The advancement of Integrated Sensing and Communication (ISAC) has facilitated the adoption of target tracking technologies, yet most existing research focuses on waveform design rather than specific tracking algorithms within ISAC systems. To address this gap, this paper proposes an effective target tracking method based on Kalman filter (KF) algorithms within the ISAC framework. The method filters noisy radar measurements, such as bistatic range (BR), bistatic range rate (BRR), and direction of arrival (DOA), to enhance tracking accuracy. Performance is assessed by using trajectory maps and root-meansquare error (RMSE) curves, demonstrating that the proposed method significantly improves estimation accuracy while increasing robustness to measurement noise, especially in DOA. Compared to our previously proposed geometric target localization method, this approach delivers superior tracking performance, making it highly suitable for real-time applications in ISAC systems.

Accurate and real-time tracking of multiple moving targets remains a fundamental challenge in radar-based indoor sensing, particularly when the number of targets is unknown or varies over time. This paper presents a real-time motion tracking system RTCtrack that operates directly on signal variations induced by human movement. At each time step, the method extracts significant variation points relative to a static baseline and incrementally groups them into coherent trajectories without requiring prior knowledge of the number of targets. A tail-based spatial association strategy enables robust path formation, while weak or inconsistent clusters are automatically removed based on activity level. To maintain tracking stability in evolving environments, an adaptive baseline update mechanism replaces the reference signal when persistent global deviations are detected. RTCtrack is evaluated using a public indoor radar dataset involving multiple individuals moving independently. Simulation results show that the system reliably identifies and tracks multiple motion paths in real time without knowing the exact number of targets. Comparisons with video-based ground truth confirm strong spatial and temporal alignment, demonstrating its effectiveness for radar sensing and passive human motion tracking.

This paper studies time difference of arrival (TDoA)based algorithms for drone controller localization and analyzes TDoA estimation in multipath channels. Building on TDoA estimation, we propose two algorithms to enhance localization accuracy in multipath environments: the Maximum Likelihood (ML) algorithm, and the Least Squares Bancroft with GaussNewton (LS-BF-GN) algorithm. We evaluate these proposed algorithms in two typical outdoor channels: Wireless Local Area Network (WLAN) Channel F and the two-ray ground reflection (TRGR) channel. Our simulation results demonstrate that the ML and LS-BF-GN algorithms significantly outperform the LSBF algorithm in multipath channels. To further enhance localization accuracy, we propose averaging multiple tentative location estimations. Additionally, we evaluate the impact of time synchronization errors among sensors on localization performance through simulation.

Antenna position errors caused by manufacturing accuracy limits lead to a great difference between the real and ideal models of antenna arrays in extremely high frequency (EHF) band MIMO radar with tiny physical elements and spacings. As the real array is a non-uniform array, the conventional window function fails when applied to non-uniform sampling data, which causes angular ambiguities and seriously degrades the performance of DOA estimation. Based on an advanced analysis of spectral distortion in non-uniform sampling, this paper introduces a method to design a specialized window function for non-uniform MIMO arrays, aiming to minimize the equivalent noise bandwidth (ENBW) while sufficiently suppressing the side-lobe-to-peak ratio of the window function's spectrum. The simulation results show the effectiveness of the optimized window function, which reduces angular ambiguities and thereby improves the performance of DOA estimation for non-uniform MIMO arrays.

The fusion framework based on Light Detection and Ranging (LiDAR) and vision is susceptible to environmental constraints. Therefore, they are unable to adapt to complex and challenging environments. In response to this, this paper proposes a tightly coupled framework based on Global Navigation Satellite System (GNSS) data, which tightly couples GNSS, inertial, LiDAR and visual data. The experimental results demonstrate that, using publicly available challenging environment datasets, the proposed fusion system can achieve a 52.68% improvement in positioning accuracy compared to some existing fusion algorithms.

Spatiotemporal calibration is an essential problem in the fusion system with heterogeneous multi-source information. Therefore, a universal spatiotemporal calibration algorithm for heterogeneous information is much needed. This paper proposes a universal spatiotemporal calibration technique with the inertial sensor as the central coordinate system. Firstly, it employs a high-order spline interpolation method to transform the output data of the inertial sensor into a continuous form. Subsequently, the calibration model is established by combining the output data from other sensors. The paper provides detailed descriptions of the spatiotemporal calibration models for LiDAR (Light Detection and Ranging) data, and visual data, respectively. In the simulations, the proposed calibration models are applied to existing open-source programs using publicly available datasets. The results demonstrate that, with the adoption of the proposed calibration algorithm, the position estimation accuracy of the fusion system can be improved by 39%.

This paper proposes a novel geometric solution for tracking a moving target through multistatic sensing. In contrast to existing two-step weighted least square (2SWLS) methods which use the bistatic range (BR) and bistatic range rate (BRR) measurements, the proposed method incorporates an additional direction of arrival (DOA) measurement of the target obtained from a communication receiver in an integrated sensing and communication (I SAC) system. Unlike the existing 2SWLS methods that require at least three transmitter-receiver (TX-RX) pairs to operate, the proposed algorithm can conduct location estimation with a single TX-RX pair and velocity estimation with two TX-RX pairs. Simulations reveal that the proposed method exhibits superior performance compared to existing 2SWLS methods, particularly when dealing with moderate levels of noise in DOA measurements.

We propose a bistatic joint radar and communication (JRC) based on standardized 5G new radio (NR) waveform, where the receiver can decode the transmitted signal, and estimate the range, speed, and angle of the radar targets at the same time. The proposed solution consists of several key components, including a) a decision feedback architecture and two JRC algorithms for bistatic radar sensing; b) a multi-user bistatic JRC solution; and c) a direction of arrival (DOA) estimation algorithm based on the range-Doppler map (RDM). We present simulation results to demonstrate the effectiveness of our proposed solution, and its ability to work well in the multi-user scenarios where multiple users share the same spectrum, making it a promising solution for practical applications that require radar sensing in crowded environments.

For massive machine type communications (mMTC), it is critical to squeeze the transmission overhead as packet length is usually short and power is limited. Reducing preamble/pilot length for channel estimation is thus very important. In this paper, we propose to use short preamble for channel estimation in generalized frequency division multiplexing (GFDM) communication. Based on the short preamble, we can estimate a short channel first. We then show that the conventional zero-padding method for extending short channel to long channel in GFDM is not accurate. A new efficient method to construct the long effective channel from the obtained short channel is proposed. The proposed new method can construct a long channel for GFDM equalization without knowing the time sync error. It is proved theoretically that the constructed channel is correct given that the length of cyclic prefix (CP) and cyclic suffix (CS) are longer than the original channel length plus the time sync error. Simulations at various situations are shown to verify the results.

In this paper, we present the design, analytical and simulation results for a wireless Medium Access Control (MAC) protocol, referred to as the “Transmission Gating Time Hyperchannel” (TGT-HC) to support bounded low latency delivery of cyclic control traffic such as generated by close-loop distributed control systems. TGT-HC is based on the contention-free Carrier-Sense Multiple Access (CSMA) Hyperchannel protocol. To enhance its performance, we use a combination of per-flow Time-Aware Shaper (TAS) and First-In-First-Out (FIFO) scheduling to determine its sensing slot assignments. Based on the results, we find that this enhanced protocol comes close to the ideal delay performance of a First-Come- First-Serve (FCFS) single server in handling cyclic traffic.

In this paper, we propose a novel scheme called subband random sensing (SRS) grant free uplink for ultra-reliable low-latency communication (URLLC) in unlicensed spectrum. The SRS grant free uplink creatively combines the subband sensing, user grouping and random access, which allows a user sensing the unlicensed spectrum to use available sub-resources in a wideband and reduce collisions with other users. The new scheme overcomes the severe spectrum wastage problem of the semi-persistent scheduling (SPS) uplink adopted by new radio (NR) release 16 in applications with sporadic traffic. Compared with the contention based grant free uplink, the new scheme achieves much lower collision probability, which directly leads to higher reliability. Analysis and simulations are provided to prove the exceptional performances.

First, we directly introduce the cosine transform to compute the time-domain electromagnetic (EM) response excited by cause step turnoff. Then, we advance a novel semianalytic algorithm based on integration, summation, and cube spline interpolation (ISCSI) method to efficiently accomplish the cosine transform. The ISCSI method divides the integral into a sum of partial integrals, which can be solved by cube spline interpolation combined with Newton-Leibniz formula. On the basis, we apply a known convolution method to further compute the time-domain response by arbitrarily waveform in airborne transient EM measurement. Finally, numerical tests valid the ISCSI method is easier to implement and more efficient than the traditional digital filter method because the ISCSI method only requires less frequency sampling amount to accomplish the cosine transform.

Context and location services become increasingly important in many Internet of Things (IoT) applications such as smart home, disaster assistance, security and safety in smart factory and so on. Traditionally, these services are provided by independent systems. In this paper, we propose a software defined radio (SDR) based multi-function radar for respiratory rate and moving speed detections as well as ranging, which can be used to provide human body detection and tracking in human-centric IoT applications. Since SDR concept is adopted, the same hardware platform could be shared with wireless communications periodically by re-defining. The system architecture design is introduced. The detection algorithms are presented. The ranging performance is compared with some typical ones using simulations. The detections for respiratory rate, ranging and moving speed are carried out on the SDR platform consisting of FMCOMMS5 and ZC706. The system occupies 2MHz bandwidth. It is shown that the proposed multi-function radar achieves the estimation of the human respiratory rate with <;5% errors compared to a clinic-grade device. The ranging resolution achieved is <;0.3 m. The tiny movement as slow as 0.19 m/s can be detected.

With the increasing usage of software defined radio and digital signal processing, the hardware and RF frontend for radar and wireless communication tends to be similar. Thus, using the same RF and hardware platform for joint radar-communication becomes viable. Joint radar-communication would bring more efficient plan and usage for the radio spectral resource. Furthermore, it could enable new applications which require information exchange and precise localization at the same time. In this paper, efficient algorithms are proposed to use standardized waveform in wireless communication (IEEE 802.11ad, IEEE 802.11p, etc) for range and speed detection of targets. Novel approach is found to cancel the self-interference due to full duplexing operation. Detection performances are verified by simulations.

In this paper, a TV white space (TVWS) network in which there is a centralized spectrum manager (SM) is considered. The SM is connected to the geo-location database (GLDB) to periodically obtain the available spectrum fragments, and is responsible for assigning the available spectrum to the white space devices (WSDs) in its coverage region. The spectrum available is usually fragmented with different bandwidths, and the bandwidth required by the WSDs can also be diverse. Unlike existing works which assume complete knowledge of all WSDs' bandwidth requirements before assignment, we consider the more practical settings that WSDs request bandwidth in a sequential manner. Upon the arrival of a bandwidth request, the SM has to determine which fragment the request should be assigned to, without a prior knowledge of the bandwidths of the future requests. We are interested in designing the spectrum assignment policy at the SM upon each arrival of bandwidth request. The above problem is formulated as a stochastic sequential decision-making problem. The optimal spectrum assignment policy to maximize the overall spectrum utilization of the TVWS network is computed through the value iteration method. We demonstrate the performance advantage of our proposed optimal spectrum assignment policy over two heuristic policies through numerical results. To the best knowledge of the authors, this is the first paper that addresses the modeling and design considering the sequential behaviors of WSDs for GLDB-based TVWS network.

The majority of this paper is to develop a reliable and high-performance routing protocol for cognitive radio networks (CRN). Due to the presence of the high-priority users (primary users) in the CRN, an existing routing path may easily become unavailable for any link on the path being occupied by primary users (PUs). The most intuitive way to avoid this problem is to select the path in which fewer PU activities are observed at the cost of performance sacrifice. This paper proposes a novel CRN routing protocol which aims to maintain the performance by utilizing the high-quality link as much as possible and to alleviate the route repair cost due to the occurrence of PUs. The experimental results show that in most situations, the proposed protocol outperforms other routing protocols including one traditional and two CR routing protocols in terms of packet round-trip delay, total system throughput and packet loss rate. The results also show that the proposed protocol is very resistant to the variation of PU activities.

A modified simulated-annealing method is proposed for recursively minimizing the nonlinear cost function in the subspace-based code-timing estimation for direct-sequence code-division multiple-access signals over a multipath fading channel. The new algorithm has low complexity, because of the recursive updating of the final code-timing estimates together with the noise-subspace tracking

In previous years, the MUSIC timing estimator were proposed for direct-sequence code-division multiple access (DS-CDMA) signals. It has been generally assumed that the channel noise is temporally white. However it may be invalid in practice due to, for instance, the presence of some narrowband interference or the lumping of secondary users into with the noise. We consider blind code timing in unknown colored noise by using the matrix decomposition technique. A subspace-based code timing estimation algorithm is presented and its performance is evaluated by computer simulation and compared with the MUSIC method. It is proved that the proposed timing estimation algorithm provides better performance than the MUSIC method in colored noise.

We consider the propagation delay estimation of a direct-sequence code-division multiple-access (DS-CDMA) system in unknown correlated noise. The new propagation delay estimation method proposed here is based on generalized correlation decomposition (GCD). First, the received signal with a dual-antenna through a flat-fading channel is modeled. Then, two GCD-based propagation delay estimators: dual-antenna singular value decomposition (SVD) and dual-antenna canonical correlation decomposition (CCD) estimators are presented and compared with common MUSIC estimator. Simulation results show that the proposed estimators, especially the dual-antenna CCD estimator, are robust against the near-far problem and outperform the common MUSIC estimator in unknown correlated noise.