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Multifrequency continuous-wave-continuous-wave (MFCW-CW) radars simultaneously transmit multifrequency continuous-wave (MFCW) signals sequentially at a constant time step and a continuous-wave (CW) signal constantly. As MFCW signals are transmitted sequentially, only one phase difference exists at each time. All phase differences between the MFCW and CW signals should be exploited to resolve ambiguous ranges. In this article, we propose a method to compensate the phase difference between the sequential MFCW signals and CW signal using the Doppler frequency of the CW signal. To verify the phase difference compensation result, we estimated the range of multiple targets. The simulation results showed that the proposed phase difference compensation method performed well regardless of compensation direction and target motion. Moreover, we proposed applying the maximum likelihood estimation (MLE) and reduced-complexity off-grid sparse Bayesian learning (RC-OGSBL) to estimate ranges and analyzed their performance. The range accuracy of the MLE using a 0.1-m grid search showed superior performance and the running time of the MLE using the simple method is the fastest among the simulated methods. The RC-OGSBL using a 5-m grid showed good results in terms of range accuracy and running time.

JPDAF is a method of updating target's state estimation by using posterior probability that measurements are originated from existing target in multi-target tracking. In this paper, we propose a multi-target tracking algorithm for falling cluster bombs separated from a mother bomb based on JPDAS method which is obtained by applying fixed-interval smoothing technique to JPDAF. The performance of JPDAF and JPDAS multi-target tracking algorithm is compared by observing the average of the difference between targets' state estimations obtained from 100 independent executions of two algorithms and targets' true states. Based on this, results of simulations for a radar tracking problem that show proposed JPDAS has better tracking performance than JPDAF is presented.

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