Adaptive simultaneous multibeam resource management for colocated MIMO radar in multiple targets tracking

Highlights

• Simultaneous multibeam resource management for colocated MIMO radar.

• Sub-array number and its influence on beam width.

• Joint adjustment of sub-array number, system sampling period, transmit energy, beam directions and working mode.

• Superior performance of the proposed algorithm than the algorithms with fixed parameters and the existing method.

Abstract

Different from conventional phased array radar, the colocated multiple-input multiple-output (MIMO) radar can form multiple beams simultaneously through transmitting orthogonal waveforms by its sub-arrays. The illuminating mode of multiple beams can be changed with the sub-array number, providing greater flexibility in system resource management. In this paper, a novel resource management optimization model for the colocated MIMO radar in multiple targets tracking is proposed, where the average time and energy resource consumption of the system are minimized under the guarantee that the illuminated targets can be effectively detected. When solving the proposed optimization problem, the adaptive simultaneous multibeam resource management (ASMRM) algorithm for the colocated MIMO radar is obtained, where the sub-array number, the beam directions, the system sampling period, the transmit waveform energy and the working mode can be controlled adaptively. Simulation results demonstrate that the working parameters of the colocated MIMO radar can be changed effectively. Furthermore, in terms of the average system resource consumption, the proposed algorithm is superior to the algorithms with fixed parameters while realizing the effective multiple targets tracking.

Introduction

Multiple-input multiple-output (MIMO) radar, which is a new generation of radar system, has raised growing attention recently [1], [2], [3], [4], [5], [6], [7], [8] and is on a path from theory to practical use. Compared with phased array radar, MIMO radar achieves better performance in target localization, target identification, and can obtain higher resolution and sensitivity when detecting the slow moving target [9], [10], [11].

Generally, MIMO radar can be divided into two types, that is, colocated MIMO radar [10] as well as distributed MIMO radar [12], [13]. In the distributed MIMO radar system, the transmit antennae are located far apart from each other in comparison with their distance to the target [12]. However, many actual difficulties still stop distributed MIMO radar from being applied in practice [14]. Compared with distributed MIMO radar, the colocated MIMO radar system, where the transmit and receive antennae are deployed close to each other relative to their distance to the target, can be considered as an advancement of the existing phased array radar. Hence, based on the current technical conditions, the colocated MIMO radar system has more practical value than the distributed MIMO radar [15]. Compared with phased array radar, multiple orthogonal beams can be transmitted in the colocated MIMO radar simultaneously. Therefore, when the colocated MIMO radar system is applied in multiple targets tracking, the primary problem to be solved is how to allocate multiple beams among multiple targets, which is actually the resource management problem.

For radar resource management, the essence is to control its working parameters adaptively. Existing researches on resource management for colocated MIMO radar mainly focus on the waveform design and power allocation. The authors in [16] propose an efficient optimization method to design a constant modulus probing signal, which can synthesize a desired beam pattern while maximally suppressing both the autocorrelation and cross-correlation side lobes between given spacial angles. In power allocation and waveform design for target identification and classification, two waveform design problems with constraints on waveform power have been investigated in [17]. The power allocation in jamming is considered in [18], where the robust jamming power allocation strategies are proposed. Furthermore, the problem of antenna allocation in resource management has been studied in [19], [20], where the optimal distribution of antennae is found by applying the relevant operators to the Cramér-Rao lower bound (CRLB). In addition, the authors in [21] propose an active jamming suppression method based on the transmit array designation for the colocated MIMO radar.

The target tracking process is not considered in the aforementioned studies. The authors in [22], [23], [24] put forth resource management strategies based on the Minimax criterion in multiple targets tracking, whose aim is to run out of the power budget to improve the worst tracking accuracy. In [22], a simultaneous multibeam resource allocation (SMRA) strategy is proposed, where the beam number, beam directions and the transmit power of each beam are adjusted. In addition, the cases when one beam illuminates a single target and multiple targets are both taken into consideration in [22]. It is extended to the netted radar system [23]. The power allocation algorithm for the colocated MIMO radar in clutter is proposed in [24]. In [25], the power allocation in clutter with netted radar system for single-target tracking is addressed. Consider the influence of waveform selection on resource management, the authors in [26] put forward a joint beam, power and waveform selection strategy. The aforementioned studies are based on the Minimax criterion, while a resource management model considering the desired tracking accuracy is proposed in [27], where the sub-array number, the working mode and the transmit energy can be controlled adaptively. In addition, an adaptive cost function (ACF), which is related to the desired tracking accuracy, is proposed in [28], [29], [30], where the ACF is optimized by allocating the beam and power resources adaptively. Furthermore, resource management for sensor network and radar communication integration system has also begun to receive attention[31], [32], [33]. The authors in [31] propose a collaborative detection and power allocation (CDPA) scheme, where the false alarm rate and transmit power of each node can be controlled. Based on the Markov decision process (MDP), the authors in [32] propose a new resource scheduling method for target tracking in clutter in the radar network, where the selection of radar node and its resource can be optimized. For resource management in the integrated radar and communications system (IRCS), a power minimization-based joint sub-carrier assignment and power allocation (PM-JSAPA) strategy is proposed in [33], where the total radiated power of the IRCS is minimized.

While existing studies have made seminal contributions to the MIMO radar resource management problem, there are still some issues to be addressed:

Firstly, resource management for MIMO radar focuses on the energy resource in [22], [23], [24], [26], [27], [28], [29], [30], where energy resource management considers the spacial distribution of the transmitted energy at each illuminating moment. Diverse energy resource management strategies are derived from different resource management problem models. However, the time resource management, which focuses on how to choose the optimal illuminating moment for the system, is not considered.

Secondly, in the existing studies [22], [23], [24], [25], [26], in order to improve the worst tracking performance among the multiple targets, all system resource is used up. However, in practice, how to minimize the system resource consumption on the premise of ensuring effective targets tracking is a more valuable problem.

Thirdly, each beam corresponds to one single target in [23], [24], [26], [28], [29], [30]. The mechanism of how multiple beams are formed and the ability of each beam to detect multiple targets have not been taken into account. Therefore, in the resulting resource management strategies, the ability of a single wide beam to illuminate multiple targets is ignored.

Based on above, an adaptive resource management optimization model for the colocated MIMO radar in multiple targets tracking is established in this paper. In the optimization model, the objective function considers comprehensively the average energy and time resources consumed by the illuminated targets, and the constraints ensure the effective tracking of the targets. In solving the optimization model, the simultaneous multibeam mode of the colocated MIMO radar is fully considered and an adaptive resource management algorithm is proposed. In the proposed algorithm, five working parameters can be controlled adaptively, including the sub-array number, the beam directions, the system sampling period, the transmit waveform energy and the working mode. The main contributions of this paper are the following:

1.A novel resource management optimization model for the colocated MIMO radar in multiple targets tracking is put forth. In the proposed resource management model, the objective function considers not only the energy resource but also the time resource, namely, the average time and energy resource consumption of the system. Then, to solve the established optimization model, the adaptive simultaneous multibeam resource management (ASMRM) algorithm is proposed to minimize the total system resource consumption while ensuring effective targets tracking.

2.In our work, we consider that the multiple beams are realized by the sub-array division. The sub-array number and its influence on beam width are also taken into account, that is, the number of multiple beams and the width of each beam will be changed with the sub-array number. When the sub-array number is large, a single beam in the multiple beams is wide enough to detect multiple targets simultaneously under certain conditions. As a result, in addition to the optimization of traditional working parameters, the optimization of the sub-array number is mainly considered.

The rest of this paper is organized as follows: The problem formulation is given in Section 2. In Section 3, the resource management optimization model is proposed. Then, an adaptive resource management algorithm for colocated MIMO radar based on the simultaneous multibeam mode is given in Section 4 to solve the proposed optimization model. To show the effectiveness of the proposed algorithm, several numerical simulation results are provided in Section 5. Finally, the conclusions are drawn in Section 6.