SQP-based Power Allocation Strategy for Target Tracking in MIMO Radar Network with Widely Separated Antennas
الموضوعات :
Mohammad Akhondi Darzikolaei
1
(Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology,Babol, Iran)
Mohammad Reza Karami-Mollaei
2
(Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology,Babol, Iran)
Maryam Najimi
3
(Department of Electrical and Computer Engineering, University of Science and Technology of Mazandaran,Behshahr, Iran)
الکلمات المفتاحية: MIMO radar, Power allocation, SQP, Target tracking,
ملخص المقالة :
MIMO radar with widely separated antennas enhances detection and estimation resolution by utilizing the diversity of the propagation path. Each antenna of this type of radar can steer its beam independently towards any direction as an independent transmitter. However, the joint processing of signals for transmission and reception differs this radar from the multistatic radar. There are many resource optimization problems which improve the performance of MIMO radar. But power allocation is one of the most interesting resource optimization problems. The power allocation finds an optimum strategy to assign power to transmit antennas with the aim of minimizing the target tracking errors under specified transmit power constraints. In this study, the performance of power allocation for target tracking in MIMO radar with widely separated antennas is investigated. Therefore, a MIMO radar with distributed antennas is configured and a target motion model using the constant velocity (CV) method is modeled. Then Joint Cramer Rao bound (CRB) for target parameters (joint target position and velocity) estimation error is calculated. This is utilized as a power allocation problem objective function. Since the proposed power allocation problem is nonconvex. Therefore, a SQP-based power allocation algorithm is proposed to solve it. In simulation results, the performance of the proposed algorithm in various conditions such as a different number of antennas and antenna geometry configurations is examined. Results affirm the accuracy of the proposed algorithm.
[1] M. A. Darzikolaei, A. Ebrahimzade, and E. Gholami, “Classification of radar clutters with artificial neural network,” in 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), 2015, pp. 577–581.
[2] M. A. Darzikolaei, A. Ebrahimzade, and E. Gholami, “The Separation of Radar Clutters using Multi-Layer Perceptron,” Information Systems & Telecommunication, p. 41, 2017.
[3] E. Fishler, A. Haimovich, R. Blum, D. Chizhik, L. Cimini, and R. Valenzuela, “MIMO radar: An idea whose time has come,” in Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No. 04CH37509), 2004, pp. 71–78.
[4] E. Fishler, A. Haimovich, R. S. Blum, L. J. Cimini, D. Chizhik, and R. A. Valenzuela, “Spatial diversity in radars—Models and detection performance,” IEEE Transactions on signal processing, vol. 54, no. 3, pp. 823–838, 2006.
[5] J. Li and P. Stoica, “MIMO radar with colocated antennas,” IEEE Signal Processing Magazine, vol. 24, no. 5, pp. 106–114, 2007.
[6] A. M. Haimovich, R. S. Blum, and L. J. Cimini, “MIMO radar with widely separated antennas,” IEEE Signal Processing Magazine, vol. 25, no. 1, pp. 116–129, 2007.
[7] M. Hai, “MIMO radar with widely separated antennas technology,” IEE Signal Magazine, vol. 26, no. 2, pp. 98–106, 2009.
[8] Y. Bar-Shalom, X. R. Li, and T. Kirubarajan, Estimation with applications to tracking and navigation: theory algorithms and software. John Wiley & Sons, 2004.
[9] A. Pakdaman and H. Bakhshi, “Separable transmit beampattern design for MIMO radars with planar colocated antennas,” AEU-International Journal of Electronics and Communications, vol. 89, pp. 153–159, 2018.
[10] M. Xie, W. Yi, T. Kirubarajan, and L. Kong, “Joint node selection and power allocation strategy for multitarget tracking in decentralized radar networks,” IEEE Transactions on Signal Processing, vol. 66, no. 3, pp. 729–743, 2017.
[11] H. Godrich, A. P. Petropulu, and H. V. Poor, “Power allocation strategies for target localization in distributed multiple-radar architectures,” IEEE Transactions on Signal Processing, vol. 59, no. 7, pp. 3226–3240, 2011.
[12] H. Chen, S. Ta, and B. Sun, “Cooperative game approach to power allocation for target tracking in distributed MIMO radar sensor networks,” IEEE Sensors Journal, vol. 15, no. 10, pp. 5423–5432, 2015.
[13] P. Chavali and A. Nehorai, “Scheduling and power allocation in a cognitive radar network for multiple-target tracking,” IEEE Transactions on Signal Processing, vol. 60, no. 2, pp. 715–729, 2011.
[14] C. Shi, S. Salous, F. Wang, and J. Zhou, “Power allocation for target detection in radar networks based on low probability of intercept: A cooperative game theoretical strategy,” Radio Science, vol. 52, no. 8, pp. 1030–1045, 2017.
[15] J. Yan, H. Liu, B. Jiu, and Z. Bao, “Power allocation algorithm for target tracking in unmodulated continuous wave radar network,” IEEE sensors journal, vol. 15, no. 2, pp. 1098–1108, 2014.
[16] L. Wang, L. Wang, Y. Zeng, and M. Wang, “Jamming power allocation strategy for MIMO radar based on MMSE and mutual information,” IET Radar, Sonar & Navigation, vol. 11, no. 7, pp. 1081–1089, 2017.
[17] S. M. H. Andargoli and J. Malekzadeh, “LPI radar network optimization based on geometrical measurement fusion,” Optimization and Engineering, vol. 20, no. 1, pp. 119–150, 2019.
[18] B. Ma, H. Chen, B. Sun, and H. Xiao, “A joint scheme of antenna selection and power allocation for localization in MIMO radar sensor networks,” IEEE communications letters, vol. 18, no. 12, pp. 2225–2228, 2014.
[19] X. Li, W. Yi, G. Cui, L. Kong, and X. Yang, “Radar selection for single-target tracking in radar networks,” in 2015 IEEE Radar Conference (RadarCon), 2015, pp. 0545–0550.
[20] Y. Lu, Z. He, X. Zhang, and S. Liu, “Transmit and receive sensors joint selection for MIMO radar tracking based on PCRLB,” in 2016 IEEE 13th International Conference on Signal Processing (ICSP), 2016, pp. 1551–1555.
[21] J. She, F. Wang, and J. Zhou, “A novel sensor selection and power allocation algorithm for multiple-target tracking in an LPI radar network,” Sensors, vol. 16, no. 12, p. 2193, 2016.
[22] J. Yan, H. Liu, W. Pu, S. Zhou, Z. Liu, and Z. Bao, “Joint beam selection and power allocation for multiple target tracking in netted colocated MIMO radar system,” IEEE Transactions on Signal Processing, vol. 64, no. 24, pp. 6417–6427, 2016.
[23] X. Song, N. Zheng, and T. Bai, “Resource allocation schemes for multiple targets tracking in distributed MIMO radar systems,” International Journal of Antennas and Propagation, vol. 2017, 2017.
[24] N. Garcia, A. M. Haimovich, M. Coulon, and M. Lops, “Resource allocation in MIMO radar with multiple targets for non-coherent localization,” IEEE Transactions on Signal Processing, vol. 62, no. 10, pp. 2656–2666, 2014.
[25] Yi, Wei, Ye Yuan, Reza Hoseinnezhad, and Lingjiang Kong. "Resource scheduling for distributed multi-target tracking in netted colocated MIMO radar systems." IEEE Transactions on Signal Processing 68 (2020): 1602-1617.
[26] Li, Zhengjie, Junwei Xie, Haowei Zhang, Houhong Xiang, and Zhaojian Zhang. "Adaptive sensor scheduling and resource allocation in netted collocated MIMO radar system for multi-target tracking." IEEE Access 8 (2020): 109976-109988.
[27] Q. He, R. S. Blum, and A. M. Haimovich, “Noncoherent MIMO radar for location and velocity estimation: More antennas means better performance,” IEEE Transactions on Signal Processing, vol. 58, no. 7, pp. 3661–3680, 2010.
[28] V. Trees and L. Harry, Detection, Estimation, and Modulation Theory-Part l-Detection, Estimation, and Linear Modulation Theory. John Wiley & Sons New York, 2001.
[29] H. Godrich, A. M. Haimovich, and R. S. Blum, “Target localization accuracy gain in MIMO radar-based systems,” IEEE Transactions on Information Theory, vol. 56, no. 6, pp. 2783–2803, 2010.
[30] D. Wassel, “Exploring novel designs of nlp solvers: architecture and implementation of worhp,” PhD Thesis, Universität Bremen, 2013.
SQP-based Power Allocation Strategy for Target Tracking in MIMO Radar Network with Widely Separated Antennas
|
Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology,Babol, Iran M.akhondi@stu.nit.ac.ir Mohammad Reza Karami Mollaei * Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology,Babol, Iran Maryam Najimi Department of Electrical and Computer Engineering, University of Science and Technology of Mazandaran,Behshahr, Iran
Received: 22/Aug/2021 Revised: 06/Nov/2021 Accepted: 07/Dec/2021 |
Abstract
MIMO radar with widely separated antennas enhances detection and estimation resolution by utilizing the diversity of the propagation path. Each antenna of this type of radar can steer its beam independently towards any direction as an independent transmitter. However, the joint processing of signals for transmission and reception differs this radar from the multistatic radar. There are many resource optimization problems which improve the performance of MIMO radar. But power allocation is one of the most interesting resource optimization problems. The power allocation finds an optimum strategy to assign power to transmit antennas with the aim of minimizing the target tracking errors under specified transmit power constraints. In this study, the performance of power allocation for target tracking in MIMO radar with widely separated antennas is investigated. Therefore, a MIMO radar with distributed antennas is configured and a target motion model using the constant velocity (CV) method is modeled. Then Joint Cramer Rao bound (CRB) for target parameters (joint target position and velocity) estimation error is calculated. This is utilized as a power allocation problem objective function. Since the proposed power allocation problem is nonconvex. Therefore, a SQP-based power allocation algorithm is proposed to solve it. In simulation results, the performance of the proposed algorithm in various conditions such as a different number of antennas and antenna geometry configurations is examined. Results affirm the accuracy of the proposed algorithm.
Keywords: MIMO radar; Power allocation; SQP; Target tracking.
1- Introduction
The RADAR is a short form of Radio Detection And Ranging. Radar utilizes electromagnetic waves to detect, locate and measure the speed of reflected objects. It transmits the electromagnetic waves into space and receives the echo signals [1-2]. In recent years, radar network systems such as multi-static radars and multi-input multi-output (MIMO) radars have become an attractive and improtant problem [3]. Spatial diversity [4], waveform diversity [5], and multiplexing gain [6] over common monostatic radar [7] are some positive characteristics of networked radar systems. This structure of radars helps to raise tracking accuracy in multiple target tracking scenarios with radar, sonar, and video sensors [8]. A MIMO radar is a kind of radar structure that uses a combination of antennas as transmitter and receiver and each of them emits its own waveform apart from others [9]. Widely separated antennas and collocated antennas are two famous categories for MIMO radar. In collocated MIMO radar, the antennas are close to each other. The antennas in MIMO radar with widely separated antennas are far from each other. In other words, the transmit and receive antennas are located in a wide area. Therefore, the target is seen from different angles by antennas. In this type of radars, each receiver should receive all signals from all transmitters and then emit them to the central processor. This means that each receiver does not process or make a decision individually and sends its signals to the central processor to process all signals. This feature is the main difference between multi-static and MIMO radar with widely separated antennas. Power allocation is usually performed in radar networks to find the best strategy to assign power among various transmit antennas, aiming at minimizing the estimation error under specified transmit power constraints or its converse [10]. Power allocation is an important section of military operations in a hostile environment to obtain a low probability of interception [11]. Power allocation in radar networks is studied in last researches. For example, in [12] power allocation in widely separated multi-input multi-output (MIMO) radar for range-only target tracking is evaluated. This is performed by maximizing the Bayesian Fisher information matrix (B-FIM). B-FIM is derived for the predetermined signal model and then the problem is modeled as one cooperative game. [13] Shows the commercial application of power allocation in radar networks. In fact, it puts a cognitive radar network in an urban environment and this network tracks cars and vehicles. The power allocation problem for radar networks in a cooperative game-theoretic structure is considered in [14] to enhance the low probability of intercept (LPI) performance. In addition, by considering transmit power constraint and minimum signal to noise and interference ratio (SINR) for each radar, a cooperative Nash Bargaining power allocation game based on LPI is expressed. The radar network in [15] consists of unmodulated continuous wave (UCW) radars. This network utilizes a power allocation algorithm for Doppler-only target tracking. This algorithm minimizes the mean square error of target state estimation with a power budget constraint. [16] Investigates power allocation for radar networks to increase the performance of low probability of intercept. Two power allocation strategies are stated in this reference. One is for optimizing transmit power allocation with predetermined mutual information (MI) threshold and another is for finding optimal power allocation with minimum mean square error (MMSE) threshold. In [17], power allocation and target assignment in radar networks is mentioned to improve LPI performance. The target geometrical method is used to fuse information for measuring target localization by different radars. Authors in [18] propose joint antenna selection and power allocation for localization in distributed MIMO radar networks. The sensor management is performed by solving a constrained problem which is expressed to minimize the error in estimating target position, while it is constrained by transmitter number and power budget. [19] Talks about sensor selection in radar networks and for target tracking, the number of radars are selected. This problem is just for one target tracking. In this reference, sensor selection is performed by information theory. Joint transmitter and receiver selection in target tracking in distributed MIMO radar is described in [20]. Due to resource restriction in radars, it is necessary to select some radars in the MIMO radar network at each time and also keep the system performance in the best condition. So, lower bound PCRLB is u as an optimization criterion for an optimization problem in this literature. In [21], a joint power allocation and sensor selection algorithm for multi-target tracking in an LPI radar network with N monostatic radars is introduced. This algorithm can minimize the total transmitted power of a radar network based on predefined mutual information threshold between reflected signal and target impulse computed predictively with the estimation of the target state is needed for estimation of target parameters. [22] Describes the joint beam selection and power allocation strategies for multi-target tracking in collocated MIMO radar. Each radar works based on a multi-beam working mode, in which multiple simultaneous transmit beams are synthesized. This strategy applies an optimization technique to control the limited beam and power resource of each radar to obtain accurate target state estimation. Therefore, Bayesian Cramer Rao Lower Bound is extracted, normalized, and utilizes as the optimization criterion. To increase the system performance and resource utilization of widely separated MIMO radar, a joint resource allocation for velocity estimation problem in multi-target tracking is proposed in [23]. The authors chose one target as a key target and then examined their strategy by this target. They considered a Mean Square Estimation of velocity estimation of the key target as a minimization problem criterion. With limited resources and requirements for velocity estimation for targets, a joint optimization model with the selection of numbers of receivers and transmitters and allocation of transmit power and signal time is introduced. The Authors in [24] claims that since transmitters in MIMO radars with widely separated antennas emit waveforms with different powers and bandwidths, therefore these two parameters are limited. In this reference, they offer power allocation, bandwidth allocation, and joint power and bandwidth allocation problems. They compute Cramer Rao for target localization accuracy and utilize it as optimization criteria. In [25], a solution for joint beam and power scheduling in the netted Collocated MIMO radar systems for distributed multi-target tracking is suggested. An adaptive sensor scheduling integrated with power and bandwidth allocation is presented for centralized multiple target tracking in the netted collocated MIMO radar in [26].
By investigating the above references, we understand that power allocation for target tracking in MIMO radar with widely separated antennas is very essential and the performance of this problem should be improved. With our reviews, there are some challenges in the power allocation problem for target tracking in MIMO radar with widely separated antennas which are not investigated in other papers and we consider them in this paper.
1. In this paper, for the calculation of target tracking errors, joint target velocity and position estimation is used to improve tracking performance. Although someone works on this issue for MIMO radar, this is not used in power allocation problems in MIMO radar with widely separated antennas. For example, in [25] and [7], joint estimation is considered but certain power is determined for the transmit power and power allocation strategy is not performed. And also, in [25], it does not exactly specify its MIMO radar structure (distributed or collocated). In other researches about power allocation for target tracking in MIMO radar with widely separated antennas, joint estimation is not worked. For instance, in [12], range-only estimation for target tracking in MIMO radar with widely separated antennas is performed and it is not considered velocity. Whereas, in [23], velocity estimation is utilized to compute target tracking for power allocation problem in distributed MIMO radar. These are just some papers which we investigated and we concluded that joint estimation for target tracking in power allocation problem for MIMO radar with widely separated antennas is not used yet. Although it may be performed for collocated MIMO radar. Thus , joint target velocity and position estimation is the first novel idea for our power allocation strategy in MIMO radar with widely separated antennas.
2. Using random mathematical statistics for target RCS is another unique characteristic of this paper. Because in other researches, or a deterministic model for target RCS is considered [7, 24, 18, 22, 23], or if they supposed a random target RCS, they neglected it in their next calculations for simplicity [20, 21]. Therefore, none of the previous researches did contribute a power allocation strategy for target tracking in MIMO radar with widely separated antennas by considering random complex Gaussian target RCS. They usually neglected it or put a deterministic number instead of variance of Gaussian distribution. But in this paper, in all calculations, random complex Gaussian with random variance in different transmit-receive paths is considered. Note that assuming random RCS is necessary for MIMO radar with widely separated antennas. Because each antenna sees the target in a specified angle and target reflections in different transmit-receive paths are different with respect to each other.
The main contributions of this paper are as follows:
1. The system model for MIMO radar with widely separated antennas is introduced. Then CV model is considered for target motion. Considering a complex Gaussian random model for target RCS and using this feature in the next calculations, Cramer Rao bound for target parameters estimation error, is one of the prominent aspects of this paper.
2. Maximum likelihood (ML) estimation for unknown target parameters which were target position and velocity is calculated and then joint CRB for target position and velocity estimation is computed. Output of joint CRB is considered as an objective function for power allocation problem.
3. A power allocation strategy is formed. In fact, joint CRB for target position and velocity estimation function (target tracking errors) subject to some constraints such as limitation in total transmit power and transmit power of each transmit antenna is the power allocation problem of this paper. Our goal is to minimize tracking errors by using the mentioned constraints.
4. For solving the previous section problem, since it is nonconvex problem, SQP1 based power allocation algorithm is proposed. This algorithm is formed based on the SQP algorithm and it can allot optimal power to each transmit antenna to satisfy the constraints in the problem. The rest of the paper is structured as follows:
The system model is mentioned in Section 2. Section3 exhibits the ML estimation calculations. Joint CRB for target parameters is computed in section4. Section 5 forms a power allocation problem to minimize target tracking error by considering total power limitation. And also, a proposed SQP-based algorithm is presented in this part to solve this problem. Simulation results are shown in section 6 and finally in part 7, concluding remarks are addressed.
2- System Model
Consider a MIMO radar with widely separated antennas with M transmitters and N receivers. Denote the location of mth transmitter in (
), where 
and the coordinates of nth receiver in (
), where
. Target is in initial location (
) with initial velocity of (
). A set of low pass equivalent orthogonal waveforms,
, is transmitted. (
. period, effective bandwidth and transmit power of 
th transmit waveform are shown as 
,
, 
. Target RCS corresponding to 
th path is modeled as a zero-mean complex Gaussian random variable
. Where 
is the variance of th path and it is known. Fig.1 shows the structure and location of antennas in MIMO radar with widely separated antennas with respect to the target.
Fig. 1 configuration of a MIMO radar with widely separated antennas
We suppose the below assumption to simplify our problem.
1. 
(Noise of th path with a zero-mean complex Gaussian random variable and variance of 
) and 
in different paths are mutually independent.
2. Transmit waveforms are orthogonal.
(1)
This orthogonality also remains for time delays 
and Doppler shifts 
and 
[26]:
(2)
3. Set 
without loss of generality.
4. The antennas are adequately separated [27]. Therefore, each path provides an independent observation of the target and is independent for different and 
paths.
The time delay of 
th channel in 
th time slot is:
(3)
Where,
(4)
In the above equations, 
is light velocity. 
is distance from target and th transmitter and 
is distance from a target and th receiver.
With these assumptions, the received signal from th transmit antenna at th receive antenna at time is given by:
(5)
In the above equation, 
represents a zero-mean complex Gaussian noise with the variance of . Power variations due to path loss is shown as 
. Where 
is the carrier frequency.
Doppler frequency in path and time is given by:
(6)
is wavelength.
2-1- Target Dynamic Model
Target tracking in a MIMO Radar with widely separated antennas is the favorable problem of this paper. The target motion model is the constant velocity (CV). This model is as below[7]:
| (7) |
| (8) |
| (9) |
| (10) |
| (11) |
| (12) |
| (13) |
| (14) |
| (15) |
| (16) |
| (17) |
| (18) |
| (19) |
Therefore, the above matrix parameters are defined as:
