Resource Allocation for Multi-target Tracking in Multi-static Radar Systems with Imperfect Detection Performance

This paper investigates the problem of low probability of intercept-based optimal power allocation (LPI-OPA) for an integrated multistatic radar and communication system, which consists of multiple transmitters operating at different frequencies, a radar receiver, and a communication receiver (CR). The integrated multistatic radar and communication system is capable of fulfilling the requirements of both radar and communication subsystems. The key tenet of the integrated system is to minimize the total power consumption by optimizing the transmit power allocation at each transmitter for radar waveforms and information signals, which is constrained by a predetermined target detection performance for the RR and a desired information rate for the CR. Since the analytical closed-form expression of the probability of detection is not tractable, its upper bound is derived. We analytically show that the resulting optimization problem can be reformulated as two subproblems, which can be solved by an efficient solution procedure based on the approach of linear programming and the Karush–Kuhn–Tuckers optimality conditions. Simulation results are provided to show that the LPI performance of the integrated multistatic radar and communication system can significantly be enhanced by employing our proposed LPI-OPA scheme.

Asynchronism among the local radars is one of the most important challenges for multi-target tracking (MTT) in multi-static radar systems. In order to address this challenge in the framework of random finite set (RFS) based on Bayesian inference, we propose a decentralized probability hypothesis density (PHD) filter based on the asynchronous periodical sequential estimation (APSE). First, starting from a multi-target Bayesian filter, we derive the multi-target density update expressions for the APSE solution in the RFS framework. Next, we develop the PHD recursion expressions of the APSE solution, named as APSE-PHD, and describe the Gaussian mixture (GM) implementation of the APSE-PHD. Simulation results for a challenging tracking scenario confirm that the proposed APSE-PHD algorithm is effective for MTT in the asynchronous multistatic radar system and outperforms the existing PHD-based algorithm.

The paper presents a unified view of the tracking algorithms available for multistatic radar systems. The first topic considered is the derivation of tracking algorithms for bistatic radar. Different algorithms can be conceived according to different sets of measurements which may be processed (e.g. the range sum and one angle, or two angles). Mean and standard deviation values of tracking errors are evaluated by means of Monte Carlo simulation for a number of typical target paths. The major topic considered in the paper refers to the derivation of a unique tracking filter structure for multistatic radar systems. Particular attention is paid to the case of one transmitter and two noncolocated receivers; additionally, several combinations of multistatic measurements are considered. Furthermore, in this case the tracking performance of the algorithms are evaluated by means of Monte Carlo simulation techniques. The closing Section highlights the trends of the research towards multitarget tracking with distributed estimation techniques and the problem of data fusion.

This paper studies the problem of Stackelberg game based distributed power allocation for spectral coexisting multistatic radar and communication systems. The strategy aims to minimize the radiated power of each radar by optimizing transmit power allocation for a desired signal-to-interference-plus-noise ratio (SINR) meanwhile the communication base station (CBS) is protected from the interference of radar transmissions. We formulate this distributed power allocation process as a Stackelberg game, where the CBS is a leader and the radars are the followers. The Nash equilibrium (NE) for the formulated game is derived. Then, the existence of the NE and uniqueness of the solution are analytically proved. Moreover, an iterative algorithm is developed to solve the resulting problem. Finally, numerical results verify that the proposed scheme is effective on power allocation and CBS protection with reduced signaling overhead.

Joint resource and trajectory optimization in a UAV-enabled dual-function radar-communication network

A collocated multiple-input and multiple-output (MIMO) radar has the ability to track multiple targets under the simultaneous multi-beam mode, where each transmit beam is defocused to search the entire surveillance space, and then; a focused beam is formed at the receiver to achieve a higher resolution. A joint power and bandwidth allocation strategy is developed in this mode. The optimization model is established with the aim of improving the sum of weighted posterior Cramer-Rao lower bounds (PCRLBs) of multiple targets, which is predicted based on the feedback information in the tracking recursive cycle, subject to the limited power and bandwidth budget. By exploiting the convex relaxation technique and the cycle minimizer, this problem is transformed into a two-stage convex problem, and an efficient solution is provided using the cyclical optimization framework. The effectiveness of the proposed strategy is demonstrated by comparison with three uniform resource allocation methods in many cases.

The multistatic ambiguity function can be used as a tool for analyzing multistatic radar systems. It has been demonstrated that the multistatic ambiguity function can serve as a guideline for developing multistatic radar signal processing rules and waveform selection strategies in system configurations with a single transmitter and multiple receivers. In this work we extend the development of multistatic ambiguity function to radar systems with multiple transmitters and multiple receivers.

In this paper we consider the radar assignment problem for air-defense radar network to detect and track multiple stealth targets. For the purpose of selecting appropriate radar at appropriate time to optimize the detection and tracking performance under the given constraints, we propose a novel radar assignment algorithm based on posterior Cramer-Rao lower bound (PCRLB), binary particle swarm optimization (BPSO) and particle filtering (PF). A group of randomly generated particles are used to obtain the detection probability of newborn targets, while the tracking accuracy is measured by PCRLB. Moreover, the BPSO is adopted to search the surveillance region for the optimal activated radar subset. Finally, the PF outputs of activated radars are fused. Simulation results show that the proposed method can not only quickly identify newborn targets, but also can optimize the tracking performance of existing targets. Compared with traditional methods, the tracking accuracy of the radar network is improved.

In this paper, we present a new localization method in multistatic radar systems. Based on two-stage algorithm, which is used to estimate the location of a target that transmits a signal in passive sensor networks, we reformulate the target localization problem and derive the new two-stage algorithm which suits our situation. Simulation results demonstrate that the proposed method outperforms conventional methods and achieves the Cramer-Rao lower bound (CRLB) with the knowledge of noise statistics over the range of moderate noise power.

In this letter, a three-dimensional (3D) joint estimation algorithm which is the variable-step hill-climbing algorithm based on two-step weighted least squares (VHC-2WLS) is proposed. The VHC-2WLS combines bistatic range (BR), time difference of arrival (TDOA) and Doppler shift (DS) to improve the estimation accuracy of the position and velocity of a moving target in the multistatic radar system (MRS). First, the position of a moving target is estimated by utilizing two-step weighted least squares (2WLS) method. Second, the estimated position from the first step is optimized by variable-step hill-climbing (VHC) method. Finally, the velocity is calculated through the optimized position value from the second step and least squares (LS) approach. The proposed method is corroborated by simulations to be able to reach the Cramer-Rao lower bound (CRLB) accuracy under mild noise conditions. Numerical simulations demonstrate the superiority in the estimation accuracy of the proposed algorithm over state-of-the-art methods.

A joint power allocation (PA) scheme for a class of MIMO relay systems is presented in this paper. Based on the maximization of the capacity or minimization of the mean-square error (MSE), two joint PA optimization problems are first formulated. Since the cost functions thus obtained are in general not convex, a tight lower bound of the capacity and a similar upper bound of the MSE are derived, and employed to modify the two cost functions so as to obtain a convex optimization problem. It is confirmed through computer simulations that the proposed PA scheme outperforms the existing method in terms of both the capacity and the MSE of MIMO relay systems.

The Multiple Airborne Sensor Targeting and Evaluation Rig (MASTER) is a high fidelity simulation environment in which data fusion, tracking and sensor management algorithms developed within QinetiQ Ltd. can be demonstrated and evaluated. In this paper we report an observer trajectory planning tool that adds considerable functionality to MASTER. This planning tool can coordinate multiple sensor platforms in tracking highly manoeuvring targets. It does this by applying instantaneous thrusts to each platform, the magnitude of which is chosen to gain maximum observability of the target. We use an efficient search technique to determine the thrust that should be applied to each platform at each time step, and the planning horizon can either be one-step (greedy) or two-step. The measure of performance used in evaluating each potential sensor manoeuvre (thrust) is the posterior Cramer-Rao lower bound (PCRLB), which gives the best possible (lowest mean square error) tracking performance. We exploit a recent novel approach to approximating the PCRLB for manoeuvring target tracking (the best-fitting Gaussian (BFG) approach: Hernandez et al., 2005). A closed-form expression gives the BFG approximation at each sampling time. Hence, the PCRLB can be approximated with a very low computational overhead. As a result, the planning tool can be implemented as an aid to decision-making in real-time, even in this time-critical airborne domain. The functionality of MASTER enables one to access the performance of the planning tool in a range of sensor-target scenarios, enabling one to determine the minimal sensor requirement in order to satisfy mission requirements.

Multi-target tracking in distributed sensor networks using particle PHD filters

Large-scale sensor array management has applications in a number of target tracking problems. For example, in ground target tracking, hundreds or even thousands of unattended ground sensors (UGS) may be dropped over a large surveillance area. At any one time it may then only be possible to utilize a very small number of the available sensors at the fusion center because of bandwidth limitations. A similar situation may arise in tracking sea surface or underwater targets using a large number of sonobuoys. The general problem is then to select a subset of the available sensors in order to optimize tracking performance. The Posterior Cramer-Rao Lower Bound (PCRLB), which quantifies the obtainable accuracy of target state estimation, is used as the basis for network management. In a practical scenario with even hundreds of sensors, the number of possible sensor combinations would make it impossible to enumerate all possibilities in real-time. Efficient local (or greedy) search techniques must then be used to make the computational load manageable. In this paper we introduce an efficient search strategy for selecting a subset of the sensor array for use during each sensor change interval in multi-target tracking. Simulation results illustrating the performance of the sensor array manager are also presented.

SummaryWe investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.