI) at interceptor, while the attainable MI outage probability at radar net- work is enforced to be greater than a specified confidence level. Regarding to the complexity and uncertainty of elec- tromagnetic environment in the modern battlefield, the trapezoidal fuzzy number is used to describe the threshold of achievable MI at radar network based on the credibility theory. Finally, the FCCP model is transformed to a crisp equivalent form with the property of trapezoidal fuzzy number. Numerical simulation results demonstrating the performance of the proposed strategy are provided.

Signal Processing for Tracking of Moving Object in Multi-Impulse Radar Network System

Indoor positioning systems (IPSs) have been discussed for use in entertainment, home automation, rescue, surveillance, and healthcare applications. In this paper, we present an IPS that uses an impulse radio-ultra-wideband (IR-UWB) radar network. This radar network system requires at least two radar devices to determine the current coordinates of a moving person. However, one can enlarge the monitoring area by adding more radar sensors. To track moving targets in indoor environments, for example, patients in hospitals or intruders in a home, signal processing procedures for tracking should be applied to the raw data measured using IR-UWB radars. This paper presents the signal processing method required for robust target tracking in a radar network, that is, an iterative extended Kalman filter- (IEKF-) based object tracking method, which uses two IR-UWB radars to measure the coordinates of the targets. The proposed IEKF tracking method is compared to the conventional extended Kalman filter (EKF) method. The results verify that the IEKF method improves the performance of 2D target tracking in a real-time system.

圧縮センシングを用いた気象用レーダの大容量観測データの圧縮

In Japan, severe weather phenomena such as heavy rains and tornados sometimes cause meteorological disasters. In many cases, these are micro scale phenomena in the sense of spatial and temporal resolutions, which make it difficult to detect them with conventional meteorological radars due to their insufficient spatial and temporal resolutions. Therefore, we have been developing meteorological radars with high resolution and accuracy such as phased array radar (PAR) and Ku-band broadband radar (BBR), and radar network systems consisting of multiple PARs and BBRs to realize further enhancement of the radar performance in terms of efficiency and accuracy. These high-resolution radars, however, definitely produce large-volume data, which is unacceptable in a current backbone information network. In order to solve this problem, in this paper, we tackle the compression of the large-volume radar data by using Compressed sensing (CS), which can realize highly efficient data compression for sparse signals. When using CS, the radar data is compressed by projecting it onto a randomly generated subspace, and the compressed data is reconstructed by solving a simple ℓ1 optimization problem. We apply the CS-based data compression scheme to measured radar reflectivity factor, and evaluate the relation between compression ratio and reconstruction accuracy. For the compression ratio of 0.3, rainfall rate calculated from the reconstructed radar reflectivity factor has a mean error of -0.89 mm/h with more than 30 dBZ precipitation.

Error Reduction by Reflected Signals in Automotive Radar Network Systems

Error Reduction by Reflected Signals in Automotive Radar Network Systems

Mutual information–based LPI optimisation for radar network

Radar network can offer significant performance improvement for target detection and information extraction employing spatial diversity. For a fixed number of radars, the achievable mutual information (MI) for estimating the target parameters may extend beyond a predefined threshold with full power transmission. In this paper, an effective low probability of intercept (LPI) optimisation algorithm is presented to improve LPI performance for radar network. Based on radar network system model, we first provide Schleher intercept factor for radar network as an optimisation metric for LPI performance. Then, a novel LPI optimisation algorithm is presented, where for a predefined MI threshold, Schleher intercept factor for radar network is minimised by optimising the transmission power allocation among radars in the network such that the enhanced LPI performance for radar network can be achieved. The genetic algorithm based on nonlinear programming (GA-NP) is employed to solve the resulting nonconvex and nonlinear optimisation problem. Some simulations demonstrate that the proposed algorithm is valuable and effective to improve the LPI performance for radar network.