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Range/Doppler migrations, which result from the integration time increasing and the target's manoeuvring motion, will affect the coherent integration performance severely. To deal with range/Doppler migration, a novel coherent integration algorithm, improved axis rotation discrete chirp-Fourier transform (IAR-DCFT), is proposed. IAR-DCFT could eliminate range migration via improved axis rotation transform, and realise the compensation of Doppler migration and coherent integration via discrete chirp-Fourier transform. IAR-DCFT may be regarded as tri-dimensional motion parameter filter banks, which is analogous to moving target detection that can be treated as Doppler filter banks, and estimate a target's velocity, acceleration and jerk simultaneously. Then the derivations of maximumlikelihood estimator and likelihood ratio test detector show that IAR-DCFT is the optimal estimator and a detector. The performance of the optimal estimator is verified by comparing with Cramer-Rao lower bound. Subsequently, the detailed performance analyses of IAR-DCFT are provided, including coherent integration gain, coherent integration time, multi-target detection and computational complexity. Furthermore, the authors introduce the generalisation of IAR-DCFT, that is, multi-range-cell associated IAR-DCFT (MR-IAR-DCFT), which can be applied to detect a target with high-manoeuvring motion or used in a longer time integration case. Finally, some numerical experiments are given to verify the performance of IAR-DCFT and MR-IAR-DCFT.

This paper presents a novel algorithm for the localization of mixed far-field sources (FFSs) and near-field sources (NFSs) without estimating the source number. Firstly, the algorithm decouples the direction-of-arrival (DOA) estimation from the range estimation by exploiting fourth-order spatial-temporal cumulants of the observed data. Based on the joint diagonalization structure of multiple spatial-temporal cumulant matrices, a new one-dimensional (1-D) spatial spectrum function is derived to generate the DOA estimates of both FFSs and NFSs. Then, the FFSs and NFSs are identified and the range parameters of NFSs are determined via beamforming technique. Compared with traditional mixed sources localization algorithms, the proposed algorithm avoids the performance deterioration induced by erroneous source number estimation. Furthermore, it has a higher resolution capability and improves the estimation accuracy. Computer simulations are implemented to verify the effectiveness of the proposed algorithm.

The cubic phase function (CPF) based estimator is efficient in estimating the parameters for mono-component linear frequency-modulated (LFM) signals. However, it suffers from cross-terms and spurious peaks when dealing with multi-component LFM signals. Aimed at this identifiability problem, a coherently integrated CPF (CICPF) algorithm is proposed to enhance the auto-terms and suppress spurious peaks. Comparisons with several existing algorithms are made, which show that the CICPF not only solve the identifiability problem for multi-component LFM signals, but also acquires high anti-noise performance.Introduction: Linear frequency-modulated (LFM) signals have many applications in radar, sonar, communication etc. Detection and parameter estimation of LFM signals have received considerable attention. For multi-component LFM signals, a number of algorithms were proposed, such as the combined radon-Wigner distribution [1], radon-ambiguity function [2] and the fractional Fourier transform [3] etc. Recently, an instantaneous frequency rate (IFR) estimator using the cubic phase function (CPF) was proposed for LFM signal estimation [4][5][6][7]. However, for a multi-component signal, the CPF based algorithm suffers from spurious peaks and cross-terms. This performance of the CPF becomes even worse in the scenario of dense LFM signals. For K-component LFM, there are K 2 − K cross-terms and (K 2 − K )/2 spurious peaks, respectively. According to the theoretical analysis in [6], the spurious peaks may appear within the observation time with probability 1 − (1/2) (K2 − K )/2 , provided that the phase parameters are independently and uniformly distributed. To solve this problem, the product CPF (PCPF) and the integrated CPF (ICPF) are proposed in [6,7], respectively. However, these two algorithms cannot acquire high anti-noise performance. In this Letter, we propose a coherently integrated CPF (CICPF) algorithm for the detection and parameter estimation of multicomponent LFM signals. This algorithm exploits the property that the auto-terms in the CPF plane are concentrated along straight lines parallel to the time axis. Therefore, the energy of auto-terms can be enhanced by integration along these straight lines, with the cross-terms and the spurious peaks suppressed. However, the anti-noise performance is improved at the same time.

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