1. Nearest Neighbor Data Association (NNDA)

function [assignments] = nearest_neighbor(observations, tracks, gate_threshold)
    % Input: observations - measurement matrix (N×4)
    %        tracks - active track matrix (M×4)
    %        gate_threshold - gating distance threshold
    % Output: assignment matrix (M×N)
    
    num_obs = size(observations,1);
    num_tracks = size(tracks,1);
    cost = zeros(num_tracks,num_obs);
    
    for t = 1:num_tracks
        for m = 1:num_obs
            % Calculate Mahalanobis distance
            residual = observations(m,:) - tracks(t,1:3);
            covariance = tracks(t,4:6);
            cost(t,m) = sqrt(residual * covariance * residual');
        end
    end
    
    % Association decision logic
    assign_matrix = zeros(num_tracks,num_obs);
    for t = 1:num_tracks
        [~,best_idx] = min(cost(t,:));
        if cost(t,best_idx) < gate_threshold
            assign_matrix(t,best_idx) = 1;
        end
    end
end

2. Probabilistic Data Association (PDA)

function [assignments] = prob_association(observations, tracks, noise_density)
    % Input parameters:
    % noise_density - clutter density (per sq km)
    % additional parameters as defined above
    
    [num_tracks,num_obs] = size(observations);
    cost = zeros(num_tracks,num_obs);
    candidate_list = [];
    
    % Compute validation gate
    for t = 1:num_tracks
        for m = 1:num_obs
            if chi2inv(0.95,3) > mahal_distance(tracks(t,:),observations(m,:))
                candidate_list = [candidate_list; m];
            end
        end
    end
    
    % Calculate association probabilities
    num_candidates = length(candidate_list);
    assoc_prob = 1 / (num_candidates + noise_density);
    
    for t = 1:num_tracks
        for m = 1:num_obs
            if ismember(m,candidate_list)
                cost(t,m) = assoc_prob * mahal_distance(tracks(t,:),observations(m,:));
            else
                cost(t,m) = noise_density * 1e-6;
            end
        end
    end
    
    % Solve optimal assignment
    assignments = munkres(-cost);
end

3. Joint Probabilistic Data Association (JPDA)

function [assignments] = joint_prob_association(observations, tracks, gate_param)
    % Joint probability computation
    [num_tracks,num_obs] = size(observations);
    track_count = num_tracks;
    meas_count = num_obs;
    
    % Build association matrix
    assoc_matrix = zeros(track_count,meas_count);
    for t = 1:track_count
        for m = 1:meas_count
            separation = norm(observations(m,:) - tracks(t,1:3));
            if separation < gate_param
                assoc_matrix(t,m) = calc_joint_prob(tracks(t,:),observations(m,:));
            end
        end
    end
    
    % Find maximum probability assignment
    assignments = munkres(-assoc_matrix);
end

function p = calc_joint_prob(track_state, measurement)
    % Joint probability density calculation
    cov_matrix = track_state(4:6);
    residual = measurement - track_state(1:3);
    p = exp(-0.5*residual/cov_matrix*residual') / sqrt((2*pi)^3*det(cov_matrix));
end

4. Intreactive Multiple Model (IMM)

function [updated_tracks] = imm_filter(tracks, measurements, model_set)
    % Input:
    % model_set - collection of motion models [cv, ct](@ref)
    % Output: updated track states
    
    num_tracks = size(tracks,1);
    num_models = size(model_set,1);
    
    % Model probability update loop
    for i = 1:num_tracks
        for j = 1:num_models
            % Model prediction step
            [tracks(i).state, tracks(i).covariance] = ...
                kalman_predict(model_set(j), tracks(i).state, tracks(i).covariance);
            
            % Compute association probability
            tracks(i).model_weight(j) = jpda_associate(tracks(i).state, measurements, 50);
        end
        
        % Normalize probabilities
        tracks(i).model_weight = tracks(i).model_weight / sum(tracks(i).model_weight);
        
        % State fusion
        tracks(i).state = weighted_blend(tracks(i).state, tracks(i).model_weight, model_set);
    end
end

Complete Simulation Framework

%% System Configuration
num_targets = 3;
scan_count = 100;
time_step = 0.1;
detection_range = 1000;

%% Generate Test Scenario
[ground_truth, sensor_data] = create_scenario(num_targets, scan_count, time_step);

%% Initialize Tracking System
active_tracks = init_track_set(ground_truth);

%% Main Processing Loop
for k = 1:scan_count
    % Data association step
    link_matrix = joint_prob_association(active_tracks, sensor_data{k}, 50);
    
    % Model adaptation (IMM)
    for i = 1:length(active_tracks)
        active_tracks(i) = imm_filter(active_tracks(i), sensor_data{k}, ...
            {constant_velocity_model, coordinated_turn_model});
    end
    
    % Display results
    render_tracking_results(active_tracks, sensor_data{k});
end

%% Helper Functions
function [ground_truth, sensor_data] = create_scenario(N, M, dt)
    % Generate ground truth trajectories and measurements
    ground_truth = struct('position',{rand(N,3)*1000}, ...
                          'velocity',{rand(N,3)*20});
    sensor_data = cell(M,1);
    
    for k = 1:M
        for i = 1:N
            % Inject measurement noise
            z = ground_truth(i).position + mvnrnd(zeros(1,3), diag([50,50,25]))';
            sensor_data{k} = [sensor_data{k}; z];
        end
    end
end

Parameter Tuning Reference

Extended Implementation Ideas

1. Multi-Sensor Integration```

   % Combine radar and visual tracking data combined_output = fuse_sensor_inputs(radar_readings, camera_readings);

2. Anti-Spoofing Measures

   • Incorporate anomaly detection module
   • Leverage deep neural networks for measurement validation

3. 3D Extension```

   % Handle altitude dimension tracks(:,4) = rand(elevation_range);