Power resource allocation scheme for distributed MIMO dual-function radar-communication system based on low probability of intercept

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.

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.

Joint LPI waveform and passive beamforming design for FDA-MIMO-DFRC systems

The purpose of this paper is to compare the Low Probability of Intercept (LPI) performance achievable with optical and RF systems (specifically, millimeter wave systems). The specific case of ground to space communications is considered. The LPI performance measure used in the comparison is the Circular Equivalent Vulnerability Radius (CEVR). Using the millimeter wave propagation model of Crane and Blood, a model for computing the CEVR of a millimeter wave earth to space link is described. We then show how the same model can be used to calculate the CEVR for optical systems using the optical propagation model developed by one of the authors (East). Optimal methods of intercepting frequency hopping millimeter wave systems and pulse position modulation (PPM) optical systems are also described.

This paper investigates the problem of optimal power allocation for radar network to improve the system low probability of intercept (LPI) performance. We first introduce radar network signal model, and derive Schleher intercept factor for radar network. Then, with the defined target threatening level, two optimal power allocation algorithms are proposed. The first one is to optimize transmitting power allocation with a predefined mutual information (MI) threshold, and the second one is to find the optimal power allocation for a given minimum mean-square error (MMSE) threshold. There is a one-to-one correspondence between target threatening level and MI/MMSE threshold, for we need different quantity of target information and estimation accuracy in the condition of different target threatening level. Additionally, the nonlinear programming based genetic algorithm (NPGA) is employed to solve the nonlinear and constrained optimization problems. Simulations demonstrate that these two algorithms lead to the same solution under the same system constraints and can improve LPI performance for radar network effectively.

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.

To 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.

The low probability of intercept (LPI) radar waveform is reflected in a lower pulse power and complex structure of the pulse series. The previous one ensures that it avoids being intercepted by the electronic measures system (ESM) carried by the goal; the latter makes it difficult for ESM to separate and identify. As for pulse position jitter pulse array, because of pulse position's random jitter in a specific pulse interval of the pulse repetition interval (PRI), the spectrum broadens and spectral density reduces, making it hard for the ESM to intercept. Meanwhile, because the waveform doesn't have a fixed PRI, it's also difficult to be sorted or identified Therefore pulse position jitter waveform is a rational LPI waveform. Combined with a design about the LPI radar signal waveform, this paper carries out a research on PRI jitter waveform, and provides a waveform generating and echo signal processing method; then the paper simulates the LPI characteristics and comes to a conclusion which has an important applying value .

A new technique offering the low probability of interception (LPI) ability improvement of radar systems based on 4D antenna arrays is presented. Conventional radar systems usually utilize modulated waveforms and directional antennas to transmit electromagnetic wave and receive the echo signals to obtain the range and velocity information of targets. The purpose of LPI design is to keep the eavesdropping guessing until it is too late. Until now, most of the studies on LPI are based on design of LPI waveforms. In this paper, the advantage of multi-dimensional control of 4D antenna arrays is utilized to improve the LPI performance of radar. By maintaining the signals distorted pseudorandomly in non-detecting directions, the studies show that 4D antenna arrays are capable of hiding radar signal characteristics and thus decreasing the probability of being intercepted. Numerical results are provided to validate the effectiveness of the proposed approach.

In this paper, power allocation and target assignment is considered as a promising way to obtain low probability of interception (LPI) in the radar network. Spatial diversity in the netted radars gives us a flexibility to control power intelligently and radar assignment dynamically in such a way that not only detection performances satisfied but LPI characteristics of the network are optimized. We formulate this problem as a non-convex and nonlinear optimization problem associated with detection performance constraints. The optimum solution of general problem is complicated and cannot be solved mathematically. We relaxed problem to more tractable form for the networks with low complexity in which combination of radar's information cannot be handled. In the considered scenario, for each target only one radar is assigned and each assigned radar can only transmit one target's information. We propose a simple framework to obtain optimum power allocation and radar assignment strategy due to spatial diversity of netted radars. The framework has lower complexity compared with optimum exhaustive search algorithm and simulation results show effectiveness of proposed algorithm in satisfaction of detection performances and improvement of LPI specification of radar network.

Frequency diverse array (FDA) has its unique advantage in realizing low probability of intercept (LPI) technology for its dependent beam pattern. In this paper, we proposed a cognitive radar based on the frequency diverse array multiple-input multiple-output (MIMO). To implement LPI of FDA MIMO transmit signals, a scheme for array weighting design is proposed, which is to minimize the energy of the target location and maximize the energy of the receiver. This is based on the range dependent characteristics of the frequency diverse array transmit beam pattern. To realize the objective problem, the algorithm is proposed as follows: the second-order nonconvex optimization problem is converted into a convex problem and solved by the bisection method and convex optimization. To get the information of target, the FDA MIMO radar is proposed to estimate the target parameters. Simulation results show that the proposed approach is effective in decreasing the detection probability of radar with lossless detection performance of the receive signal.

We revisit the problem of protein structure determination from geometrical restraints from NMR, using convex optimization. It is well-known that the NP-hard distance geometry problem of determining atomic positions from pairwise distance restraints can be relaxed into a convex semidefinite program (SDP). However, often the NOE distance restraints are too imprecise and sparse for accurate structure determination. Residual dipolar coupling (RDC) measurements provide additional geometric information on the angles between atom-pair directions and axes of the principal-axis-frame. The optimization problem involving RDC is highly non-convex and requires a good initialization even within the simulated annealing framework. In this paper, we model the protein backbone as an articulated structure composed of rigid units. Determining the rotation of each rigid unit gives the full protein structure. We propose solving the non-convex optimization problems using the sum-of-squares (SOS) hierarchy, a hierarchy of convex relaxations with increasing complexity and approximation power. Unlike classical global optimization approaches, SOS optimization returns a certificate of optimality if the global optimum is found. Based on the SOS method, we proposed two algorithms-RDC-SOS and RDC-NOE-SOS, that have polynomial time complexity in the number of amino-acid residues and run efficiently on a standard desktop. In many instances, the proposed methods exactly recover the solution to the original non-convex optimization problem. To the best of our knowledge this is the first time SOS relaxation is introduced to solve non-convex optimization problems in structural biology. We further introduce a statistical tool, the Cramér-Rao bound (CRB), to provide an information theoretic bound on the highest resolution one can hope to achieve when determining protein structure from noisy measurements using any unbiased estimator. Our simulation results show that when the RDC measurements are corrupted by Gaussian noise of realistic variance, both SOS based algorithms attain the CRB. We successfully apply our method in a divide-and-conquer fashion to determine the structure of ubiquitin from experimental NOE and RDC measurements obtained in two alignment media, achieving more accurate and faster reconstructions compared to the current state of the art.

A robust parameter estimation of FHSS signals using time–frequency analysis in a non-cooperative environment

To solve the problem of dwell time management for multiple target tracking in Low Probability of Intercept (LPI) radar network, a Nash bargaining solution (NBS) dwell time allocation algorithm based on cooperative game theory is proposed. This algorithm can achieve the desired low interception performance by optimizing the allocation of the dwell time of each radar under the constraints of the given target detection performance, minimizing the total dwell time of radar network. By introducing two variables, dwell time and target allocation indicators, we decompose the dwell time and target allocation into two subproblems. Firstly, combining the Lagrange relaxation algorithm with the Newton iteration method, we derive the iterative formula for the dwell time of each radar. The dwell time allocation of the radars corresponding to each target is obtained. Secondly, we use the fixed Hungarian algorithm to determine the target allocation scheme based on the dwell time allocation results. Simulation results show that the proposed algorithm can effectively reduce the total dwell time of the radar network, and hence, improve the LPI performance.

Nowadays, there is a growing awareness of the requirement of low probability of intercept (LPI) in modern battlefield. In this study, a novel LPI optimization algorithm is presented for target tracking in radar network architectures. We first provide radar network signal model. Then, an effective LPI optimization algorithm is proposed, where Schleher intercept factor is utilized as an optimization metric for LPI performance. Our purpose is to minimize the intercept factor for a predetermined minimum mean square error (MMSE) threshold of the estimate of target response matrix. The resulting nonlinear and constrained problem is solved through the genetic algorithm with nonlinear constraint (GANC). Simulation results demonstrate the effectiveness of our proposed algorithm.