In this paper, two optimal resource allocation schemes are developed for asynchronous multiple targets tracking (MTT) in heterogeneous radar network. The key idea of the heterogeneous resource allocation (HRA) schemes is to coordinate the heterogeneous transmit resource (transmit power, dwell time, etc.) of different types of radars for better resource utilization efficiency. We use the Bayesian Cramr-Rao lower bound (BCRLB) as a metric function to quantify the target tracking performance, and build the following two HRA schemes: For given system resource budget, (1) Minimize the total resource consumption for given BCRLB requirements on multiple targets; (2) Maximize the overall MTT accuracy. Instead of updating the state of each target recursively at different measurement arrival times, we combine multiple asynchronous measurements into a single composite measurement, and use it as an input of the tracking filter for state estimation. In such a case, target tracking BCRLB no longer needs to be recursively calculated, and thus we can formulate the HRA schemes as two convex optimization problems. We subsequently design two efficient methods to solve these problems by exploring their unique structures. Simulation results demonstrate that the HRA processes can either provide smaller overall MTT BCRLB for given resource budgets, or require less resource to establish the same tracking performance for multiple targets.

We consider the adaptive target detection problem in colocated MIMO radar where disturbance covariance matrix is unknown, and no training data is available. A tunable detector is proposed, which includes the Rao and Wald tests as special cases. The selectivity or robustness of the proposed detector to mismatched signals can be flexibly tuned by adjusting the tuning parameter. An equivalent form of the proposed detector is obtained to provide a deeper insight into how the selectivity or robustness is adjusted by selecting the tuning parameter. We derive analytical expressions for the probability of false alarm and the detection probability to evaluate its detection performance in both matched and mismatched steering vector cases. It is shown that the proposed detector bears a constant false alarm rate property with respect to the disturbance covariance matrix. Numerical results reveal that the selectivity of the proposed detector can be enhanced by increasing the value of the tuning parameter.

Joint radar-communications dual function (DFRC) has drawn lots of attention since it can make a better use of the scarce wireless frequency resources and expensive hardware platforms. In case of joint radar-communications signal co-use, many communication sequences have poor range sidelobes and thus are not very suitable for the radar function. In this paper, we present a single carrier joint radar-communications method operating in the pulsed radar mode. Digital communication sequences are first partitioned into blocks that are then mapped to digital phase-coded sequences, like Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift Keying (QPSK) sequences. The phases of the digital sequences are perturbed a bit such that certain degrees of freedom are available to optimize for lower range sidelobes. Insignificant phase perturbation will be deemed as phase noise by a communication receiver and then phase codes can be correctly decoded; in radar processing channels, range compression are performed with known and optimized phase perturbation such that low range sidelobes are obtained. An implementation scheme is presented. Numerical results with BPSK and QPSK sequences indicate that little phase perturbation can significantly drop the range sidelobe level but will insignificantly rise the bit error rate.

The problem of detecting a subspace signal in colored Gaussian noise with unknown covariance matrix is investigated by incorporating persymmetric structure of received data. The signal of interest is described with a subspace model, namely, it belongs to a subspace spanned by the columns of a known matrix, but with unknown coordinates. We propose a persymmetric detector with two tunable parameters, which includes many existing persymmetric detectors as special cases. Approximate expressions for the probabilities of false alarm and detection of the proposed detector are derived, which are verified via Monte Carlo simulations. Numerical results reveal that the exploitation of the persymmetric structure leads to a significant gain in the detection performance, especially in the case of limited training data. In addition, a further gain in the detection performance can be obtained by optimally selecting the tunable parameters.