This paper proposes a novel digital adaptive blind background calibration technique for the gain, timing skew, and offset mismatch errors in a time-interleaved analog-to-digital converter (TI-ADC). Based on the frequency-shifted basis functions generated only from the measured TI-ADC output, the three mismatch errors can be represented, extracted, and then subtracted from the TI-ADC output adaptively, which is quite different from the conventional methods (e.g., using a bank of adaptive FIR filters). The advantage of the proposed technique is that it only needs to know the measured output signal and the TI-ADC channel number, without needing any additional information; and it is applicable to any TI-ADCs with no limitations to the channel number, sub-ADC sampling rate, signal type, and so on. Specifically, the frequency-shifted signal generator employs the Hilbert transform and does not need extra finite impulse response filters. Extensive simulation and measurement results have been presented to demonstrate the effectiveness and superiority of the proposed technique, for single-tone, multi-tone, wideband modulated, and multi-band modulated signals. In the measurement for a 32GS/s 16-channel TI-ADC system, it shows that the proposed technique can improve the effective number of bit by 2 ~ 3 bit and the SFDR by 30 ~ 35 dB.

This paper proposes a novel multi-objective ant colony system (MOACS) approach to solve the cooperative task allocation problem of multi-robot systems. The task allocation problem is formulated as a multi-objective multiple traveling salesman problem (MTSP). The objectives are to minimize the total and maximum cost of the robotic vehicles so that the workload of each vehicle could be balanced. The time cost matrices of the salesmen are different and asymmetric due to the different flight speeds of vehicles and executing time of tasks. Based on the single-objective ant colony system (ACS), a novel solution construction method and a novel pheromone update rule are proposed. At each step in the solution construction phase, the ant with minimum cost has the biggest chance to add an unassigned task to balance the workload of each vehicle, while the ant with maximum cost also has a bigger chance than any other ants to add an unassigned task to find better Pareto front. The minimum value of the pheromone is limited in the pheromone update phase, which is helpful in avoiding fast convergence and local optima. Extensive simulation results suggest that the proposed MOACS has better performance and effectiveness than the existing non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO). Hardware-in-the-loop experiments on multiple unmanned aerial vehicles (UAVs) also show that compared with NSGA-II and MOPSO, the maximum and total flight distance of the UAVs with the proposed MOACS are decreased by up to 28.46% and 26.34%, respectively, while the maximum and total time used to finish all tasks are decreased by up to 23.86% and 17.94%.

This letter proposes a novel consensus-based timetable algorithm (CBTA) to solve the decentralized simultaneous multi-agent task allocation problem. Due to the limited capability of each agent, multiple agents may be required to perform a task simultaneously. A key challenge is how to meet the requirements and minimize the average start time of all tasks. The proposed CBTA aims to minimize the start time of each task to minimize the average start time of all tasks indirectly, it iterates between a timetable construction phase and a consensus phase. New tasks are included in the timetable of each agent by comparing the estimated start time of tasks placed by its own and other agents during the timetable construction phase. Then in the consensus phase, agents share their timetables with a communication network, and conflicts among their timetables are eliminated according to a consensus rule. Extensive simulation results show that the average start time of tasks of the proposed CBTA is nearly the same as the consensus-based bundle algorithm (CBBA) when performing single-agent tasks, and it is much less than the consensus-based grouping algorithm (CBGA) when performing multi-agent tasks with various communication network topologies.

This paper addresses the distributed task allocation problem for maximizing the total number of successfully executed tasks of multirobot systems. Due to the deadline time of tasks and fuel limits of robotic vehicles, not all tasks can be successfully executed sometimes. Based on the performance impact (PI) algorithm, an effective and efficient performance impact (EEPI) algorithm is proposed, its novelty lies in its cost function and task release procedure. The fundamental ideas of the proposed cost function are as follows. First, the traveling time from the initial position of each vehicle to the positions of its tasks is minimized, so that more time can be left for the vehicle to execute more tasks due to the limited fuel. Second, the start time of each task should be close enough to its deadline, so that tasks with earlier deadlines can be assigned earlier than those with later deadlines. To avoid invalid removal performance impacts (RPIs) and inclusion performance impacts (IPIs), the tasks assigned to a vehicle are all released if the number of tasks removed by the vehicle during the task removal phase is the most, which further increases the total number of successfully executed tasks. Both simulations and hardware-in-the-loop experiments suggest that compared with the state-of-the-art distributed task allocation algorithms, the proposed EEPI is not only effective in maximizing the number of successfully executed tasks but efficient in saving the number of iterations and time to converge. Note to Practitioners —This work was motivated by the limitations of the existing distributed task allocation algorithms for maximizing the total number of successfully executed tasks. The consensus-based bundle algorithm (CBBA) has been proven to guarantee convergence and 50% optimality under the diminishing marginal gain (DMG) assumption in previously published works. Based on CBBA, a performance impact (PI) algorithm was proposed, and simulations show that it can assign more tasks than CBBA when applied to time-critical scenarios with low task-to-vehicle ratios. Starting from the results of PI, a rescheduling method named PI for maximizing assignments (PI-maxAss) was proposed, which has been demonstrated to assign more tasks than PI with high task-to-vehicle ratios. However, much more iterations and time are required by PI-maxAss to converge to globally consistent assignments because of the rescheduling. Due to the above considerations, an effective and efficient performance impact (EEPI) algorithm is proposed in this paper to maximize the number of successfully executed tasks without any rescheduling. Both simulations and hardware-in-the-loop experiments suggest that compared with the algorithms mentioned above, the proposed EEPI is effective in maximizing the number of successfully executed tasks and efficient in saving the number of iterations and time to converge. In future work, the distributed task allocation problem in which several vehicles execute a task at the same time cooperatively or a vehicle executes several tasks simultaneously will be further addressed.

High frequency (HF) transmitter implemented with the multioctave broadband power amplifier (PA) enables long-distance communication and has strong survivability. However, it is faced with the problem of harmonics because of the nonlinearity of the PA. We first consider canceling the harmonics using a harmonic cancellation digital predistortion (HC-DPD) technique, instead of lossy and bulky analog filter banks. A uniform complex-valued Hilbert transform-based polynomial (HTBP) function is proposed for the modeling of the harmonics, as well as the fundamental component in the real-valued input and output signals. In order to validate this idea, detailed experiments were performed for carrier frequencies located at 5 and 10 MHz, respectively. After the proposed HC-DPD, the harmonics were all canceled to satisfy the linearization requirements. In particular, the improvement of the second harmonic is more than 40 dB, which is nearly 20 dB better than that of the memoryless polynomial (MLP) model. Furthermore, the experimental results demonstrate that the proposed technique is scalable, in which the model coefficients extracted at a specific power level, signal bandwidth, and carrier frequency can be extended to other scenarios. Both the second and third harmonics were canceled for being below -45 dBc after the HC-DPD.

This paper proposes a novel adaptive architecture for blind estimation and calibration of the frequency response mismatches in an M-channel time-interleaved analog-to-digital converter (TI-ADC). Based on the basis functions generated from the measured TI-ADC output, the mismatch errors could be reconstructed blindly and subtracted from the TI-ADC output adaptively. The advantage of the proposed technique is that it only needs to know the measured output signal without requiring any additional information and it is applicable to any channel TI-ADCs. Specially, the calibration structure only needs several differentiators with fixed coefficients without requiring a bank of adaptive FIR filters. In addition, the numerical simulations have been presented to demonstrate the effectiveness and superiority of the proposed technique, which shows that the proposed technique can improve the SNR and SFDR by over 25 dB.