Given the significant potential of orthogonal time frequency space (OTFS) signals in advancing integrated sensing and communication (ISAC) systems, this paper investigates the target sensing enhancements in ISAC-OTFS systems. Specifically, a customized inter-range-cell interference (IRCI)-free range reconstruction method is proposed for high-quality target sensing. The resultant mainlobe signal-to-noise ratio (SNR) is derived as a function of OTFS waveform parameters, and the achievable maximum SNR is deduced to provide a benchmark for subsequent simulations. The optimization of OTFS waveforms for maximizing the target sensing SNR is formulated with hardware realization constraints (i.e., peak to average power ratio (PAPR) and energy constraints). The resultant non-convex multi-ratio fractional programming problem is solved using a hybrid algorithm named multi-ratio fractional programming and partial successive convex approximation (MR-FP-PSCA). Finally, the numerical results, including the IRCI-free target sensing method and the proposed optimization algorithm, demonstrate the effectiveness of the developed schemes.

Active sensing, which requires signal transmission, offers high detection performance but suffers from poor stealth capability. In contrast, passive sensing offers strong stealth capability by exploiting non-cooperative illuminators of opportunity (IOs), but its detection performance is weaker due to the unknown IO signals. This paper proposes a target detector for multistatic hybrid active-passive sensing (HAPS) systems based on the generalized likelihood ratio test (GLRT) criterion. The proposed detector aims to combine active and passive sensing strengths to mitigate their respective limitations and enhance overall low-interception detection performance. A test statistic is formulated by integrating both active and passive observations, which is then decomposed into several likelihood functions to reduce computational complexity. Nuisance parameters are estimated within each function and replaced with their maximum likelihood estimates. A low-dimensional GLRT test statistic for HAPS is established by fusing these reduced-dimensional likelihood functions. Simulations show that the proposed detector outperforms purely active or passive detectors, highlighting its superior performance and robustness.

Phased array radar (PAR) is shown to provide an enhanced performance for target tracking due to its beam agility and ability for time resource allocation. Existing algorithms for PAR resource allocation often consider standard and simplified dynamic models for target motion, which are not suitable for complex scenarios such as for ballistic target tracking. To expand the applications of resource allocation algorithms in this context, this paper proposes a robust dwell time allocation algorithm for multiple ballistic reentry target (BRT) tracking in PAR. We aim to achieve an online dwell time allocation by establishing and solving optimization problems at each tracking interval. The motion of BRT is characterized by using the flat earth model, and the predicted Cramér-Rao lower bound (PCRLB) for BRT is derived to quantify the performance of tracking. By minimizing the weighted sum of time consumption of the PAR and cost of attaining the desired performance, a robust dwell time allocation approach is established. Finally, the superiority of the proposed algorithm is demonstrated through simulation.

This paper presents a method for the joint detection and tracking of weak targets in automotive radars using the multi-frame track-before-detect (MF-TBD) procedure. Generally, target tracking in automotive radars is challenging due to radar field of view (FOV) misalignment, nonlinear coordinate conversion, and self-positioning errors of the ego-vehicle, which are caused by platform motion. These issues significantly hinder the implementation of MF-TBD in automotive radars. To address these challenges, a new MF-TBD detection architecture is first proposed. It can adaptively adjust the detection threshold value based on the existence of moving targets within the radar FOV. Since the implementation of MF-TBD necessitates the inclusion of position, velocity, and yaw angle information of the ego-vehicle, each with varying degrees of measurement error, we further propose a multi-frame energy integration strategy for moving-platform radar and accurately derive the target energy integration path functions. The self-positioning errors of the ego-vehicle, which are usually not considered in some previous target tracking approaches, are well addressed. Numerical simulations and experimental results with real radar data demonstrate large detection and tracking gains over standard automotive radar processing in weak target environments.

In this paper, a power optimization (PO) strategy is proposed to combat deception jamming in multi-radar systems (MRSs) performing multi-target tracking (MTT). As a crucial parameter for distinguishing between physical and false targets in MRS under deception jamming, we propose integrating the deception range into the augmented target state to be estimated. The posterior Cramér-Rao lower bound (PCRLB) of the target location parameter is adopted as the tracking performance metric. Using the predicted PCRLB of deception range in the augmented target state, a discriminator is designed to improve the rejection probability of false target. Tracking PCRLBs for physical targets and rejection probabilities for false targets are calculated and used to build the objective function. Considering resource constraints, the anti-deception jamming PO problem is formulated and solved. Numerical results demonstrate the effectiveness of the presented PO strategy for MTT in discriminating false targets.

Polyphase waveform design for the minimization of Integrated Sidelobe Level Ratio (ISLR) is the key technology in Multiple-Input Multiple-Output (MIMO) radar systems. Due to the discrete phase constraint, the problem is non-convex and challenging to solve. Existing methods often rely on experience-based hard quantization with fixed thresholds, leading to limited performance due to the hard quantization. To address this issue, we propose the Optimal Loss Function-Based Soft Quantization (OLF-SQ) method with adjustable quantization thresholds. Firstly, the Complex Circle Manifold (CCM) space is constructed to satisfy the constant modulus constraint, and then the Gradient Descent (GD) model-driven network layer based on the CCM is devised to obtain the continuous waveform. Secondly, the soft quantization function is derived with adjustable quantization thresholds, and then the soft quantization network layer is devised to obtain the discrete waveform, where the quantization thresholds are learned by the unsupervised learning network. Compared with the existing methods, the proposed method has better performance in terms of ISLR and beampattern shaping.

In this letter, we investigate the per-user MUI energy-controllable waveform design problem in Dual-Function Radar-Communication (DFRC) systems. Due to the unimodular constraint and per-user MUI energy constraint, the problem is non-convex and difficult to solve. To address it, the Inequality Constrained Manifold Optimization (ICMO) method is proposed. First, we transform the per-user MUI energy constraint into a penalty function added to the objective function through the exact penalty technique, resulting in a transformed problem containing only the unimodular constraint. Then, we note that the Complex Circle Manifold (CCM) naturally satisfies the unimodular constraint, the problem can be further converted into an unconstrained problem over CCM, and we derive a conjugate gradient descent (CGD) algorithm to solve it. Compared with existing methods, the proposed method exhibits advantages in terms of per-user communication performance, signal-to-interferenceand-noise ratio (SINR), and beampattern performance. Besides, our method has lower computational costs.

This paper investigates integrated waveform design for radar systems to simultaneously achieve a low probability of intercept (LPI) by an adversary electronic support measure (ESM) system while maintaining communications with other radar nodes. A reconfigurable intelligent surface (RIS) is exploited and jointly designed to enhance the achievable performance of the whole system. LPI detection based on the ESM’s feature analysis on the radar waveform is achieved under the constraints of a desired signal-to-interference-and-noise ratio for the radar detection and a desired signal-to-noise ratio-based quality of service for the communication channels. To deal with the resulting non-convex optimization problem, a suboptimal composite algorithm with polynomial complexity is proposed. The initial feasible solution of the algorithm is efficiently obtained through an asymptotic optimization problem. Finally, the complexity of the proposed algorithm is analyzed. Simulation results including comparisons with baselines highlight the effectiveness of the proposed scheme.

This paper proposes a power allocation strategy for multi-target tracking in netted radar system under suppression jamming. The aim is to achieve better tracking accuracy with limited power. The information reduction factor is introduced in the posterior Cramér-Rao lower bound (PCRLB) to indicate the uncertainty of the measurement caused by the jamming signal, which makes the detection probability $(P_{d})$ being less than unity. This bound is used as the tracking performance metric. Then, a non-convex optimization problem regarding power allocation is established by minimizing the worst case tracking PCRLB. The gradient projection algorithm is used to solve the formulated problem. Finally, a closed-loop feedback framework is established by using the timely feedback results of target tracking to guide the subsequent power allocation. The simulation results verify the effectiveness of the performance metrics and the superiority of the proposed power allocation strategy.

An effective resource allocation strategy can maximize the remote sensing performance of radar systems, such as target detection and tracking. In this paper, two typical power allocation (PA) strategies are developed for the multi-target tracking (MTT) task in colocated MIMO (C-MIMO) radar systems with the consideration of the clutter. The multi-beam concept and the posterior PDF fusion are adopted by the C-MIMO radar system to obtain the global posterior distribution. Specifically, each radar generates multiple simultaneous beams with controllable power during each interval. To ensure that the limited system resources can be utilized effectively, the online PA scheme is implemented according to the prior knowledge predicted from the tracking cyclic recursive feedback results. The posterior Cramér-Rao lower bound (PCRLB) is derived by enumerating all possible target detection and false alarm occurrence cases, and is utilized as the tracking performance metric since it provides a more accurate lower bound on the target state estimation in clutter. Besides, to solve the computationally expensive problem of this PCRLB caused by enumeration operation, we propose a two-step approximate approach. Then, combined with the system resource configuration, two different types of resource optimization problems are designed, namely, performance maximization for a fixed power budget and direct resource minimization. These formulated PA problems are shown to be non-convex and non-linear. Therefore, we further propose a modified particle swarm optimization (MPSO) algorithm to solve these problems efficiently. Simulation results verify the superiority and effectiveness of the proposed PA strategies in terms of tracking performance in clutter.

Wireless sensor networks (WSNs) have been demonstrated to enhance parameter estimation performance for target tracking. In this paper, a prior information based quantizer design framework is proposed for target tracking for WSNs. In the proposed framework, the imperfect wireless channels between local sensors and the fusion center are considered. To make full use of the historical states and measurements embedded into the Bayesian tracking methodology, the quantizer design is suggested to be implemented with considering the prior state information. To this end, a channel-aware posterior Cramér-Rao lower bound (PCRLB) is derived based on the state prediction and further used as the performance indicator for quantizer design. Regarding target tracking, we model the quantizer design problem as a non-convex and highly nonlinear optimization problem that is intractable in general. We split the problem in terms of different scenarios, and for one-bit quantizer design based on a binary symmetric channel (BSC), we find that the optimal solution can be analytically computed. While for the general fading channel-based quantizer design problems, we propose two polynomial-time algorithms to find the solutions. Meanwhile, an approximation-based channel-aware particle filter (A-CAPF) is proposed to improve the implementation efficiency of state filtering. Simulation results demonstrate the enhanced performance and execution efficiency of the proposed algorithms in the context of the BSC and Rayleigh fading channel.

Wireless sensor networks (WSNs) are demonstrated to be the increasingly essential systems for various Internet of Things (IoT)-based sensing applications. This article proposes a cost-aware dynamic sensor scheduling (CADSS) framework for WSNs with multiple tasks. At its core, a system cost function is designed to quantify the expenses of the WSNs due to task executions, and a task quality function is modeled to indicate the performance of the corresponding tasks. The proposed CADSS is further formulated as an optimization problem to minimize the system cost while maintaining the desired task qualities. In this way, a comprehensive task utility evaluation methodology for self-organized WSNs is constituted. Furthermore, by modeling the posterior Cramér–Rao lower bound (PCRLB) as the task quality function and a weighted sum of the communication and sensor scheduling cost as the system cost, the CADSS is instantiated into a multitarget tracking (MTT) application. It is shown that the formulated CADSS is a nonconvex optimization problem involving two coupled binary variables that, respectively, correspond to the scheduling of sensor and cluster head. We then propose a parallel convex relation approach to solve it effectively. Numerical results verify the effectiveness of the proposed CADSS by comparing it with state-of-the-art strategies.

Multi-frame detection (MFD) and multi-sensor fusion are two popular methods of target detection and estimation which can improve the performance by increasing the number of measurement samples. In this paper, we combine these two methods together, proposing a novel multi-sensor multi-frame detection (MS-MFD) method. On the one hand, MS-MFD can make use of the target information as much as possible through the multi-frame integration. On the other hand, it can acquire the target space-diversity gain by jointly processing the measurement samples on different observation orientations, providing more accurate estimates. In particular, the proposed method consists of two steps. First, it conducts the MFD processing in each sensor node, computing the local multi-frame jointly posterior probability density. Then, it transmits the local densities to the fusion center for further processing, calculating the global target estimates. Furthermore, in order to improve the implementation efficiency of MS-MFD, a Gaussian Mixture model based method is proposed to approximate the distribution of local posterior probability density, so that the transmission costs of local posterior probability density can be significantly reduced. It is demonstrated by simulations that the proposed methods show superior performance.

In this paper, a new power allocation framework is proposed with the task of multiple target tracking (MTT), in which an adaptive cost function (ACF) with respect to the transmit power and tracking accuracy requirements is first designed. Then we take the ACF as an objective function and formulate the proposed framework as a mathematical optimization problem. In this problem, the posterior Cramér-Rao lower bound (PCRLB) provides us with a lower bound on the estimated error of the targets state. Numerical simulation demonstrates that in the scenario where the common method is not applicable, an effective and robust power allocation scheme can be obtained by the proposed method.

In this paper, we consider an adaptive node and power simultaneous scheduling (ANPSS) strategy for target tracking in distributed multiple radar systems. For all of the available nodes, with full resources allocation, minimizing estimation mean-square error (MSE) may exceed the predetermined system tracking performance goal and cause unnecessary resources consumption. Therefore, tracking performance driven resource allocation schemes for multiple radar systems are proposed. For a predefined estimation MSE threshold, the total transmitted energy is minimized by optimally scheduling node and power resources with the required tracking accuracy. For a given total power budget, the attainable tracking MSE is minimized by optimizing node and power allocation among the transmit radars. The Bayesian Cramer-Rao lower bound (BCRLB) is used as a performance metric. The resulting optimization problems are solved through Zoutendijk method of feasible directions (ZMFD). Numerical results demonstrate that significant resource savings could be obtained through the proposed schemes.

In this paper, we propose an adaptive node selection strategy for target tracking in passive multiple radar systems, with the objective of minimizing the number of nodes in the tracking task. Since the signal parameters are random in passive systems, we first take the expectation over the random parameters, and derive a new Bayesian Cramer-Rao lower bound (BCRLB) as the criterion. Then, we formulate a knapsack-based node selection problem with the required tracking accuracy constraint. This formulation can be solved optimally by an exhaustive search algorithm, but with high computational complexity. For real-time application, we propose an efficient heuristic algorithm to solve it, which offers considerable reduction in computational complexity. Numerical results demonstrate the superior performance of the proposed strategy and the effectiveness of the proposed solution.