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In multi-radar air surveillance scenario bias in individual radar measurements is an important prerequisite for effective fusion of radar data. Conventional bias computation approaches depend on GPS fitted air sorties for computation of bias in radar measurements. The method proposed in this paper computes radar bias automatically using GPS data available from Automatic Dependent Surveillance Broadcast (ADS-B) measurements from targets of opportunity. The proposed method computes the bias in radar measurements without the need of costly dedicated calibration sorties or multiple radars and thereby reducing the cost and manual effort. This paper provides details about the mathematical frame works of recursive weighted least square based bias estimation algorithm and the associated data conversion formats in different coordinate systems. The proposed method preprocess the correlated radar and AD-B measurements and selects measurement pairs from linear segments for time alignment. This paper provides a detailed scheme of automatic bias estimation process with different parameters involved in controlling the accuracy and periodicity of the bias to be computed. The scheme developed in this paper also includes a bias validation module and a unique averaging method to ensure accuracy and smooth variations across the measurement batches considered for computation. The simulation results obtained in this paper suggest the utility of the proposed approach for practical bias computation applications in a cost effective way. The method developed in this paper is based on insight obtained from analysis of recorded data from field deployed radars.INDEX TERMS Radar bias, ADS-B, bias validation.

Effectiveness of tracking closely moving targets depends on the capability to resolve the ambiguity in associating measurements-to-tracks. Joint probabilistic data association (JPDA) has been shown to be very effective in tracking closely moving objects, but the approach is susceptible to track coalescence. The factor graph (FG) based association scheme developed in this paper circumvents the track coalescence by avoiding multiple hypothesis equivalence with recursive updation of likelihood values. The improvement in association using factor graph based data association scheme over JPDA has been demonstrated using a simulated scenario of closely moving targets. The steady state likelihood values obtained at the end of recursive process are shown to match the likelihoods obtained from measurements.

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